Compression of the Koopman matrix for nonlinear physical models via hierarchical clustering

Abstract

Machine learning methods allow the prediction of nonlinear dynamical systems from data alone. The Koopman operator is one of them, which enables us to employ linear analysis for nonlinear dynamical systems. The linear characteristics of the Koopman operator are hopeful to understand the nonlinear dynamics and perform rapid predictions. The extended dynamic mode decomposition (EDMD) is one of the methods to approximate the Koopman operator as a finite-dimensional matrix. In this work, we propose a method to compress the Koopman matrix using hierarchical clustering. We performed numerical experiments on cart-pole and rope models and compared the results with those of the conventional singular value decomposition (SVD); the results indicate that the hierarchical clustering performs better than the naive SVD compressions.