var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"owner\":\"Ghassemi\",\"authorFirst\":\"1\",\"filter\":\"keywords:SBO\",\"theme\":\"side\",\"host\":\"bibbase.org\"},\n      data: [{\"title\":\"Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings\",\"type\":\"inproceedings\",\"year\":\"2017\",\"pages\":\"V02BT03A026\",\"websites\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375\",\"month\":\"8\",\"publisher\":\"American Society of Mechanical Engineers\",\"day\":\"6\",\"city\":\"Cleveland, Ohio, USA\",\"id\":\"b74d514d-3038-3197-aa98-1cad32734741\",\"created\":\"2019-07-08T20:51:37.509Z\",\"accessed\":\"2019-07-08\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:06.003Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2017optimal\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Zhu, Kaige and Chowdhury, Souma\",\"doi\":\"10.1115/DETC2017-68350\",\"booktitle\":\"Volume 2B: 43rd Design Automation Conference\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings},\\n type = {inproceedings},\\n year = {2017},\\n pages = {V02BT03A026},\\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375},\\n month = {8},\\n publisher = {American Society of Mechanical Engineers},\\n day = {6},\\n city = {Cleveland, Ohio, USA},\\n id = {b74d514d-3038-3197-aa98-1cad32734741},\\n created = {2019-07-08T20:51:37.509Z},\\n accessed = {2019-07-08},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:06.003Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2017optimal},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Zhu, Kaige and Chowdhury, Souma},\\n doi = {10.1115/DETC2017-68350},\\n booktitle = {Volume 2B: 43rd Design Automation Conference},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Zhu,  K.\",\"Chowdhury,  S.\"],\"urls\":{\"Website\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency\",\"type\":\"inproceedings\",\"year\":\"2018\",\"websites\":\"https://arc.aiaa.org/doi/10.2514/6.2018-3107\",\"month\":\"6\",\"publisher\":\"American Institute of Aeronautics and Astronautics\",\"day\":\"25\",\"city\":\"Atlanta, GA, USA\",\"id\":\"cc163f9a-922a-36bd-b7d4-712619fd852f\",\"created\":\"2019-07-08T20:54:02.941Z\",\"accessed\":\"2019-07-08\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:05.767Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"lulekar2018riblet\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.\",\"bibtype\":\"inproceedings\",\"author\":\"Lulekar, Sumeet and Ghassemi, Payam and Chowdhury, Souma\",\"doi\":\"10.2514/6.2018-3107\",\"booktitle\":\"2018 Multidisciplinary Analysis and Optimization Conference\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency},\\n type = {inproceedings},\\n year = {2018},\\n websites = {https://arc.aiaa.org/doi/10.2514/6.2018-3107},\\n month = {6},\\n publisher = {American Institute of Aeronautics and Astronautics},\\n day = {25},\\n city = {Atlanta, GA, USA},\\n id = {cc163f9a-922a-36bd-b7d4-712619fd852f},\\n created = {2019-07-08T20:54:02.941Z},\\n accessed = {2019-07-08},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.767Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {lulekar2018riblet},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.},\\n bibtype = {inproceedings},\\n author = {Lulekar, Sumeet and Ghassemi, Payam and Chowdhury, Souma},\\n doi = {10.2514/6.2018-3107},\\n booktitle = {2018 Multidisciplinary Analysis and Optimization Conference},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Lulekar,  S.\",\"Ghassemi,  P.\",\"Chowdhury,  S.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf\",\"Website\":\"https://arc.aiaa.org/doi/10.2514/6.2018-3107\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring\",\"type\":\"inproceedings\",\"year\":\"2019\",\"websites\":\"https://arc.aiaa.org/doi/10.2514/6.2019-2983\",\"month\":\"6\",\"publisher\":\"American Institute of Aeronautics and Astronautics\",\"day\":\"17\",\"city\":\"Dallas, TX, USA\",\"id\":\"f8e1829b-a456-33db-98eb-e0b2402024c0\",\"created\":\"2019-07-08T20:56:27.362Z\",\"accessed\":\"2019-07-08\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:06.192Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2019amrpbs\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to \\\"when\\\" to sample and \\\"how many\\\" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards \\\"where\\\" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (\\\"q-EI\\\"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Lulekar, Sumeet S. and Chowdhury, Souma\",\"doi\":\"10.2514/6.2019-2983\",\"booktitle\":\"AIAA Aviation 2019 Forum\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring},\\n type = {inproceedings},\\n year = {2019},\\n websites = {https://arc.aiaa.org/doi/10.2514/6.2019-2983},\\n month = {6},\\n publisher = {American Institute of Aeronautics and Astronautics},\\n day = {17},\\n city = {Dallas, TX, USA},\\n id = {f8e1829b-a456-33db-98eb-e0b2402024c0},\\n created = {2019-07-08T20:56:27.362Z},\\n accessed = {2019-07-08},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:06.192Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2019amrpbs},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to \\\"when\\\" to sample and \\\"how many\\\" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards \\\"where\\\" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (\\\"q-EI\\\"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Lulekar, Sumeet S. and Chowdhury, Souma},\\n doi = {10.2514/6.2019-2983},\\n booktitle = {AIAA Aviation 2019 Forum},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Lulekar,  S., S.\",\"Chowdhury,  S.