研究论文
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Ehsan Haghighat, Maziar Raissi, Adrian Moure, Héctor Gómez, Rubén Juanes
Massachusetts Institute of Technology University of Colorado Boulder Purdue University West Lafayette
来源Computer Methods in Applied Mechanics and Engineering
年份2021
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学术脉络
学科主题
物理Model Reduction and Neural Networks
Advanced Numerical Analysis Techniques · Numerical methods in engineering
参考文献 63
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Isogeometric analysis toward integration of CAD and FEA
被引 2,388J. Austin Cottrell, Thomas J.R. Hughes, Yuri Bazilevs · 2009
Machine learning applications in genetics and genomics
被引 2,137Maxwell W. Libbrecht, William Stafford Noble · Nature Reviews Genetics · 2015
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引用本文 1,299
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
被引 2,658Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo · Journal of Scientific Computing · 2022
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
被引 882Laith Alzubaidi, Jinshuai Bai, Aiman Al-Sabaawi · Journal Of Big Data · 2023
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
被引 802Sifan Wang, Hanwen Wang, Paris Perdikaris · Science Advances · 2021
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