A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how to incorporate the momentum balance and constitutive relations into PINN, and explore in detail the application to linear e...

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Vydáno v:	Computer methods in applied mechanics and engineering Ročník 379; s. 113741
Hlavní autoři:	Haghighat, Ehsan, Raissi, Maziar, Moure, Adrian, Gomez, Hector, Juanes, Ruben
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.06.2021 Elsevier BV
Témata:	Algorithms Artificial neural network Artificial neural networks Constitutive relationships Convergence Deep learning Elastoplasticity Finite element method Inversion Linear elasticity Machine learning Mathematical models Model testing Neural networks Parameters Physics Physics-informed deep learning Robustness (mathematics) Sensitivity analysis Solid mechanics Training Transfer learning Inversion Artificial neural network Physics-informed deep learning Elastoplasticity Transfer learning Linear elasticity
ISSN:	0045-7825, 1879-2138
On-line přístup:	Získat plný text
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