Physics-informed optimizing discrete loss framework for nonlinear computational mechanics using automatic differentiation

2026/08/02 by Jinlong Yang, Shaoyu Zhao, Liangteng Guo +4

paper · doi:10.1016/j.apm.2026.117235

published in Applied Mathematical Modelling 160, 117235 (Elsevier BV)

crossref created 2026/08/02 · crossref deposited 2026/08/07 · crossref indexed 2026/08/07 · crossref issued 2026/12/01 · crossref published 2026/12/01 · crossref published-print 2026/12/01

Citations

• Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method
• Non-linear mechanics of geometrically imperfect graphene origami-enabled auxetic metamaterial third-order beam structures
• Energy-based physics-informed neural network for frictionless contact problems under large deformation
• Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss
• NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs
• Tunable nonlinear bending behaviors of functionally graded graphene origami enabled auxetic metamaterial beams
• Optimizing a DIscrete Loss (ODIL) to solve forward and inverse problems for partial differential equations using machine learning tools
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Physics-Informed Neural Networks for Heat Transfer Problems
• Physics-informed machine learning
• Physics-informed neural networks (PINNs) for fluid mechanics: A review
• Physics-informed neural networks (PINNs) for fluid mechanics: a review
• Parallel Physics-Informed Neural Networks via Domain Decomposition
• Differential Quadrature Method in Computational Mechanics: A Review