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  2023 (16)
Optimization of Transformer Ratio and Beam Loading in a Plasma Wakefield Accelerator with a Structure-Exploiting Algorithm. Su, Q.; Larson, J.; Dalichaouch, T. N.; Li, F.; An, W.; Hildebrand, L.; Zhao, Y.; Decyk, V.; Alves, P.; Wild, S. M.; and Mori, W. B. Physics of Plasmas, 30(5): 053108. 2023.
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Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models. Sürer, Ö.; Plumlee, M.; and Wild, S. M. Technometrics. 2023. To appear
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Designing a Framework for Solving Multiobjective Simulation Optimization Problems. Chang, T. H.; and Wild, S. M. Technical Report 2304.06881, arXiv, 2023.
Designing a Framework for Solving Multiobjective Simulation Optimization Problems [link]Paper   link   bibtex   7 downloads  
Numerical Evidence Against Advantage with Quantum Fidelity Kernels on Classical Data. Slattery, L.; Shaydulin, R.; Chakrabarti, S.; Pistoia, M.; Khairy, S.; and Wild, S. M. Physical Review A, 107: 062417. 2023.
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ParMOO: A Python Library for Parallel Multiobjective Simulation Optimization. Chang, T. H.; and Wild, S. M. Journal of Open Source Software, 8(82): 4468. 2023.
doi   link   bibtex   1 download  
Bandwidth Enables Generalization in Quantum Kernel Models. Canatar, A.; Peters, E.; Pehlevan, C.; Wild, S. M.; and Shaydulin, R. Transactions on Machine Learning Research. 2023.
Bandwidth Enables Generalization in Quantum Kernel Models [link]Paper   link   bibtex  
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization. Bollapragada, R.; and Wild, S. M. Mathematical Programming Computation, 15: 327–364. 2023.
doi   link   bibtex   2 downloads  
libEnsemble User Manual, Version 0.10.0. Hudson, S.; Larson, J.; Wild, S. M.; Bindel, D.; and Navarro, J. Technical Report Argonne National Laboratory, 2023.
libEnsemble User Manual, Version 0.10.0 [link]Paper   link   bibtex  
Constructing a Simulation Surrogate with Partially Observed Output. Chan, M. Y.; Plumlee, M.; and Wild, S. M. Technometrics. 2023. To appear
doi   link   bibtex   2 downloads  
A Framework for Fully Autonomous Design of Materials via Multiobjective Optimization and Active Learning: Challenges and Next Steps. Chang, T. H.; Elias, J. R.; Wild, S. M.; Chaudhuri, S.; and Libera, J. A. In Workshop on ``Machine Learning for Materials'' ICLR 2023, 2023.
A Framework for Fully Autonomous Design of Materials via Multiobjective Optimization and Active Learning: Challenges and Next Steps [link]Paper   link   bibtex  
Bayesian Calibration of Viscous Anisotropic Hydrodynamic Simulations of Heavy-Ion Collisions. Liyanage, D.; Sürer, Ö.; Plumlee, M.; Wild, S. M.; and Heinz, U. Technical Report 2302.14184, arXiv, 2023.
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Optimization and Learning with Zeroth-order Stochastic Oracles. Wild, S. M. SIAM News, 56(1). 2023.
Optimization and Learning with Zeroth-order Stochastic Oracles [link]Paper   link   bibtex   11 downloads  
A Stochastic Quasi-Newton Method in the Absence of Common Random Numbers. Menickelly, M.; Wild, S. M.; and Xie, M. Technical Report 2302.09128, arXiv, 2023.
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Modeling Approaches for Addressing Simple Unrelaxable Constraints with Unconstrained Optimization Methods. Padidar, M.; Larson, J.; and Wild, S. M. Optimization Letters, 17: 561–589. 2023.
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Derivative-Free Optimization of a Rapid-Cycling Synchrotron. Eldred, J. S.; Larson, J.; Padidar, M.; Stern, E.; and Wild, S. M. Optimization and Engineering, 24: 1289–1319. 2023.
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DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. Ćiprijanović, A.; Lewis, A.; Pedro, K.; Madireddy, S.; Nord, B.; Perdue, G. N.; and Wild, S. M. Machine Learning: Science and Technology, 4(2): 025013. 2023.
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  2022 (10)
Hashing-Like Johnson–Lindenstrauss Transforms and Their Extreme Singular Values. Dzahini, K. J.; and Wild, S. M. Technical Report 2212.14858, ArXiv, 2022.
Hashing-Like Johnson–Lindenstrauss Transforms and Their Extreme Singular Values [link]Paper   link   bibtex   1 download  
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations. Hudson, S.; Larson, J.; Navarro, J.; and Wild, S. M. IEEE Transactions on Parallel and Distributed Systems, 33(4): 977–988. 2022.
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DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification. Ćiprijanović, A.; Kafkes, D.; Snyder, G.; Sánchez, F. J.; Perdue, G. N.; Pedro, K.; Nord, B.; Madireddy, S.; and Wild, S. M. Machine Learning: Science and Technology, 3(3): 035007. 2022.
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Uncertainty Quantification in Breakup Reactions. Sürer, Ö.; Nunes, F. M.; Plumlee, M.; and Wild, S. M. Physical Review C, 106: 024607. 2022.
doi   link   bibtex   2 downloads  
Towards Precise and Accurate Calculations of Neutrinoless Double-Beta Decay: Project Scoping Workshop Report. Cirigliano, V.; Davoudi, Z.; Engel, J.; Furnstahl, R. J.; Hagen, G.; Heinz, U.; Hergert, H.; Horoi, M.; Johnson, C. W.; Lovato, A.; Mereghetti, E.; Nazarewicz, W.; Nicholson, A.; Papenbrock, T.; Pastore, S.; Plumlee, M.; Phillips, D. R.; Shanahan, P. E.; Stroberg, S. R.; Viens, F.; Walker-Loud, A.; Wendt, K. A.; and Wild, S. M. Technical Report 2207.01085, arXiv, 2022.