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf\",\"Website\":\"https://arc.aiaa.org/doi/10.2514/6.2019-2983\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Integrating Exploratory Data Analytics into ReaxFF Parameterization\",\"type\":\"article\",\"year\":\"2018\",\"pages\":\"1300-1310\",\"volume\":\"8\",\"websites\":\"https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article\",\"month\":\"9\",\"day\":\"18\",\"id\":\"41741ef3-ea42-36c3-b9f2-1d4ef12f282f\",\"created\":\"2019-07-08T20:56:48.639Z\",\"accessed\":\"2019-07-08\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:05.925Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"hernandez2018integrating\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron carbide (B–C) system. Through statistical data analytics, ReaxFF's parametric complexity was overcome via dimensional reduction (55 parameters) while retaining enough degrees of freedom to capture most of the variability in responses. Two potentials were identified which improved on an existing parameterization for the objective set if interest, showcasing the framework's capabilities.\",\"bibtype\":\"article\",\"author\":\"Hernández-Rivera, Efraín and Chowdhury, Souma and Coleman, Shawn P. and Ghassemi, Payam and Tschopp, Mark A.\",\"doi\":\"10.1557/mrc.2018.155\",\"journal\":\"MRS Communications\",\"number\":\"03\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@article{\\n title = {Integrating Exploratory Data Analytics into ReaxFF Parameterization},\\n type = {article},\\n year = {2018},\\n pages = {1300-1310},\\n volume = {8},\\n websites = {https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article},\\n month = {9},\\n day = {18},\\n id = {41741ef3-ea42-36c3-b9f2-1d4ef12f282f},\\n created = {2019-07-08T20:56:48.639Z},\\n accessed = {2019-07-08},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.925Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {hernandez2018integrating},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron carbide (B–C) system. Through statistical data analytics, ReaxFF's parametric complexity was overcome via dimensional reduction (55 parameters) while retaining enough degrees of freedom to capture most of the variability in responses. Two potentials were identified which improved on an existing parameterization for the objective set if interest, showcasing the framework's capabilities.},\\n bibtype = {article},\\n author = {Hernández-Rivera, Efraín and Chowdhury, Souma and Coleman, Shawn P. and Ghassemi, Payam and Tschopp, Mark A.},\\n doi = {10.1557/mrc.2018.155},\\n journal = {MRS Communications},\\n number = {03},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Hernández-Rivera,  E.\",\"Chowdhury,  S.\",\"Coleman,  S., P.\",\"Ghassemi,  P.\",\"Tschopp,  M., A.\"],\"urls\":{\"Website\":\"https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"hernndezrivera-chowdhury-coleman-ghassemi-tschopp-integratingexploratorydataanalyticsintoreaxffparameterization-2018\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\",\"chowdhury,  s\":\"https://124118259-887001909434481872.preview.editmysite.com/\"}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Optimal Metamodeling to Interpret Activity-Based Health Sensor Data\",\"type\":\"inproceedings\",\"year\":\"2017\",\"websites\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086\",\"month\":\"8\",\"publisher\":\"American Society of Mechanical Engineers\",\"day\":\"6\",\"city\":\"Cleveland, Ohio, USA\",\"id\":\"dcb348b4-c676-3a9f-bd62-e9cbe8eed25d\",\"created\":\"2019-07-08T21:19:22.379Z\",\"accessed\":\"2019-07-08\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:05.932Z\",\"read\":\"true\",\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2017health\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"© 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of < 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Mehmani, Ali, Chowdhury, Souma\",\"doi\":\"10.1115/DETC2017-68385\",\"booktitle\":\"Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {Optimal Metamodeling to Interpret Activity-Based Health Sensor Data},\\n type = {inproceedings},\\n year = {2017},\\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086},\\n month = {8},\\n publisher = {American Society of Mechanical Engineers},\\n day = {6},\\n city = {Cleveland, Ohio, USA},\\n id = {dcb348b4-c676-3a9f-bd62-e9cbe8eed25d},\\n created = {2019-07-08T21:19:22.379Z},\\n accessed = {2019-07-08},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.932Z},\\n read = {true},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2017health},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {© 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of < 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Mehmani, Ali, Chowdhury, Souma},\\n doi = {10.1115/DETC2017-68385},\\n booktitle = {Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Mehmani, Ali, Chowdhury,  S.\"],\"urls\":{\"Website\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions\",\"type\":\"article\",\"year\":\"2021\",\"websites\":\"https://link.springer.com/10.1007/s11517-021-02349-3\",\"month\":\"4\",\"day\":\"21\",\"id\":\"43908fa9-50fa-3a8c-87b9-5ab8d15369d6\",\"created\":\"2019-10-31T00:51:05.700Z\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2021-04-22T17:19:16.471Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"berthelson2019head\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Berthelson, P. R. and Ghassemi, P. and Wood, J. W. and Stubblefield, G. G. and Al-Graitti, A. J. and Jones, M. D. and Horstemeyer, M. F. and Chowdhury, S. and Prabhu, R. K.