How unconstrained machine-learning models learn physical symmetries

ArXi:2603.24638v1 Announce Type: new The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations. In many cases, models are built using constrained mathematical forms that ensure that symmetries are enforced exactly.