Learning physics-based reduced-order models from data using nonlinear manifolds
Rudy Geelen,
Laura Balzano,
Stephen Wright
et al.
Abstract:We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the data through a general representation learning problem. The proposed approach is driven by embeddings of low-order polynomial form. A projection onto the nonlinear manifold reveals the algebraic structure of the reduced-space system that governs the problem of interest. The matrix operators of the reduced-order model are then inferred… Show more
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