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Towards Precise and Accurate Calculations of Neutrinoless Double-Beta Decay. Cirigliano, V.; Davoudi, Z.; Engel, J.; Furnstahl, R. J.; Hagen, G.; Heinz, U.; Hergert, H.; Horoi, M.; Johnson, C. W.; Lovato, A.; Mereghetti, E.; Nazarewicz, W.; Nicholson, A.; Papenbrock, T.; Pastore, S.; Plumlee, M.; Phillips, D. R.; Shanahan, P. E.; Stroberg, S. R.; Viens, F.; Walker-Loud, A.; Wendt, K. A.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 49(12): 120502. 2022.
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Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. Ćiprijanović, A.; Lewis, A.; Pedro, K.; Madireddy, S.; Nord, B.; Perdue, G. N.; and Wild, S. M. In Machine Learning and the Physical Sciences – NeurIPS Workshop, 2022.
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Stochastic Trust-Region Algorithm in Random Subspaces with Convergence and Expected Complexity Analyses. Dzahini, K. J.; and Wild, S. M. Technical Report 2207.06452, ArXiv, 2022.
Stochastic Trust-Region Algorithm in Random Subspaces with Convergence and Expected Complexity Analyses [link]Paper   link   bibtex   3 downloads  
Stochastic Average Model Methods. Menickelly, M.; and Wild, S. M. Technical Report 2207.06305, ArXiv, 2022.
Stochastic Average Model Methods [link]Paper   link   bibtex   4 downloads  
Importance of Kernel Bandwidth in Quantum Machine Learning. Shaydulin, R.; and Wild, S. M. Physical Review A, 106(4): 042407. 2022.
doi   link   bibtex   1 download  
  2021 (13)
A Method for Convex Black-Box Integer Global Optimization. Larson, J.; Leyffer, S.; Palkar, P.; and Wild, S. M. Journal of Global Optimization, 80: 439–477. 2021.
doi   link   bibtex   6 downloads  
Tuning Multigrid Methods with Robust Optimization and Local Fourier Analysis. Brown, J.; He, Y.; MacLachlan, S.; Menickelly, M.; and Wild, S. M. SIAM Journal on Scientific Computing, 43(1): A109–A138. 2021.
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Scalable Statistical Inference of Photometric Redshift via Data Subsampling. Fadikar, A.; Wild, S. M.; and Chaves-Montero, J. In Computational Science – ICCS 2021, pages 245–258, 2021. Springer
doi   link   bibtex   1 download  
Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics. Phillips, D. R.; Furnstahl, R. J.; Heinz, U.; Maiti, T.; Nazarewicz, W.; Nunes, F. M.; Plumlee, M.; Pratola, M. T.; Pratt, S.; Viens, F. G.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 48(7): 072001. 2021.
doi   link   bibtex   3 downloads  
Hierarchical Analysis of Halo Center in Cosmology. Di, Z. W.; Rangel, E.; Yoo, S.; and Wild, S. M. In Computational Science – ICCS 2021, pages 671–684, 2021. Springer
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Machine Learning-Based Inversion of Nuclear Responses. Raghavan, K.; Balaprakash, P.; Lovato, A.; Rocco, N.; and Wild, S. M. Physical Review C, 103(3): 035502. 2021.
doi   link   bibtex   1 download  
Randomized Algorithms for Scientific Computing. Buluc, A.; Kolda, T. G.; Wild, S. M.; Anitescu, M.; DeGennaro, A.; Jakeman, J.; Kamath, C.; Ramakrishnan; Kannan; Lopes, M. E.; Martinsson, P.; Myers, K.; Nelson, J.; Restrepo, J. M.; Seshadhri, C.; Vrabie, D.; Wohlberg, B.; Wright, S. J.; Yang, C.; and Zwart, P. Technical Report U.S.\ Department of Energy, ASCR, 2021.
doi   link   bibtex   3 downloads  
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control. Renganathan, S. A.; Larson, J.; and Wild, S. M. Technical Report 2105.09824, ArXiv, 2021.
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control [link]Paper   link   bibtex   1 download  
Robustness of Deep Learning Algorithms in Astronomy – Galaxy Morphology Studies. Ćiprijanović, A.; Kafkes, D.; Perdue, G. N.; Pedro, K.; Snyder, G.; Sánchez, F. J.; Madireddy, S.; Wild, S. M.; and Nord, B. In Machine Learning and the Physical Sciences – NeurIPS Workshop, 2021.
Robustness of Deep Learning Algorithms in Astronomy – Galaxy Morphology Studies [link]Paper   link   bibtex  
Sequential Learning of Active Subspaces. Wycoff, N.; Binois, M.; and Wild, S. M. Journal of Computational and Graphical Statistics, 30(4): 1224–1237. 2021.
doi   link   bibtex   1 download  
Exploiting Symmetry Reduces the Cost of Training QAOA. Shaydulin, R.; and Wild, S. M. IEEE Transactions on Quantum Engineering, 2: 1–9. 2021.
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The Middle Science: Traversing Scale in Complex Many-Body Systems. Clark, A. E.; Adams, H.; Hernandez, R.; Krylov, A. I.; Niklasson, A. M. N.; Sarupria, S.; Wang, Y.; Wild, S. M.; and Yang, Q. ACS Central Science, 7(8): 1271–1287. 2021.
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Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives. Bollapragada, R.; Menickelly, M.; Nazarewicz, W.; O'Neal, J.; Reinhard, P.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 48(2): 024001. 2021.
doi   link   bibtex   2 downloads  
  2020 (6)
Derivative-Free Robust Optimization by Outer Approximations. Menickelly, M.; and Wild, S. M. Mathematical Programming, 179(1–2): 157–193. 2020.
doi   link   bibtex   2 downloads  
A Survey of Nonlinear Robust Optimization. Leyffer, S.; Menickelly, M.; Munson, T.; Vanaret, C.; and Wild, S. M. INFOR: Information Systems and Operational Research, 58(2): 342–373. 2020.
doi   link   bibtex   1 download  
Calibration of Energy Density Functionals with Deformed Nuclei. Schunck, N.; O'Neal, J.; Grosskopf, M.; Lawrence, E.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 47(7): 074001. 2020.
