Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks

arXiv:2308.15640v3 Announce Type: replace-cross
Abstract: Identifying constitutive parameters in engineering and biological materials, particularly those with intricate geometries and mechanical behaviors, remains a longstanding challenge. The recent advent of Physics-Informed Neural Networks (PINNs) offers promising solutions, but current frameworks are often limited to basic constitutive laws and encounter practical constraints when combined with experimental data. In this paper, we introduce a robust PINN-based framework designed to identify material parameters for soft materials, specifically those exhibiting complex constitutive behaviors, under large …

abstract arxiv basic challenge cond-mat.mtrl-sci cs.lg current engineering frameworks laws materials networks neural networks parameters physics physics-informed practical solutions type