AI RESEARCH
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
arXiv CS.LG
•
ArXi:2605.07738v1 Announce Type: cross 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