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Pufferscale: Rescaling HPC Data Services for High Energy Physics Applications. Cheriere, N.; Dorier, M.; Antoniu, G.; Wild, S. M.; Leyffer, S.; and Ross, R. In 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2020), 2020.
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Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization. Renganathan, S. A.; Larson, J.; and Wild, S. M. Technical Report 2006.08037, ArXiv, 2020.
Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization [link]Paper   link   bibtex   1 download  
Looking Ahead to the 2021 SIAM Conference on Computational Science and Engineering. Grigori, L.; Kilmer, M.; and Wild, S. M. SIAM News, 53(4). 2020.
Looking Ahead to the 2021 SIAM Conference on Computational Science and Engineering [pdf]2   Looking Ahead to the 2021 SIAM Conference on Computational Science and Engineering [link]Paper   link   bibtex   5 downloads  
  2019 (9)
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research. Balaprakash, P.; Egele, R.; Salim, M.; Vishwanath, V.; Wild, S. M.; Xia, F.; Brettin, T.; and Stevens, R. In International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), pages 37:1–37:33, 2019. ACM
doi   link   bibtex   1 download  
Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. Baker, N.; Alexander, F.; Bremer, T.; Hagberg, A.; Kevrekidis, Y.; Najm, H.; Parashar, M.; Patra, A.; Sethian, J.; Wild, S. M.; and Willcox, K. Technical Report U.S.\ Department of Energy, ASCR, 2019.
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Simultaneous Sensing Error Recovery and Tomographic Inversion Using an Optimization-Based Approach. Austin, A.; Di, Z. W.; Leyffer, S.; and Wild, S. M. SIAM Journal on Scientific Computing, 41(3): B497–B521. 2019.
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Derivative-Free Optimization Methods. Larson, J.; Menickelly, M.; and Wild, S. M. Acta Numerica, 28: 287–404. 2019.
doi   link   bibtex   4 downloads  
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization. Bollapragada, R.; and Wild, S. M. In Beyond First Order Methods in Machine Learning NeurIPS 2019 Workshop, 2019.
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization [link]Paper   link   bibtex   2 downloads  
Optimization-Based Simultaneous Alignment and Reconstruction in Fluorescence Tomography. Di, Z. W.; Chen, S.; Gursoy, D.; Paunesku, T.; Leyffer, S.; Wild, S. M.; and Vogt, S. Optics Letters, 44(17): 4331–4334. 2019.
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Large Area Imaging of Integrated Circuits Using Hard X-Ray Ptychography. Klug, J.; Deng, J.; Jiang, Y.; Preissner, C.; Di, Z. W.; Bicer, T.; Wild, S. M.; Vogt, S.; Sandberg, R.; and Honig, S. Technical Report 17-2, Argonne, 2019.
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Calibrating Sensing Drift in Tomographic Inversion. Huang, X.; Di, Z. W.; and Wild, S. M. In 2019 IEEE International Conference on Image Processing (ICIP19), pages 1267–1271, 2019.
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Adaptive Learning for Concept Drift in Application Performance Modeling. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Lockwood, G.; Ross, R.; Snyder, S.; and Wild, S. M. In Proceedings of the 48th International Conference on Parallel Processing (ICPP 2019), pages 79:1–79:11, 2019.
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  2018 (10)
3D X-Ray Imaging of Continuous Objects beyond the Depth of Focus Limit. Gilles, M. A. T.; Nashed, Y. S. G.; Du, M.; Jacobsen, C.; and Wild, S. M. Optica, 5(9): 1078–1086. 2018.
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Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing. Leyffer, S.; Wild, S. M.; Fagan, M.; Snir, M.; Palem, K.; Finkel, H.; and Yoshii, K. In 3rd International Workshop on Post-Moore Era Supercomputing (PMES), of SC18 Workshops, 2018.
Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing [link]Paper   link   bibtex  
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. Balaprakash, P.; Salim, M.; Uram, T. D.; Vishwanath, V.; and Wild, S. M. In 25th IEEE International Conference on High Performance Computing (HiPC18), 2018.
doi   link   bibtex   1 download  
Robust Learning of Trimmed Estimators via Manifold Sampling. Menickelly, M.; and Wild, S. M. In Modern Trends in Nonconvex Optimization for Machine Learning – ICML 2018 Workshop, 2018.
Robust Learning of Trimmed Estimators via Manifold Sampling [link]Paper   link   bibtex  
Accurate, Rapid Identification of Dislocation Lines in Coherent Diffractive Imaging via a Min-Max Optimization Formulation. Ulvestad, A.; Menickelly, M.; and Wild, S. M. AIP Advances, 8(1): 015114. 2018.
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Modeling I/O Performance Variability Using Conditional Variational Auto Encoders. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Ross, R.; Snyder, S.; and Wild, S. M. In IEEE Cluster Conference, pages 109–113, 2018.
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Understanding and Improving the Trust in Results of Numerical Simulations and Scientific Data Analytics. Cappello, F.; Gupta, R.; Di, S.; Constantinescu, E. M.; Peterka, T.; and Wild, S. M. In Euro-Par 2017: Parallel Processing Workshops, pages 545–556, 2018. Springer
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Asynchronously Parallel Optimization Solver for Finding Multiple Minima. Larson, J.; and Wild, S. M. Mathematical Programming Computation, 10(3): 303–332. 2018.
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Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Ross, R.; Snyder, S.; and Wild, S. M. In Lecture Notes in Computer Science: ISC High Performance 2018: High Performance Computing, pages 184–204, 2018. Springer
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Manifold Sampling for Optimization of Nonconvex Functions that are Piecewise Linear Compositions of Smooth Components. Khan, K. A.; Larson, J.; and Wild, S. M. SIAM Journal on Optimization, 28(4): 3001–3024. 2018.
