A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity. (arXiv:2204.12088v1 [cs.LG])

In this work, we present a deep neural network architecture that can
efficiently approximate classical elasto-plastic constitutive relations. The
network is enriched with crucial physics aspects of classical
elasto-plasticity, including additive decomposition of strains into elastic and
plastic parts, and nonlinear incremental elasticity. This leads to a
Physics-Informed Neural Network (PINN) surrogate model named here as
Elasto-Plastic Neural Network (EPNN). Detailed analyses show that embedding
these physics into the architecture of the neural network facilitates a more
efficient training of …

arxiv deep neural network modeling network neural network physics