A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs

arXiv:2403.11591v1 Announce Type: cross
Abstract: We present a physics-informed neural network (PINN) approach for the discovery of slow invariant manifolds (SIMs), for the most general class of fast/slow dynamical systems of ODEs. In contrast to other machine learning (ML) approaches that construct reduced order black box surrogate models using simple regression, and/or require a priori knowledge of the fast and slow variables, our approach, simultaneously decomposes the vector field into fast and slow components and provides a functional of the …

abstract approximation arxiv class contrast cs.lg cs.na discovery general machine machine learning math.ds math.na network neural network physics physics-informed pinn sims systems type