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  2017 (9)
Advancing Cross-Cutting Ideas for Computational Climate Science. Evans, K.; Ng, E. G.; Caldwell, P.; Hoffman, F. M.; Jackson, C.; Kleese van Dam, K.; Leung, L.; Martin, D.; Ostrouchov, G.; Tuminaro, R.; Ullrich, P.; Wild, S. M.; and Williams, S. Technical Report U.S.\ Department of Energy, BER and ASCR, 2017.
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Bragg Coherent Modulation Imaging: Strain- and Defect-Sensitive Single Views of Extended Samples. Ulvestad, A.; Cha, W.; Calvo-Almazan, I.; Maddali, S.; Wild, S. M.; Maxey, E.; Dupraz, M.; and Hruszkewycz, S. O. Technical Report Argonne National Laboratory, MCS Division, 2017.
Bragg Coherent Modulation Imaging: Strain- and Defect-Sensitive Single Views of Extended Samples [link]Paper   link   bibtex  
Solving Derivative-Free Nonlinear Least Squares Problems with POUNDERS. Wild, S. M. In Terlaky, T.; Anjos, M. F.; and Ahmed, S., editor(s), Advances and Trends in Optimization with Engineering Applications, pages 529–540. SIAM, 2017.
doi   link   bibtex   5 downloads  
Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Ross, R.; Snyder, S.; and Wild, S. M. In 2017 International Conference on Networking, Architecture, and Storage (NAS), pages 1–10, 2017. IEEE
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CONORBIT: Constrained Optimization by Radial Basis Function Interpolation in Trust Regions. Regis, R. G.; and Wild, S. M. Optimization Methods and Software, 32(3): 552–580. 2017.
doi   link   bibtex   2 downloads  
Joint Reconstruction of X-Ray Fluorescence and Transmission Tomography. Di, Z. W.; Chen, S.; Hong, Y. P.; Jacobsen, C.; Leyffer, S.; and Wild, S. M. Optics Express, 25(12): 13107–13124. 2017.
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Improving FPGA Design Parameter Exploration: Timing, Power, and Area. Mametjanov, A.; Balaprakash, P.; Choudary, C.; Hovland, P. D.; Wild, S. M.; Sabin, G.; and Wolfe, G. Technical Report ANL/MCS-P7000-0117, Argonne National Laboratory, MCS Division, 2017.
Improving FPGA Design Parameter Exploration: Timing, Power, and Area [pdf]Paper   link   bibtex   1 download  
Uncertainty Quantification for Optical Model Parameters. Lovell, A. E.; Nunes, F. M.; Sarich, J.; and Wild, S. M. Physical Review C, 95(2): 024611. 2017.
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Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales. Foster, I.; Ainsworth, M.; Allen, B.; Bessac, J.; Cappello, F.; Choi, J. Y.; Constantinescu, E. M.; Davis, P. E.; Di, S.; Di, Z. W.; Guo, H.; Klasky, S.; Kleese van Dam, K.; Kurc, T.; Liu, Q.; Malik, A.; Mehta, K.; Mueller, K.; Munson, T.; Ostrouchov, G.; Parashar, M.; Peterka, T.; Pouchard, L.; Tao, D.; Tugluk, O.; Wild, S. M.; Wolf, M.; Wozniak, J. M.; Xu, W.; and Yoo, S. In Rivera, F. F.; Pena, T. F.; and Cabaleiro, J. C., editor(s), European Conference on Parallel Processing, pages 3–19, 2017. Springer
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  2016 (17)
Report of the DOE Workshop on Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing. Bethel, E. W.; and Greenwald (eds.), M. Technical Report U.S.\ Department of Energy, ASCR, 2016.
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AutoMOMML: Automatic Multi-Objective Modeling with Machine Learning. Balaprakash, P.; Tiwari, A.; Wild, S. M.; Carrington, L.; and Hovland, P. D. In Kunkel, M. J.; Balaji, P.; and Dongarra, J., editor(s), High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings, pages 219–239, 2016. Springer
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Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing. Leyffer, S.; Wild, S. M.; Fagan, M.; Snir, M.; Palem, K.; Finkel, H.; and Yoshii, K. Technical Report ANL/MCS-P6077-1016, Argonne National Laboratory MCS, 2016.
Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing [link]Paper   link   bibtex  
Exploiting Performance Portability in Search Algorithms for Autotuning. Roy, A.; Balaprakash, P.; Hovland, P. D.; and Wild, S. M. In 2016 IEEE International Parallel and Distributed Processing Symposium IPDPS Workshops, pages 1535–1544, 2016.
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Rejoinder: Modeling an Augmented Lagrangian for Blackbox Constrained Optimization. Gramacy, R. B.; Gray, G. A.; Le Digabel, S.; Lee, H. K. H.; Ranjan, P.; Wells, G.; and Wild, S. M. Technometrics, 58(1): 26–29. 2016.
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Novel Large Scale Simulation Process to Support DOT's CAFE Modeling System. Moawad, A.; Balaprakash, P.; Rousseau, A.; and Wild, S. M. International Journal of Automotive Technology, 17(6): 1067–1077. 2016.
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Origins and Optimization of Entanglement in Plasmonically Coupled Quantum Dots. Otten, M.; Larson, J.; Min, M.; Wild, S. M.; Pelton, M.; and Gray, S. K. Physical Review A, 94(2): 022312. 2016.
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Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing. Bethel, E. W.; Greenwald, M.; Kleese van Dam, K.; Parashar, M.; Wild, S. M.; and Wiley, H. S. In 2016 IEEE 12th International Conference on e-Science, pages 213–222, 2016.
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Bayesian Optimization under Mixed Constraints with a Slack-Variable Augmented Lagrangian. Picheny, V.; Gramacy, R. B.; Wild, S. M.; and Le Digabel, S. In Lee, D. D.; Sugiyama, M.; Luxburg, U. V.; Guyon, I.; and Garnett, R., editor(s), Advances in Neural Information Processing Systems 29, pages 1435–1443, 2016. Curran Associates, Inc.
