Dictionary learning for Kernel EDMD

TL;DR

This paper introduces a gradient-based kernel learning method for kernel EDMD, enabling automatic kernel parameter optimization and improved Koopman operator approximation for nonlinear dynamical systems.

Contribution

It extends dictionary learning to kernel learning in kEDMD, allowing for automatic kernel selection and parameter tuning via gradient optimization.

Findings

The method effectively learns kernels for Koopman operator approximation.

Unimportant kernels can be identified and removed based on learned weights.

Experiments on Duffing oscillator and Kuramoto-Sivashinsky PDE validate the approach.

Abstract

Studying nonlinear dynamical systems through their state space behavior can be challenging, and one possible alternative is to analyze them via their associated Koopman operator. This turns the nonlinear problem into a linear, infinite-dimensional one. To approximate the operator in finite dimensions, extended dynamic mode decomposition (EDMD) is a commonly used algorithm. It requires a finite list of functionals and a set of snapshots from the system to compute an approximation of the operator and its corresponding spectrum. Instead of choosing the list of functionals directly, it can be implicitly defined via kernels, a method known as kernel extended dynamic mode decomposition (kEDMD). However, one still needs to define the kernel and choose its parameter values. In this paper, we aim to streamline this process by extending dictionary learning for EDMD to kernel learning in kEDMD. By simplifying kEDMD we show how to perform gradient-based optimization over the learnable kernel parameters, and demonstrate that this method leads to useful kernels for the original kEDMD. The focus of our work is a method that takes a weighted list of kernels with randomly initialized values as input and outputs a list of kernels and parameter values suitable for approximating the Koopman operator of the underlying system. We demonstrate that unimportant kernels can be removed from the list by analyzing the weights in the weighted sum. We evaluate the method across several experiments, including the Duffing oscillator and the Kuramoto-Sivashinsky PDE, showcasing the method's different strengths.