most citedMixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains

97 citations · 98 across the 2 of their papers we have counts for

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cs.CE2023★ 1 cited

Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN

Shahed Rezaei, Ahmad Moeineddin, Ali Harandi

We applied physics-informed neural networks to solve the constitutive relations for nonlinear, path-dependent material behavior. As a result, the trained network not only satisfies…

cs.CE2023★ 97 cited

Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains

Ali Harandi, Ahmad Moeineddin, Michael Kaliske +2

Physics-informed neural networks (PINNs) are a new tool for solving boundary value problems by defining loss functions of neural networks based on governing equations, boundary con…