\",\"doi\":\"10.1007/s11517-021-02349-3\",\"journal\":\"Medical & Biological Engineering & Computing\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@article{\\n title = {A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions},\\n type = {article},\\n year = {2021},\\n websites = {https://link.springer.com/10.1007/s11517-021-02349-3},\\n month = {4},\\n day = {21},\\n id = {43908fa9-50fa-3a8c-87b9-5ab8d15369d6},\\n created = {2019-10-31T00:51:05.700Z},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2021-04-22T17:19:16.471Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {berthelson2019head},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Berthelson, P. R. and Ghassemi, P. and Wood, J. 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With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.\",\"bibtype\":\"inproceedings\",\"author\":\"Liu, Yuanzhi and Ghassemi, Payam and Chowdhury, Souma and Zhang, Jie\",\"doi\":\"10.1115/DETC2018-85620\",\"booktitle\":\"Volume 2B: 44th Design Automation Conference\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System},\\n type = {inproceedings},\\n year = {2018},\\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832},\\n month = {8},\\n publisher = {American Society of Mechanical Engineers},\\n day = {26},\\n id = {de1afd2c-7502-3e5f-afe0-2feeeb76b88b},\\n created = {2020-06-14T21:18:56.238Z},\\n accessed = {2020-06-14},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.724Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {liu2018battery},\\n private_publication = {false},\\n abstract = {This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.},\\n bibtype = {inproceedings},\\n author = {Liu, Yuanzhi and Ghassemi, Payam and Chowdhury, Souma and Zhang, Jie},\\n doi = {10.1115/DETC2018-85620},\\n booktitle = {Volume 2B: 44th Design Automation Conference},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Liu,  Y.\",\"Ghassemi,  P.\",\"Chowdhury,  S.\",\"Zhang,  J.\"],\"urls\":{\"Website\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring\",\"type\":\"inproceedings\",\"year\":\"2020\",\"websites\":\"https://arc.aiaa.org/doi/10.2514/6.2020-3183\",\"month\":\"6\",\"publisher\":\"American Institute of Aeronautics and Astronautics\",\"day\":\"15\",\"city\":\"Virtual 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Aeronautics and Astronautics},\\n day = {15},\\n city = {Virtual Event},\\n id = {c0028f93-0234-3d27-9b51-13ad2dc29da7},\\n created = {2020-06-15T01:47:41.205Z},\\n accessed = {2020-06-14},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:06.000Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2020physbo},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Behjat, Amir and Zeng, Chen and Lulekar, Sumeet S. and Rai, Rahul and Chowdhury, Souma},\\n doi = {10.2514/6.2020-3183},\\n booktitle = {AIAA AVIATION 2020 FORUM},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Behjat,  A.\",\"Zeng,  C.\",\"Lulekar,  S., S.\",\"Rai,  R.\",\"Chowdhury,  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Reduction\",\"type\":\"article\",\"year\":\"2021\",\"pages\":\"880-892\",\"volume\":\"59\",\"websites\":\"https://arc.aiaa.org/doi/10.2514/1.J059613\",\"month\":\"3\",\"day\":\"29\",\"id\":\"ea6a8420-0e79-3fa1-b95f-b41ba0498039\",\"created\":\"2020-06-19T17:23:00.226Z\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2021-03-05T03:33:33.326Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Lulekar2020\",\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Sanjay Lulekar, Sumeet and Ghassemi, Payam and Alsalih, Haidar and Chowdhury, Souma\",\"doi\":\"10.2514/1.J059613\",\"journal\":\"AIAA Journal\",\"number\":\"3\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@article{\\n title = {Adaptive-Fidelity Design Automation Framework to Explore Bioinspired Surface Riblets for Drag Reduction},\\n type = {article},\\n year = {2021},\\n pages = {880-892},\\n volume = 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 title = {Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings},
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 pages = {V02BT03A026},
 websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
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 city = {Cleveland, Ohio, USA},
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 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Zhu, Kaige and Chowdhury, Souma},
 doi = {10.1115/DETC2017-68350},
 booktitle = {Volume 2B: 43rd Design Automation Conference},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency},
 type = {inproceedings},
 year = {2018},
 websites = {https://arc.aiaa.org/doi/10.2514/6.2018-3107},
 month = {6},
 publisher = {American Institute of Aeronautics and Astronautics},
 day = {25},
 city = {Atlanta, GA, USA},
 id = {cc163f9a-922a-36bd-b7d4-712619fd852f},
 created = {2019-07-08T20:54:02.941Z},
 accessed = {2019-07-08},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.767Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {lulekar2018riblet},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.},
 bibtype = {inproceedings},
 author = {Lulekar, Sumeet and Ghassemi, Payam and Chowdhury, Souma},
 doi = {10.2514/6.2018-3107},
 booktitle = {2018 Multidisciplinary Analysis and Optimization Conference},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring},
 type = {inproceedings},
 year = {2019},
 websites = {https://arc.aiaa.org/doi/10.2514/6.2019-2983},
 month = {6},
 publisher = {American Institute of Aeronautics and Astronautics},
 day = {17},
 city = {Dallas, TX, USA},
 id = {f8e1829b-a456-33db-98eb-e0b2402024c0},
 created = {2019-07-08T20:56:27.362Z},
 accessed = {2019-07-08},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:06.192Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2019amrpbs},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to "when" to sample and "how many" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards "where" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria ("q-EI"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Lulekar, Sumeet S. and Chowdhury, Souma},