Bayesian Optimization under Mixed Constraints with a Slack-Variable Augmented Lagrangian [pdf]Paper   link   bibtex   2 downloads  
Optimization-Based Approach for Joint X-Ray Fluorescence and Transmission Tomographic Inversion. Di, Z. W.; Leyffer, S.; and Wild, S. M. SIAM Journal on Imaging Sciences, 9(1): 1–23. 2016.
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Overcoming the Power Wall by Exploiting Application Inexactness and Emerging COTS Architectural Features: Trading Precision for Improving Application Quality. Fagan, M.; Schlachter, J.; Yoshii, K.; Leyffer, S.; Palem, K.; Snir, M.; Wild, S. M.; and Enz, C. In 29th IEEE International System-on-Chip Conference (SOCC), pages 241–246, 2016.
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Single-View Phase Retrieval of an Extended Sample by Exploiting Edge Detection and Sparsity. Tripathi, A.; McNulty, I.; Munson, T.; and Wild, S. M. Optics Express, 24(21): 24719–24738. 2016.
doi   link   bibtex   1 download  
Coherent Diffractive Imaging of Time-Evolving Samples with Improved Temporal Resolution. Ulvestad, A.; Tripathi, A.; Hruszkewycz, S. O.; Cha, W.; Fuoss, P. H.; Wild, S. M.; and Stephenson, G. B. Physical Review B, 93(18): 184105. 2016.
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Manifold Sampling for $\ell_1$ Nonconvex Optimization. Larson, J.; Menickelly, M.; and Wild, S. M. SIAM Journal on Optimization, 26(4): 2540–2563. 2016.
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A Batch, Derivative-Free Algorithm for Finding Multiple Local Minima. Larson, J.; and Wild, S. M. Optimization and Engineering, 17(1): 205–228. 2016.
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Modeling an Augmented Lagrangian for Blackbox Constrained Optimization. Gramacy, R. B.; Gray, G. A.; Le Digabel, S.; Lee, H. K. H.; Ranjan, P.; Wells, G.; and Wild, S. M. Technometrics, 58(1): 1–11. 2016.
doi   link   bibtex   1 download  
Non-Negative Matrix Analysis in X-Ray Spectromicroscopy: Choosing Regularizers. Mak, R.; Wild, S. M.; and Jacobsen, C. AIP Conference Proceedings, 1696: 020034. 2016. 12th International Conference on X-Ray Microscopy (XRM 2014)
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  2015 (18)
A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model. Higdon, D.; McDonnell, J. D.; Schunck, N.; Sarich, J.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034009. 2015.
doi   link   bibtex   1 download  
Quantification of Uncertainties in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Sarich, J.; Wild, S. M.; and Higdon, D. Nuclear Data Sheets, 123: 115–118. 2015.
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Uncertainty Quantification and Propagation in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Higdon, D.; Sarich, J.; and Wild, S. M. European Physical Journal A, 51(12): 169(1–14). 2015.
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Error Analysis in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Sarich, J.; Wild, S. M.; and Higdon, D. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034024. 2015.
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One-Nucleon Transfer Reactions and the Optical Potential. Nunes, F. M.; Lovell, A. E.; Ross, A.; Titus, L. J.; Charity, R. J.; Dickhoff, W. H.; Mahzoon, M. H.; Sarich, J.; and Wild, S. M. In Proceedings, 14th International Conference on Nuclear Reaction Mechanisms: Varenna, Italy, June 15-19, 2015, pages 123–130, 2015.
One-Nucleon Transfer Reactions and the Optical Potential [pdf]Paper   link   bibtex  
Statistical Uncertainties of a Chiral Interaction at Next-to-Next-To Leading Order. Ekström, A.; Carlsson, B. D.; Wendt, K.; Forssén, C.; Hjorth-Jensen, M.; Machleidt, R.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034003. 2015.
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Nuclear Energy Density Optimization: UNEDF2. Kortelainen, M.; McDonnell, J. D.; Nazarewicz, W.; Olsen, E.; Reinhard, P.; Sarich, J.; Schunck, N.; Wild, S. M.; Davesne, D.; Erler, J.; and Pastore, A. In JPS Conference Proceedings, volume 6, pages 020018, 2015.
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Collective I/O Tuning Using Analytical and Machine Learning Models. Isaila, F.; Balaprakash, P.; Wild, S. M.; Kimpe, D.; Latham, R.; Ross, R.; and Hovland, P. D. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2015), pages 128–137, 2015.
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Using Multiobjective Optimization for Automotive Component Sizing. Vijayagopal, R.; Chen, R.; Sharer, P.; Wild, S. M.; and Rousseau, A. World Electric Vehicle Journal, 7(2): 261–269. 2015.
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Dynamic Model-Driven Parallel I/O Performance Tuning. Behzad, B.; Byna, S.; Wild, S. M.; Prabhat; and Snir, M. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2015), pages 184–193, 2015.
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Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements. McDonnell, J. D.; Schunck, N.; Higdon, D.; Sarich, J.; Wild, S. M.; and Nazarewicz, W. Physical Review Letters, 114(12): 122501. 2015.
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ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines. Aithal, S. M.; and Wild, S. M. In Kunkel, J. M.; and Ludwig, T., editor(s), High Performance Computing (ISC-HPC 2015), volume 9137, pages 87–95, 2015. Springer
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Analysis of the Tradeoffs between Energy and Run Time for Multilevel Checkpointing. Balaprakash, P.; Bautista Gomez, L. A.; Bouguerra, M.; Wild, S. M.; Cappello, F.; and Hovland, P. D. In Jarvis, S. A.; Wright, S. A.; and Hammond, S. D., editor(s), High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, volume 8966, pages 249–263, 2015. Springer
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Derivative-Free Optimization for Parameter Estimation in Computational Nuclear Physics. Wild, S. M.; Sarich, J.; and Schunck, N. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034031. 2015.
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A Taxonomy of Constraints in Black-Box Simulation-Based Optimization. Le Digabel, S.; and Wild, S. M. Technical Report 1505.07881, ArXiv, 2015.
