Model reduction for nonlinearizable dynamics via delay-embedded spectral submanifolds

TL;DR

This paper proves that delay embedding allows for the prediction of invariant manifold tangent spaces in dynamical systems solely based on eigenvalues, enhancing data-driven model reduction techniques.

Contribution

It provides a theoretical foundation linking delay-embedded eigenvectors to eigenvalues, enabling a priori prediction of invariant manifolds in nonlinear dynamics.

Findings

Eigenvectors of delay-embedded linearized systems depend only on eigenvalues.

Delay embedding can predict tangent spaces of invariant manifolds.

Application to datasets reveals multimodal spectral submanifolds and their interactions.

Abstract

Delay embedding is a commonly employed technique in a wide range of data-driven model reduction methods for dynamical systems, including the Dynamic mode decomposition (DMD), the Hankel alternative view of the Koopman decomposition (HAVOK), nearest-neighbor predictions and the reduction to spectral submanifolds (SSMs). In developing these applications, multiple authors have observed that delay embedding appears to separate the data into modes, whose orientations depend only on the spectrum of the sampled system. In this work, we make this observation precise by proving that the eigenvectors of the delay-embedded linearized system at a fixed point are determined solely by the corresponding eigenvalues, even for multi-dimensional observables. This implies that the tangent space of a delay-embedded invariant manifold can be predicted a priori using an estimate of the eigenvalues. We apply our results to three datasets to identify multimodal SSMs and analyse their nonlinear modal interactions. While SSMs are the focus of our study, these results generalize to any delay-embedded invariant manifold tangent to a set of eigenvectors at a fixed point. Therefore, we expect this theory to be applicable to a number of data-driven model reduction methods.