Learning Koopman Eigenfunctions and Invariant Subspaces from Data: Symmetric Subspace Decomposition

TL;DR

This paper introduces data-driven algorithms, including SSD and SSSD, to identify Koopman eigenfunctions and invariant subspaces from data, with provable guarantees and online capabilities.

Contribution

It presents the Symmetric Subspace Decomposition (SSD) and Streaming SSD algorithms for identifying Koopman eigenfunctions and invariant subspaces, including online and approximation extensions.

Findings

SSD provably finds maximal Koopman-invariant subspace

SSSD enables online data assimilation with limited memory

Extensions handle insufficient dictionaries for eigenfunction approximation

Abstract

This paper develops data-driven methods to identify eigenfunctions of the Koopman operator associated to a dynamical system and subspaces that are invariant under the operator. We build on Extended Dynamic Mode Decomposition (EDMD), a data-driven method that finds a finite-dimensional approximation of the Koopman operator on the span of a predefined dictionary of functions. We propose a necessary and sufficient condition to identify Koopman eigenfunctions based on the application of EDMD forward and backward in time. Moreover, we propose the Symmetric Subspace Decomposition (SSD) algorithm, an iterative method which provably identifies the maximal Koopman-invariant subspace and the Koopman eigenfunctions in the span of the dictionary. We also introduce the Streaming Symmetric Subspace Decomposition (SSSD) algorithm, an online extension of SSD that only requires a small, fixed memory and incorporates new data as is received. Finally, we propose an extension of SSD that approximates Koopman eigenfunctions and invariant subspaces when the dictionary does not contain sufficient informative eigenfunctions.