Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics

2026-05-08

AI Summary

This work addresses the prohibitive computational cost of full-field loss evaluation in physics-informed operator learning for microstructure surrogate modeling. To overcome this challenge, the authors propose a novel framework that integrates Equivariant Neural Operators (EquiNO) with a QR-based Discrete Empirical Interpolation Method (Q-DEIM). By constructing reduced-order representations of displacement and stress fields using periodic, divergence-free basis functions and evaluating constitutive relations only at a small number of spatial points, the method—introducing Q-DEIM to this domain for the first time—dramatically reduces training expenses. It directly predicts homogenized stresses without reconstructing full fields and demonstrates strong interpolation and extrapolation capabilities even with very few training snapshots. Numerical experiments show a reduction of approximately three orders of magnitude in per-step training cost and acceleration of homogenization computations by factors of $10^3$–$10^4$ compared to full-field simulations, while accurately capturing both microscopic stress fields and macroscopic responses.

Abstract

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 a column-pivoted QR factorization of the stress basis and restricts constitutive evaluations during training to these points alone. This makes full-batch second-order optimization practical for three-dimensional representative volume elements (RVEs). Homogenized first Piola-Kirchhoff stresses are recovered directly from the offline-averaged reduced stress modes, without the need to reconstruct the full stress field at inference time. We validate the framework on two three-dimensional finite-strain hyperelastic RVEs. Q-DEIM reduces the per-step training cost by roughly three orders of magnitude relative to full-field loss evaluation, while reduced homogenization achieves speed-up factors of order $10^3$ to $10^4$ over direct full-field computations. Despite relying on only a small number of offline snapshot loading paths for basis construction, the method accurately interpolates and extrapolates both microscopic stress fields and homogenized stresses, with prediction quality improving systematically as more snapshots are added.

Problem

• physics-informed operator learning
• surrogate modeling
• computational cost
• hyperelasticity
• multiscale simulation

Innovation

• Physics-informed operator learning
• Reduced-order modeling
• Q-DEIM
• Equilibrium Neural Operator
• Hyperelasticity