 doi = {10.2514/6.2019-2983},
 booktitle = {AIAA Aviation 2019 Forum},
 keywords = {ADAMS,SBO}
}

@article{
 title = {Integrating Exploratory Data Analytics into ReaxFF Parameterization},
 type = {article},
 year = {2018},
 pages = {1300-1310},
 volume = {8},
 websites = {https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article},
 month = {9},
 day = {18},
 id = {41741ef3-ea42-36c3-b9f2-1d4ef12f282f},
 created = {2019-07-08T20:56:48.639Z},
 accessed = {2019-07-08},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.925Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {hernandez2018integrating},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron carbide (B–C) system. Through statistical data analytics, ReaxFF's parametric complexity was overcome via dimensional reduction (55 parameters) while retaining enough degrees of freedom to capture most of the variability in responses. Two potentials were identified which improved on an existing parameterization for the objective set if interest, showcasing the framework's capabilities.},
 bibtype = {article},
 author = {Hernández-Rivera, Efraín and Chowdhury, Souma and Coleman, Shawn P. and Ghassemi, Payam and Tschopp, Mark A.},
 doi = {10.1557/mrc.2018.155},
 journal = {MRS Communications},
 number = {03},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Optimal Metamodeling to Interpret Activity-Based Health Sensor Data},
 type = {inproceedings},
 year = {2017},
 websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
 day = {6},
 city = {Cleveland, Ohio, USA},
 id = {dcb348b4-c676-3a9f-bd62-e9cbe8eed25d},
 created = {2019-07-08T21:19:22.379Z},
 accessed = {2019-07-08},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.932Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2017health},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {© 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of < 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Mehmani, Ali, Chowdhury, Souma},
 doi = {10.1115/DETC2017-68385},
 booktitle = {Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices},
 keywords = {ADAMS,SBO}
}

@article{
 title = {A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions},
 type = {article},
 year = {2021},
 websites = {https://link.springer.com/10.1007/s11517-021-02349-3},
 month = {4},
 day = {21},
 id = {43908fa9-50fa-3a8c-87b9-5ab8d15369d6},
 created = {2019-10-31T00:51:05.700Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2021-04-22T17:19:16.471Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {berthelson2019head},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 bibtype = {article},
 author = {Berthelson, P. R. and Ghassemi, P. and Wood, J. W. and Stubblefield, G. G. and Al-Graitti, A. J. and Jones, M. D. and Horstemeyer, M. F. and Chowdhury, S. and Prabhu, R. K.},
 doi = {10.1007/s11517-021-02349-3},
 journal = {Medical & Biological Engineering & Computing},
 keywords = {ADAMS,SBO}
}

@article{
 title = {Adaptive In Situ Model Refinement for Surrogate-augmented Population-based Optimization},
 type = {article},
 year = {2020},
 websites = {http://link.springer.com/10.1007/s00158-020-02592-6},
 month = {5},
 day = {26},
 id = {aac13dbf-f6fd-3060-aaa9-29efd776db80},
 created = {2020-05-27T09:09:21.463Z},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-23T07:43:24.450Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2020amr},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 bibtype = {article},
 author = {Ghassemi, Payam and Mehmani, Ali and Chowdhury, Souma},
 doi = {10.1007/s00158-020-02592-6},
 journal = {Structural and Multidisciplinary Optimization},
 keywords = {ADAMS,Journal,ML,SBO}
}

@inproceedings{
 title = {Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System},
 type = {inproceedings},
 year = {2018},
 websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
 day = {26},
 id = {de1afd2c-7502-3e5f-afe0-2feeeb76b88b},
 created = {2020-06-14T21:18:56.238Z},
 accessed = {2020-06-14},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.724Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {liu2018battery},
 private_publication = {false},
 abstract = {This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.},
 bibtype = {inproceedings},
 author = {Liu, Yuanzhi and Ghassemi, Payam and Chowdhury, Souma and Zhang, Jie},
 doi = {10.1115/DETC2018-85620},
 booktitle = {Volume 2B: 44th Design Automation Conference},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring},
 type = {inproceedings},
 year = {2020},
 websites = {https://arc.aiaa.org/doi/10.2514/6.2020-3183},
 month = {6},
 publisher = {American Institute of Aeronautics and Astronautics},
 day = {15},
 city = {Virtual Event},
 id = {c0028f93-0234-3d27-9b51-13ad2dc29da7},
 created = {2020-06-15T01:47:41.205Z},
 accessed = {2020-06-14},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:06.000Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2020physbo},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Behjat, Amir and Zeng, Chen and Lulekar, Sumeet S. and Rai, Rahul and Chowdhury, Souma},
 doi = {10.2514/6.2020-3183},
 booktitle = {AIAA AVIATION 2020 FORUM},
 keywords = {ADAMS,SBO}
}

@article{
 title = {Adaptive-Fidelity Design Automation Framework to Explore Bioinspired Surface Riblets for Drag Reduction},
 type = {article},
 year = {2021},
 pages = {880-892},
 volume = {59},
 websites = {https://arc.aiaa.org/doi/10.2514/1.J059613},
 month = {3},
 day = {29},
 id = {ea6a8420-0e79-3fa1-b95f-b41ba0498039},
