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

arXiv:2403.18181v1 Announce Type: new
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 …

abstract analysis arxiv clustering compression cs.lg data hierarchical linear machine machine learning math.ds matrix prediction systems them type via