Attention-based multi-fidelity machine learning model for computational fractional flow reserve assessment. Yang, H., Figueroa, C A., & Garikipati, K. arXiv preprint arXiv:2311.11397, 2023. ![Attention-based multi-fidelity machine learning model for computational fractional flow reserve assessment [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper bibtex 2 downloads @article{yang2023attention,
title = {Attention-based multi-fidelity machine learning model for computational fractional flow reserve assessment},
url = {http://arxiv.org/abs/2311.11397v1},
journal = {arXiv preprint arXiv:2311.11397},
author = {Yang, Haizhou and Figueroa, C Alberto and Garikipati, Krishna},
year = {2023},
}
Downloads: 2
{"_id":"JTRdwKHPgsyuBoMHh","bibbaseid":"yang-figueroa-garikipati-attentionbasedmultifidelitymachinelearningmodelforcomputationalfractionalflowreserveassessment-2023","author_short":["Yang, H.","Figueroa, C A.","Garikipati, K."],"bibdata":{"bibtype":"article","type":"article","title":"Attention-based multi-fidelity machine learning model for computational fractional flow reserve assessment","url":"http://arxiv.org/abs/2311.11397v1","journal":"arXiv preprint arXiv:2311.11397","author":[{"propositions":[],"lastnames":["Yang"],"firstnames":["Haizhou"],"suffixes":[]},{"propositions":[],"lastnames":["Figueroa"],"firstnames":["C","Alberto"],"suffixes":[]},{"propositions":[],"lastnames":["Garikipati"],"firstnames":["Krishna"],"suffixes":[]}],"year":"2023","bibtex":"@article{yang2023attention,\n\ttitle = {Attention-based multi-fidelity machine learning model for computational fractional flow reserve assessment},\n\turl = {http://arxiv.org/abs/2311.11397v1},\n\tjournal = {arXiv preprint arXiv:2311.11397},\n\tauthor = {Yang, Haizhou and Figueroa, C Alberto and Garikipati, Krishna},\n\tyear = {2023},\n}\n\n\n\n","author_short":["Yang, H.","Figueroa, C A.","Garikipati, K."],"key":"yang2023attention","id":"yang2023attention","bibbaseid":"yang-figueroa-garikipati-attentionbasedmultifidelitymachinelearningmodelforcomputationalfractionalflowreserveassessment-2023","role":"author","urls":{"Paper":"http://arxiv.org/abs/2311.11397v1"},"metadata":{"authorlinks":{}},"downloads":2},"bibtype":"article","biburl":"https://bibbase.org/zotero-group/compphys/5789834","dataSources":["pnuidRXbC87ribcT7","9ySafXXXhx8rxqW8v","kJD9wd56WxCQ38tRA"],"keywords":[],"search_terms":["attention","based","multi","fidelity","machine","learning","model","computational","fractional","flow","reserve","assessment","yang","figueroa","garikipati"],"title":"Attention-based multi-fidelity machine learning model for computational fractional flow reserve assessment","year":2023,"downloads":2}