A mesh-based neural energy method for the simulation of heterogeneous composites
Modeling heterogeneous materials remains a challenge for physics-informed neural networks such as the deep energy method (DEM). The DEM and its variants, here collectively referred to as the neural energy method (NEM), offer a differentiable variational framework. However, their conventional collocation-based implementation (C-NEM) often suffers from physically inadmissible displacement oscillations, integration errors, and high computational costs from automatic differentiation. This work introduces the mesh-based neural energy method (M-NEM), extending the NEM through a mesh-based discretization of the displacement field. By interpolating nodal displacements via shape functions, the M-N...
| Authors | Pius J. M. Wichmann, Stefan Hildebrand, Sandra Klinge |
|---|---|
| Primary category | cs.CE |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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