Learned Lifted Linearization Applied to Unstable Dynamic Systems Enabled by Koopman Direct Encoding

TL;DR

This paper introduces a Koopman lifting linearization technique that effectively models both stable and unstable nonlinear dynamical systems by learning observables through neural networks and applying direct encoding, overcoming limitations of traditional methods.

Contribution

It presents a novel Koopman-based linearization approach that separates stable and unstable dynamics, enabling accurate modeling of complex systems using learned observables and direct encoding.

Findings

Significantly outperforms existing DMD and data-driven methods.

Successfully models unstable systems with improved accuracy.

Demonstrates effectiveness on nonlinear dynamical systems.

Abstract

This paper presents a Koopman lifting linearization method that is applicable to nonlinear dynamical systems having both stable and unstable regions. It is known that DMD and other standard data-driven methods face a fundamental difficulty in constructing a Koopman model when applied to unstable systems. Here we solve the problem by incorporating knowledge about a nonlinear state equation with a learning method for finding an effective set of observables. In a lifted space, stable and unstable regions are separated into independent subspaces. Based on this property, we propose to find effective observables through neural net training where training data are separated into stable and unstable trajectories. The resultant learned observables are used for constructing a linear state transition matrix using method known as Direct Encoding, which transforms the nonlinear state equation to a state transition matrix through inner product computations with the observables. The proposed method shows a dramatic improvement over existing DMD and data-driven methods.