 created = {2020-06-19T17:23:00.226Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2021-03-05T03:33:33.326Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Lulekar2020},
 private_publication = {false},
 bibtype = {article},
 author = {Sanjay Lulekar, Sumeet and Ghassemi, Payam and Alsalih, Haidar and Chowdhury, Souma},
 doi = {10.2514/1.J059613},
 journal = {AIAA Journal},
 number = {3},
 keywords = {ADAMS,SBO}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3?jsonp=1&amp;owner=Ghassemi&amp;authorFirst=1&amp;filter=keywords:SBO&amp;theme=side&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3?jsonp=1&amp;owner=Ghassemi&amp;authorFirst=1&amp;filter=keywords:SBO&amp;theme=side\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3?jsonp=1&amp;owner=Ghassemi&amp;authorFirst=1&amp;filter=keywords:SBO&amp;theme=side\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"berthelson-ghassemi-wood-stubblefield-algraitti-jones-horstemeyer-chowdhury-etal-afiniteelementguidedmathematicalsurrogatemodelingapproachforassessingoccupantinjurytrendsacrossvariationsinsimplifiedvehicularimpactconditions-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Berthelson,  P., R.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Wood,  J., W.; Stubblefield,  G., G.; Al-Graitti,  A., J.; Jones,  M., D.; Horstemeyer,  M., F.; Chowdhury,  S.;  and Prabhu,  R., K.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/10.1007/s11517-021-02349-3\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'berthelson-ghassemi-wood-stubblefield-algraitti-jones-horstemeyer-chowdhury-etal-afiniteelementguidedmathematicalsurrogatemodelingapproachforassessingoccupantinjurytrendsacrossvariationsinsimplifiedvehicularimpactconditions-2021', 'https://link.springer.com/10.1007/s11517-021-02349-3', event);\">\n    A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>Medical & Biological Engineering & Computing</i>. 4 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/10.1007/s11517-021-02349-3\"\n     onclick=\"log_download('berthelson-ghassemi-wood-stubblefield-algraitti-jones-horstemeyer-chowdhury-etal-afiniteelementguidedmathematicalsurrogatemodelingapproachforassessingoccupantinjurytrendsacrossvariationsinsimplifiedvehicularimpactconditions-2021', 'https://link.springer.com/10.1007/s11517-021-02349-3', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s11517-021-02349-3\"\n     onclick=\"log_download('berthelson-ghassemi-wood-stubblefield-algraitti-jones-horstemeyer-chowdhury-etal-afiniteelementguidedmathematicalsurrogatemodelingapproachforassessingoccupantinjurytrendsacrossvariationsinsimplifiedvehicularimpactconditions-2021', 'http://doi.org/10.1007/s11517-021-02349-3', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/berthelson-ghassemi-wood-stubblefield-algraitti-jones-horstemeyer-chowdhury-etal-afiniteelementguidedmathematicalsurrogatemodelingapproachforassessingoccupantinjurytrendsacrossvariationsinsimplifiedvehicularimpactconditions-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('berthelson-ghassemi-wood-stubblefield-algraitti-jones-horstemeyer-chowdhury-etal-afiniteelementguidedmathematicalsurrogatemodelingapproachforassessingoccupantinjurytrendsacrossvariationsinsimplifiedvehicularimpactconditions-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A finite element–guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions},\n type = {article},\n year = {2021},\n websites = {https://link.springer.com/10.1007/s11517-021-02349-3},\n month = {4},\n day = {21},\n id = {43908fa9-50fa-3a8c-87b9-5ab8d15369d6},\n created = {2019-10-31T00:51:05.700Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2021-04-22T17:19:16.471Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {berthelson2019head},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n bibtype = {article},\n author = {Berthelson, P. R. and Ghassemi, P. and Wood, J. W. and Stubblefield, G. G. and Al-Graitti, A. J. and Jones, M. D. and Horstemeyer, M. F. and Chowdhury, S. and Prabhu, R. K.},\n doi = {10.1007/s11517-021-02349-3},\n journal = {Medical &amp; Biological Engineering &amp; Computing},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sanjaylulekar-ghassemi-alsalih-chowdhury-adaptivefidelitydesignautomationframeworktoexplorebioinspiredsurfaceribletsfordragreduction-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Sanjay Lulekar,  S.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Alsalih,  H.;  and <a class=\"bibbase author link\" href=\"https://124118259-887001909434481872.preview.editmysite.com/\">Chowdhury,  S.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/1.J059613\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sanjaylulekar-ghassemi-alsalih-chowdhury-adaptivefidelitydesignautomationframeworktoexplorebioinspiredsurfaceribletsfordragreduction-2021', 'https://arc.aiaa.org/doi/10.2514/1.J059613', event);\">\n    Adaptive-Fidelity Design Automation Framework to Explore Bioinspired Surface Riblets for Drag Reduction.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>AIAA Journal</i>, 59(3): 880-892. 