A Taxonomy of Constraints in Black-Box Simulation-Based Optimization [link]Paper   link   bibtex   1 download  
Development of a Reduced-Order Design/Optimization Tool for Automotive Engines Using Massively Parallel Computing. Aithal, S. M.; and Wild, S. M. In Proceedings of the SAE 12th International Conference on Engines & Vehicles, 2015.
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Visualizing and Improving the Robustness of Phase Retrieval Algorithms. Tripathi, A.; Leyffer, S.; Munson, T.; and Wild, S. M. Procedia Computer Science (ICCS 2015), 51: 815–824. 2015.
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Autotuning FPGA Design Parameters for Performance and Power. Mametjanov, A.; Balaprakash, P.; Choudary, C.; Hovland, P. D.; Wild, S. M.; and Sabin, G. In Proceedings of the 23rd IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM 2015), pages 84–91, 2015.
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  2014 (11)
DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges. Lucas, R.; Ang, J.; Bergman, K.; Borkar, S.; Carlson, W.; Carrington, L.; Chiu, G.; Colwell, R.; Dally, W.; Dongarra, J.; Geist, A.; Haring, R.; Hittinger, J.; Hoisie, A.; Klein, D. M.; Kogge, P.; Lethin, R.; Sarkar, V.; Schreiber, R.; Shalf, J.; Sterling, T.; Stevens, R.; Bashor, J.; Brightwell, R.; Coteus, P.; Debenedictus, E.; Hiller, J.; Kim, K. H.; Langston, H.; Murphy, R. M.; Webster, C.; Wild, S.; Grider, G.; Ross, R.; Leyffer, S.; and Laros III, J. Technical Report U.S.\ Department of Energy, ASCR, 2014.
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Energy-Performance Tradeoffs in Multilevel Checkpoint Strategies (Poster). Bautista Gomez, L. A.; Balaprakash, P.; Bouguerra, M.; Wild, S. M.; Cappello, F.; and Hovland, P. D. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2014), pages 278–279, 2014.
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Empirical Performance Modeling of GPU Kernels Using Active Learning. Balaprakash, P.; Rupp, K.; Mametjanov, A.; Gramacy, R. B.; Hovland, P. D.; and Wild, S. M. In Bader, M.; Bode, A.; Bungartz, H.; Gerndt, M.; Joubert, G. R.; and Peters, F., editor(s), Parallel Computing: Accelerating Computational Science and Engineering (ParCo2013), volume 25, pages 646–655, 2014. IOS Press
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SciDAC: Accelerating Scientific Discovery, Transforming Computational Science. D'Azevedo, E.; Ng, E. G.; and Wild, S. M. SIAM News, 47(3). 2014.
SciDAC: Accelerating Scientific Discovery, Transforming Computational Science [link]Paper   link   bibtex   2 downloads  
Unsupervised Cell Identification on Multidimensional X-ray Fluorescence Datasets. Wang, S.; Ward, J.; Leyffer, S.; Vogt, S.; Wild, S. M.; and Jacobsen, C. Journal of Synchrotron Radiation, 21(3): 568–579. 2014.
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Improving Parallel I/O Autotuning with Performance Modeling. Behzad, B.; Byna, S.; Wild, S. M.; Prabhat; and Snir, M. In Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing (HPDC14), pages 253–256, 2014. ACM
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Applied Mathematics Research for Exascale Computing. Dongarra, J.; Hittinger, J.; Bell, J.; Chacón, L.; Falgout, R.; Heroux, M.; Hovland, P. D.; Ng, E. G.; Webster, C.; and Wild, S. M. Technical Report U.S.\ Department of Energy, ASCR, 2014.
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Non-Negative Matrix Analysis for Effective Feature Extraction in X-ray Spectromicroscopy. Mak, R.; Lerotic, M.; Fleckenstein, H.; Vogt, S.; Wild, S. M.; Leyffer, S.; Sheynkin, Y.; and Jacobsen, C. Faraday Discussions, 171: 357–371. 2014.
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Multi-Objective Optimization of HPC Kernels for Performance, Power, and Energy. Balaprakash, P.; Tiwari, A.; and Wild, S. M. In Jarvis, S. A.; Wright, S. A.; and Hammond, S. D., editor(s), High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation, volume 8551, pages 239–260, 2014. Springer
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Nuclear Energy Density Optimization: Shell Structure. Kortelainen, M.; McDonnell, J. D.; Nazarewicz, W.; Olsen, E.; Reinhard, P.; Sarich, J.; Schunck, N.; Wild, S. M.; Davesne, D.; Erler, J.; and Pastore, A. Physical Review C, 89(5): 054314. 2014.
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Do You Trust Derivatives or Differences?. Moré, J. J.; and Wild, S. M. Journal of Computational Physics, 273: 268–277. 2014.
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  2013 (12)
Framework for Optimizing Power, Energy, and Performance (Poster). Balaprakash, P.; Tiwari, A.; and Wild, S. M. In International Conference for High Performance Computing, Networking, Storage, and Analysis (SC13), 2013.
Framework for Optimizing Power, Energy, and Performance (Poster) [link]Paper   link   bibtex   1 download  
Unsupervised Cell Identification on Multidimensional X-ray Fluorescence Datasets. Wang, S.; Ward, J.; Leyffer, S.; Wild, S. M.; Jacobsen, C.; and Vogt, S. In ACM SIGGRAPH 2013 Posters, pages 88:1, 2013.
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Active-Learning-Based Surrogate Models for Empirical Performance Tuning. Balaprakash, P.; Gramacy, R. B.; and Wild, S. M. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2013), pages 1–8, 2013.
