articleTop 1% cited
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Computer Methods in Applied Mechanics and Engineering · 2021 · Vol. 379 · pp. 113741–113741
Ehsan Haghighat(Massachusetts Institute of Technology)Maziar Raissi(University of Colorado Boulder)Adrian Moure(Purdue University West Lafayette)Héctor Gómez(Purdue University West Lafayette)Rubén Juanes✉(Massachusetts Institute of Technology)
Model Reduction and Neural NetworksAdvanced Numerical Analysis TechniquesNumerical methods in engineeringArtificial neural networkvon Mises yield criterionFinite element methodRobustness (evolution)Computer scienceNonlinear systemIsogeometric analysisSurrogate modelApplied mathematicsDeep learning
Funding
- King Fahd University of Petroleum and Minerals
Citations
1,050
FWCI
81.78
field-weighted impact
References
63
Percentile
100%
vs. same field & year
Citations per year
References
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Deep learning
Nature · 2015 · 79,164 citations
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