2022/01/20 by Rajat Arora, Arora, Rajat, Pratik Kakkar +5
Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Force Microscopy Techniques and Applications #Fuel Cells and Related Materials #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci) #Model Reduction and Neural Networks
paper · pdf · doi:10.48550/arxiv.2201.08363
openalex publication_date 2022/01/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.