Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity

X Wang, ZY Yin - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
Physics-informed neural networks (PINNs) have recently prevailed as differentiable solvers
that unify forward and inverse analysis in the same formulation. However, PINNs have quite …

Neural network-augmented differentiable finite element method for boundary value problems

X Wang, ZY Yin, W Wu, HH Zhu - International Journal of Mechanical …, 2025 - Elsevier
Classical numerical methods such as finite element method (FEM) face limitations due to
their low efficiency when addressing large-scale problems. As a novel paradigm, the physics …

[HTML][HTML] Variational temporal convolutional networks for I-FENN thermoelasticity

DW Abueidda, ME Mobasher - Computer Methods in Applied Mechanics …, 2024 - Elsevier
Abstract Machine learning (ML) has been used to solve multiphysics problems like
thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high …

On the Gauss–Legendre quadrature rule of deep energy method for one-dimensional problems in solid mechanics

T Le-Duc, TN Vo, H Nguyen-Xuan, J Lee - Finite Elements in Analysis and …, 2024 - Elsevier
Deep energy method (DEM) has shown its successes to solve several problems in solid
mechanics recently. It is known that determining proper integration scheme to precisely …

Physics-Encoded Finite Element Network to Handle Concentration Features and Multi-Material Heterogeneity in Hyperelasticity

X Wang, ZY Yin - Available at SSRN 4841554 - papers.ssrn.com
Physics-informed neural networks (PINNs) have recently prevailed as a differentiable solver
in solid mechanics. However, PINNs have quite limited caliber when dealing with …