Physics-informed neural networks for modeling rate- and temperature-dependent plasticity
This addresses the problem of simulating complex material behavior for researchers in computational mechanics, but it is incremental as it builds on existing PINN methods with specific improvements.
The paper tackles modeling strain-rate and temperature-dependent plasticity in elastic-viscoplastic solids using a physics-informed neural network (PINN) framework, achieving accurate predictions of spatio-temporal deformation evolution in test problems.
This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic solids. To avoid unbalanced back-propagated gradients during training, the proposed framework uses a simple strategy with no added computational complexity for selecting scalar weights that balance the interplay between different terms in the physics-based loss function. In addition, we highlight a fundamental challenge involving the selection of appropriate model outputs so that the mechanical problem can be faithfully solved using a PINN-based approach. We demonstrate the effectiveness of this approach by studying two test problems modeling the elastic-viscoplastic deformation in solids at different strain rates and temperatures, respectively. Our results show that the proposed PINN-based approach can accurately predict the spatio-temporal evolution of deformation in elastic-viscoplastic materials.