3 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/1.J059613\"\n     onclick=\"log_download('sanjaylulekar-ghassemi-alsalih-chowdhury-adaptivefidelitydesignautomationframeworktoexplorebioinspiredsurfaceribletsfordragreduction-2021', 'https://arc.aiaa.org/doi/10.2514/1.J059613', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Adaptive-Fidelity Design Automation Framework to Explore Bioinspired Surface Riblets for Drag Reduction [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2514/1.J059613\"\n     onclick=\"log_download('sanjaylulekar-ghassemi-alsalih-chowdhury-adaptivefidelitydesignautomationframeworktoexplorebioinspiredsurfaceribletsfordragreduction-2021', 'http://doi.org/10.2514/1.J059613', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sanjaylulekar-ghassemi-alsalih-chowdhury-adaptivefidelitydesignautomationframeworktoexplorebioinspiredsurfaceribletsfordragreduction-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sanjaylulekar-ghassemi-alsalih-chowdhury-adaptivefidelitydesignautomationframeworktoexplorebioinspiredsurfaceribletsfordragreduction-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Adaptive-Fidelity Design Automation Framework to Explore Bioinspired Surface Riblets for Drag Reduction},\n type = {article},\n year = {2021},\n pages = {880-892},\n volume = {59},\n websites = {https://arc.aiaa.org/doi/10.2514/1.J059613},\n month = {3},\n day = {29},\n id = {ea6a8420-0e79-3fa1-b95f-b41ba0498039},\n created = {2020-06-19T17:23:00.226Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2021-03-05T03:33:33.326Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Lulekar2020},\n private_publication = {false},\n bibtype = {article},\n author = {Sanjay Lulekar, Sumeet and Ghassemi, Payam and Alsalih, Haidar and Chowdhury, Souma},\n doi = {10.2514/1.J059613},\n journal = {AIAA Journal},\n number = {3},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-mehmani-chowdhury-adaptiveinsitumodelrefinementforsurrogateaugmentedpopulationbasedoptimization-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Mehmani,  A.;  and <a class=\"bibbase author link\" href=\"https://124118259-887001909434481872.preview.editmysite.com/\">Chowdhury,  S.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/42ce25e8-fda1-fc8b-58d1-2eb0ab053fbe/ghassemi2020amr_pub.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-mehmani-chowdhury-adaptiveinsitumodelrefinementforsurrogateaugmentedpopulationbasedoptimization-2020', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/42ce25e8-fda1-fc8b-58d1-2eb0ab053fbe/ghassemi2020amr_pub.pdf.pdf', event);\">\n    Adaptive In Situ Model Refinement for Surrogate-augmented Population-based Optimization.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>Structural and Multidisciplinary Optimization</i>. 5 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/42ce25e8-fda1-fc8b-58d1-2eb0ab053fbe/ghassemi2020amr_pub.pdf.pdf\"\n     onclick=\"log_download('ghassemi-mehmani-chowdhury-adaptiveinsitumodelrefinementforsurrogateaugmentedpopulationbasedoptimization-2020', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/42ce25e8-fda1-fc8b-58d1-2eb0ab053fbe/ghassemi2020amr_pub.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Adaptive In Situ Model Refinement for Surrogate-augmented Population-based Optimization [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://link.springer.com/10.1007/s00158-020-02592-6\"\n     onclick=\"log_download('ghassemi-mehmani-chowdhury-adaptiveinsitumodelrefinementforsurrogateaugmentedpopulationbasedoptimization-2020', 'http://link.springer.com/10.1007/s00158-020-02592-6', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Adaptive In Situ Model Refinement for Surrogate-augmented Population-based Optimization [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s00158-020-02592-6\"\n     onclick=\"log_download('ghassemi-mehmani-chowdhury-adaptiveinsitumodelrefinementforsurrogateaugmentedpopulationbasedoptimization-2020', 'http://doi.org/10.1007/s00158-020-02592-6', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-mehmani-chowdhury-adaptiveinsitumodelrefinementforsurrogateaugmentedpopulationbasedoptimization-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-mehmani-chowdhury-adaptiveinsitumodelrefinementforsurrogateaugmentedpopulationbasedoptimization-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Adaptive In Situ Model Refinement for Surrogate-augmented Population-based Optimization},\n type = {article},\n year = {2020},\n websites = {http://link.springer.com/10.1007/s00158-020-02592-6},\n month = {5},\n day = {26},\n id = {aac13dbf-f6fd-3060-aaa9-29efd776db80},\n created = {2020-05-27T09:09:21.463Z},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-23T07:43:24.450Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2020amr},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n bibtype = {article},\n author = {Ghassemi, Payam and Mehmani, Ali and Chowdhury, Souma},\n doi = {10.1007/s00158-020-02592-6},\n journal = {Structural and Multidisciplinary Optimization},\n keywords = {ADAMS,Journal,ML,SBO}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Behjat,  A.; Zeng,  C.; Lulekar,  S., S.; Rai,  R.;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf', event);\">\n    Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>AIAA AVIATION 2020 FORUM</i>, 6 2020. American Institute of Aeronautics and Astronautics\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf\"\n     onclick=\"log_download('ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/6.2020-3183\"\n     onclick=\"log_download('ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020', 'https://arc.aiaa.org/doi/10.2514/6.2020-3183', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2514/6.2020-3183\"\n     onclick=\"log_download('ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020', 'http://doi.org/10.2514/6.2020-3183', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring},\n type = {inproceedings},\n year = {2020},\n websites = {https://arc.aiaa.org/doi/10.2514/6.2020-3183},\n month = {6},\n publisher = {American Institute of Aeronautics and Astronautics},\n day = {15},\n city = {Virtual Event},\n id = {c0028f93-0234-3d27-9b51-13ad2dc29da7},\n created = {2020-06-15T01:47:41.205Z},\n accessed = {2020-06-14},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:06.000Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2020physbo},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Behjat, Amir and Zeng, Chen and Lulekar, Sumeet S. and Rai, Rahul and Chowdhury, Souma},\n doi = {10.2514/6.2020-3183},\n booktitle = {AIAA AVIATION 2020 FORUM},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Lulekar,  S., S.;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf', event);\">\n    Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>AIAA Aviation 2019 Forum</i>, 6 2019. American Institute of Aeronautics and Astronautics\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf\"\n     onclick=\"log_download('ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/6.2019-2983\"\n     onclick=\"log_download('ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019', 'https://arc.aiaa.org/doi/10.2514/6.2019-2983', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2514/6.2019-2983\"\n     onclick=\"log_download('ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019', 'http://doi.org/10.2514/6.2019-2983', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring},\n type = {inproceedings},\n year = {2019},\n websites = {https://arc.aiaa.org/doi/10.2514/6.2019-2983},\n month = {6},\n publisher = {American Institute of Aeronautics and Astronautics},\n day = {17},\n city = {Dallas, TX, USA},\n id = {f8e1829b-a456-33db-98eb-e0b2402024c0},\n created = {2019-07-08T20:56:27.362Z},\n accessed = {2019-07-08},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:06.192Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2019amrpbs},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to &quot;when&quot; to sample and &quot;how many&quot; samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards &quot;where&quot; to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (&quot;q-EI&quot;), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Lulekar, Sumeet S. and Chowdhury, Souma},\n doi = {10.2514/6.2019-2983},\n booktitle = {AIAA Aviation 2019 Forum},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to \"when\" to sample and \"how many\" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards \"where\" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (\"q-EI\"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Lulekar,  S.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf', event);\">\n    CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>2018 Multidisciplinary Analysis and Optimization Conference</i>, 6 2018. American Institute of Aeronautics and Astronautics\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf\"\n     onclick=\"log_download('lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/6.2018-3107\"\n     onclick=\"log_download('lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018', 'https://arc.aiaa.org/doi/10.2514/6.2018-3107', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2514/6.2018-3107\"\n     onclick=\"log_download('lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018', 'http://doi.org/10.2514/6.2018-3107', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency},\n type = {inproceedings},\n year = {2018},\n websites = {https://arc.aiaa.org/doi/10.2514/6.2018-3107},\n month = {6},\n publisher = {American Institute of Aeronautics and Astronautics},\n day = {25},\n city = {Atlanta, GA, USA},\n id = {cc163f9a-922a-36bd-b7d4-712619fd852f},\n created = {2019-07-08T20:54:02.941Z},\n accessed = {2019-07-08},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.767Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {lulekar2018riblet},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.},\n bibtype = {inproceedings},\n author = {Lulekar, Sumeet and Ghassemi, Payam and Chowdhury, Souma},\n doi = {10.2514/6.2018-3107},\n booktitle = {2018 Multidisciplinary Analysis and Optimization Conference},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"hernndezrivera-chowdhury-coleman-ghassemi-tschopp-integratingexploratorydataanalyticsintoreaxffparameterization-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Hernández-Rivera,  E.; <a class=\"bibbase author link\" href=\"https://124118259-887001909434481872.preview.editmysite.com/\">Chowdhury,  S.</a>; Coleman,  S., P.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Tschopp,  M., A.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'hernndezrivera-chowdhury-coleman-ghassemi-tschopp-integratingexploratorydataanalyticsintoreaxffparameterization-2018', 'https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article', event);\">\n    Integrating Exploratory Data Analytics into ReaxFF Parameterization.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>MRS Communications</i>, 8(03): 1300-1310. 9 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article\"\n     onclick=\"log_download('hernndezrivera-chowdhury-coleman-ghassemi-tschopp-integratingexploratorydataanalyticsintoreaxffparameterization-2018', 'https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Integrating Exploratory Data Analytics into ReaxFF Parameterization [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1557/mrc.2018.155\"\n     onclick=\"log_download('hernndezrivera-chowdhury-coleman-ghassemi-tschopp-integratingexploratorydataanalyticsintoreaxffparameterization-2018', 'http://doi.org/10.1557/mrc.2018.155', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/hernndezrivera-chowdhury-coleman-ghassemi-tschopp-integratingexploratorydataanalyticsintoreaxffparameterization-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('hernndezrivera-chowdhury-coleman-ghassemi-tschopp-integratingexploratorydataanalyticsintoreaxffparameterization-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Integrating Exploratory Data Analytics into ReaxFF