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An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning. Balaprakash, P.; Wild, S. M.; and Hovland, P. D. In High Performance Computing for Computational Science - VECPAR 2012, 10th International Conference, Kobe, Japan, July 17-20, 2012, Revised Selected Papers, volume 7851, of Lecture Notes in Computer Science, pages 261–269, 2013. Springer
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Axially Deformed Solution of the Skyrme-Hartree-Fock-Bogolyubov Equations Using the Transformed Harmonic Oscillator Basis (II) HFBTHO v2.00d: A New Version of the Program. Stoitsov, M. V.; Schunck, N.; Kortelainen, M.; Michel, N.; Nam, H.; Olsen, E.; Sarich, J.; and Wild, S. M. Computer Physics Communications, 184(6): 1592–1604. 2013.
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Non-Intrusive Termination of Noisy Optimization. Larson, J.; and Wild, S. M. Optimization Methods and Software, 28(5): 993–1011. 2013.
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Computational Nuclear Quantum Many-Body Problem: The UNEDF Project. Bogner, S.; Bulgac, A.; Carlson, J.; Enge, J.; Fann, G.; Furnstahl, R. J.; Gandolfi, S.; Hagen, G.; Horoi, M.; Johnson, C.; Kortelainen, M.; Lusk, E.; Maris, P.; Nam, H.; Navratil, P.; Nazarewicz, W.; Ng, E. G.; Nobre, G. P. A.; Ormand, E.; Papenbrock, T.; Pei, J. C.; Pieper, S. C.; Quaglioni, S.; Roche, K. J.; Sarich, J.; Schunck, N.; Sosonkina, M.; Terasaki, J.; Thompson, I.; Vary, J. P.; and Wild, S. M. Computer Physics Communications, 184(10): 2235–2250. 2013.
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Development of a Fast, Robust Numerical Tool for the Design, Optimization, and Control of IC Engines. Aithal, S. M.; and Wild, S. M. In Proceedings of the SAE 11th International Conference on Engines & Vehicles, 2013.
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Optimized Chiral Nucleon-Nucleon Interaction at Next-to-Next-to-Leading Order. Ekström, A.; Baardsen, G.; Forssén, C.; Hagen, G.; Hjorth-Jensen, M.; Jansen, G. R.; Machleidt, R.; Nazarewicz, W.; Papenbrock, T.; Sarich, J.; and Wild, S. M. Physical Review Letters, 110(19): 192502. 2013.
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Novel Process to Use Vehicle Simulations Directly as Inputs to the VOLPE Model. Moawad, A.; Halbach, S.; Pagerit, S.; Rousseau, A.; Balaprakash, P.; and Wild, S. M. Technical Report ANL/ESD-13/13, Argonne National Laboratory, 2013.
Novel Process to Use Vehicle Simulations Directly as Inputs to the VOLPE Model [pdf]Paper   link   bibtex  
Variable Selection and Sensitivity Analysis via Dynamic Trees with an Application to Computer Code Performance Tuning. Gramacy, R. B.; Taddy, M. A.; and Wild, S. M. Annals of Applied Statistics, 7(1): 51–80. 2013.
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Global Convergence of Radial Basis Function Trust-Region Algorithms for Derivative-Free Optimization. Wild, S. M.; and Shoemaker, C. A. SIAM Review, 55(2): 349–371. 2013.
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  2012 (10)
SPAPT: Search Problems in Automatic Performance Tuning. Balaprakash, P.; Wild, S. M.; and Norris, B. Procedia Computer Science (ICCS 2012), 9: 1959–1968. 2012.
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Quality Input for Microscopic Fission Theory. Nazarewicz, W.; Schunck, N.; and Wild, S. M. Stockpile Stewardship Quarterly, 2(1). 2012.
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Estimating Derivatives of Noisy Simulations. Moré, J. J.; and Wild, S. M. ACM Transactions on Mathematical Software, 38(3): 19:1–19:21. 2012.
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Occupation Number-based Energy Functional for Nuclear Masses. Bertolli, M. G.; Papenbrock, T.; and Wild, S. M. Physical Review C, 85(1): 014322. 2012.
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Report on the Workshop on Extreme-Scale Solvers: Transition to Future Architectures. Ang, J.; Evans, K.; Geist, A.; Heroux, M.; Hovland, P. D.; Marques, O.; Curfman McInnes, L.; Ng, E. G.; and Wild, S. M. Technical Report U.S.\ Department of Energy, ASCR, 2012.
Report on the Workshop on Extreme-Scale Solvers: Transition to Future Architectures [pdf]Paper   link   bibtex   1 download  
The TAO Linearly Constrained Augmented Lagrangian Method for PDE-Constrained Optimization. Gawlik, E.; Munson, T.; Sarich, J.; and Wild, S. M. Technical Report ANL/MCS-P2003-0112, Argonne National Laboratory, MCS Division, 2012.
The TAO Linearly Constrained Augmented Lagrangian Method for PDE-Constrained Optimization [pdf]Paper   link   bibtex   1 download  
Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems. Kannan, A.; and Wild, S. M. In Proceedings of the XIX International Conference on Computational Methods in Water Resources (CMWR 2012), 2012.
Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems [pdf]2   Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems [pdf]Paper   link   bibtex   3 downloads  
TAO 2.0 Users Manual. Munson, T.; Sarich, J.; Wild, S. M.; Benson, S.; and Curfman McInnes, L. Technical Report ANL/MCS-TM-322, Argonne National Laboratory, 2012.
TAO 2.0 Users Manual [pdf]Paper   link   bibtex   1 download  
Nuclear Energy Density Optimization: Large Deformations. Kortelainen, M.; McDonnell, J. D.; Nazarewicz, W.; Reinhard, P.; Sarich, J.; Schunck, N.; Stoitsov, M. V.; and Wild, S. M. Physical Review C, 85: 024304. 2012.
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UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem. Nam, H.; Stoitsov, M. V.; Nazarewicz, W.; Bulgac, A.; Hagen, G.; Kortelainen, M.; Maris, P.; Pei, J. C.; Roche, K. J.; Schunck, N.; Thompson, I.; Vary, J. P.; and Wild, S. M. Journal of Physics: Conference Series, 402: 012033. 2012.
UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem [link]2   doi   link   bibtex  
  2011 (7)
Advancing Nuclear Physics Through TOPS Solvers and Tools. Ng, E. G.; Sarich, J.; Munson, T.; Wild, S. M.; Aktulga, H.; Yang, C.; Maris, P.; Vary, J. P.; Kortelainen, M.; Nazarewicz, W.; Papenbrock, T.; Schunck, N.; Stoitsov, M. V.; and Bertolli, M. G. In Proceedings of SciDAC 2011, 2011.
Advancing Nuclear Physics Through TOPS Solvers and Tools [pdf]Paper   link   bibtex  
Obtaining Quadratic Models of Noisy Functions. Kannan, A.; and Wild, S. M. Technical Report ANL/MCS-P1975-1111, Argonne National Laboratory, MCS Division, 2011.
Obtaining Quadratic Models of Noisy Functions [pdf]Paper   link   bibtex   3 downloads  
Can Search Algorithms Save Large-Scale Automatic Performance Tuning?. Balaprakash, P.; Wild, S. M.; and Hovland, P. D. Procedia Computer Science (ICCS 2011), 4: 2136–2145. 2011.
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UNEDF: Advanced Scientific Computing Transforms the Low-Energy Nuclear Many-Body Problem. Stoitsov, M. V.; Nam, H.; Nazarewicz, W.; Bulgac, A.; Hagen, G.; Kortelainen, M.; Pei, J. C.; Roche, K. J.; Schunck, N.; Thompson, I.; Vary, J. P.; and Wild, S. M. In Proceedings of SciDAC 2011, 2011.
UNEDF: Advanced Scientific Computing Transforms the Low-Energy Nuclear Many-Body Problem [pdf]Paper   link   bibtex  
Computing Heavy Elements. Schunck, N.; Baran, A.; Kortelainen, M.; McDonnell, J. D.; Moré, J. J.; Nazarewicz, W.; Pei, J. C.; Sarich, J.; Sheikh, J.; Staszczak, A.; Stoitsov, M. V.; and Wild, S. M. In Proceedings of SciDAC 2011, 2011.
Computing Heavy Elements [pdf]Paper   link   bibtex  
Estimating Computational Noise. Moré, J. J.; and Wild, S. M. SIAM Journal on Scientific Computing, 33(3): 1292–1314. 2011.
doi   link   bibtex   3 downloads  
Global Convergence of Radial Basis Function Trust Region Derivative-Free Algorithms. Wild, S. M.; and Shoemaker, C. A. SIAM Journal on Optimization, 21(3): 761–781. 2011.
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  2010 (1)
Nuclear Energy Density Optimization. Kortelainen, M.; Lesinski, T.; Moré, J. J.; Nazarewicz, W.; Sarich, J.; Schunck, N.; Stoitsov, M. V.; and Wild, S. M. Physical Review C, 82(2): 024313. 2010.
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  2009 (2)
Towards The Universal Nuclear Energy Density Functional. Stoitsov, M. V.; Moré, J. J.; Nazarewicz, W.; Pei, J. C.; Sarich, J.; Schunck, N.; Staszczak, A.; and Wild, S. M. Journal of Physics: Conference Series, 180: 012082. 2009.
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Benchmarking Derivative-Free Optimization Algorithms. Moré, J. J.; and Wild, S. M. SIAM Journal on Optimization, 20(1): 172–191. 2009.
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  2008 (4)
MNH: A Derivative-Free Optimization Algorithm Using Minimal Norm Hessians. Wild, S. M. In Tenth Copper Mountain Conference on Iterative Methods, 2008.
MNH: A Derivative-Free Optimization Algorithm Using Minimal Norm Hessians [pdf]Paper   link   bibtex  
Derivative-Free Optimization Algorithms for Computationally Expensive Functions. Wild, S. M. Ph.D. Thesis, Cornell University, 2008.
Derivative-Free Optimization Algorithms for Computationally Expensive Functions [link]Paper   link   bibtex   21 downloads  
Bayesian Calibration of Computationally Expensive Models Using Optimization and Radial Basis Function Approximation. Bliznyuk, N.; Ruppert, D.; Shoemaker, C. A.; Regis, R. G.; Wild, S. M.; and Mugunthan, P. Journal of Computational and Graphical Statistics, 17(2): 270–294. 2008.
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ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions. Wild, S. M.; Regis, R. G.; and Shoemaker, C. A. SIAM Journal on Scientific Computing, 30(6): 3197–3219. 2008.
doi   link   bibtex   2 downloads  
  2007 (1)
Maximizing Influence in a Competitive Social Network: A Follower's Perspective. Carnes, T.; Nagarajan, C.; Wild, S. M.; and van Zuylen, A. In ICEC '07: Proceedings of the Ninth International Conference on Electronic Commerce, pages 351–360, 2007. ACM
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  2004 (1)
Improving Non-Negative Matrix Factorizations Through Structured Initialization. Wild, S. M.; Curry, J. H.; and Dougherty, A. Pattern Recognition, 37(11): 2217–2232. 2004.
doi   link   bibtex   1 download  
  2003 (2)
Seeding Non-Negative Matrix Factorizations with the Spherical K-Means Clustering. Wild, S. M. Master's thesis, University of Colorado, 2003.
Seeding Non-Negative Matrix Factorizations with the Spherical K-Means Clustering [link]Paper   link   bibtex   2 downloads  
Motivating Non-Negative Matrix Factorizations. Wild, S. M.; Curry, J. H.; and Dougherty, A. In Proceedings of the Eighth SIAM Conference on Applied Linear Algebra, 2003.
Motivating Non-Negative Matrix Factorizations [pdf]Paper   link   bibtex   2 downloads  
  2002 (1)
Probabilistically Optimized Airline Overbooking Strategies, or ``Anyone Willing to Take a Later Flight?!". Leder, K. Z.; Spagnolie, S. E.; and Wild, S. M. Journal of Undergraduate Mathematics and Its Applications, 23(3): 317–338. 2002.
Probabilistically Optimized Airline Overbooking Strategies, or ``Anyone Willing to Take a Later Flight?!" [link]Paper   link   bibtex   5 downloads