A hybrid physics-informed framework using polynomials and neural networks approximates invariant manifolds of discrete-time dynamical systems with nonlinear exosystems and shows higher accuracy than pure polynomial or neural approaches on bioreactor and car-following benchmarks.
Projecting to a slow manifold: Singularly perturbed systems and legacy codes
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Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks
A hybrid physics-informed framework using polynomials and neural networks approximates invariant manifolds of discrete-time dynamical systems with nonlinear exosystems and shows higher accuracy than pure polynomial or neural approaches on bioreactor and car-following benchmarks.