Parameterization},\n type = {article},\n year = {2018},\n pages = {1300-1310},\n volume = {8},\n websites = {https://www.cambridge.org/core/product/identifier/S2159685918001556/type/journal_article},\n month = {9},\n day = {18},\n id = {41741ef3-ea42-36c3-b9f2-1d4ef12f282f},\n created = {2019-07-08T20:56:48.639Z},\n accessed = {2019-07-08},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.925Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {hernandez2018integrating},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron carbide (B–C) system. Through statistical data analytics, ReaxFF&#x27;s parametric complexity was overcome via dimensional reduction (55 parameters) while retaining enough degrees of freedom to capture most of the variability in responses. Two potentials were identified which improved on an existing parameterization for the objective set if interest, showcasing the framework&#x27;s capabilities.},\n bibtype = {article},\n author = {Hernández-Rivera, Efraín and Chowdhury, Souma and Coleman, Shawn P. and Ghassemi, Payam and Tschopp, Mark A.},\n doi = {10.1557/mrc.2018.155},\n journal = {MRS Communications},\n number = {03},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron carbide (B–C) system. Through statistical data analytics, ReaxFF's parametric complexity was overcome via dimensional reduction (55 parameters) while retaining enough degrees of freedom to capture most of the variability in responses. Two potentials were identified which improved on an existing parameterization for the objective set if interest, showcasing the framework's capabilities.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Liu,  Y.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Chowdhury,  S.;  and Zhang,  J.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832', event);\">\n    Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 2B: 44th Design Automation Conference</i>, 8 2018. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832\"\n     onclick=\"log_download('liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2018-85620\"\n     onclick=\"log_download('liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018', 'http://doi.org/10.1115/DETC2018-85620', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System},\n type = {inproceedings},\n year = {2018},\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {26},\n id = {de1afd2c-7502-3e5f-afe0-2feeeb76b88b},\n created = {2020-06-14T21:18:56.238Z},\n accessed = {2020-06-14},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.724Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {liu2018battery},\n private_publication = {false},\n abstract = {This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.},\n bibtype = {inproceedings},\n author = {Liu, Yuanzhi and Ghassemi, Payam and Chowdhury, Souma and Zhang, Jie},\n doi = {10.1115/DETC2018-85620},\n booktitle = {Volume 2B: 44th Design Automation Conference},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Zhu,  K.;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375', event);\">\n    Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 2B: 43rd Design Automation Conference</i>, pages V02BT03A026, 8 2017. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375\"\n     onclick=\"log_download('ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2017-68350\"\n     onclick=\"log_download('ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017', 'http://doi.org/10.1115/DETC2017-68350', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings},\n type = {inproceedings},\n year = {2017},\n pages = {V02BT03A026},\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {6},\n city = {Cleveland, Ohio, USA},\n id = {b74d514d-3038-3197-aa98-1cad32734741},\n created = {2019-07-08T20:51:37.509Z},\n accessed = {2019-07-08},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:06.003Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2017optimal},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Zhu, Kaige and Chowdhury, Souma},\n doi = {10.1115/DETC2017-68350},\n booktitle = {Volume 2B: 43rd Design Automation Conference},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Mehmani, Ali, Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086', event);\">\n    Optimal Metamodeling to Interpret Activity-Based Health Sensor Data.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices</i>, 8 2017. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086\"\n     onclick=\"log_download('ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Optimal Metamodeling to Interpret Activity-Based Health Sensor Data [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2017-68385\"\n     onclick=\"log_download('ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017', 'http://doi.org/10.1115/DETC2017-68385', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Optimal Metamodeling to Interpret Activity-Based Health Sensor Data},\n type = {inproceedings},\n year = {2017},\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {6},\n city = {Cleveland, Ohio, USA},\n id = {dcb348b4-c676-3a9f-bd62-e9cbe8eed25d},\n created = {2019-07-08T21:19:22.379Z},\n accessed = {2019-07-08},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.932Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2017health},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {© 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of &lt; 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Mehmani, Ali, Chowdhury, Souma},\n doi = {10.1115/DETC2017-68385},\n booktitle = {Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  © 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of < 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);