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Physics-informed optimizing discrete loss framework for nonlinear computational mechanics using automatic differentiation
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DOI：10.1016/j.apm.2026.117235.png)
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Abstract
En 中文
• Optimizing discrete loss with traditional discretization is proposed to solve PDEs. • Adaptive-restart strategy improves optimizer robustness and convergence. • Accuracy is verified through canonical tests and bending of metamaterial beam. • Optimizing discrete loss achieves accurate and robust nonlinear PDE solutions.
Keywords:
Optimizing discrete loss
Functionally graded materials
Nonlinear bending
Graphene origami
Auxetic metamaterials
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