NN-OpInf: an operator inference approach using structure-preserving composable neural networks

ArXi:2603.08488v1 Announce Type: new We propose neural network operator inference (NN-OpInf): a structure-preserving, composable, and minimally restrictive operator inference framework for the non-intrusive reduced-order modeling of dynamical systems. The approach learns latent dynamics from snapshot data, enforcing local operator structure such as skew-symmetry, (semi-)positive definiteness, and gradient preservation, while also reflecting complex dynamics by ing additive compositions of heterogeneous operators. We present practical.