Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simulations. Its practical use, however, is often limited by the high cost of loss evaluation. We address this bottleneck by combining the Equilibrium Neural Operator (EquiNO) with the QR-based discrete empirical interpolation method (Q-DEIM). EquiNO learns only the modal coefficients of reduced displacement-fluctuation and first Piola-Kirchhoff stress representations built from periodic and divergence-free bases, thereby enforcing periodicity and mechanical equilibrium by construction. Q-DEIM then identifies a small set of spatial points through...
| Authors | Hamidreza Eivazi, Henning Wessels |
|---|---|
| Primary category | physics.comp-ph |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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