arXiv Machine Learning By Tatsuya Naoi, Jun Ohkubo

Learning Koopman operators for coupled systems via information on governing equations of subsystems

Read the original on arXiv Machine Learning →

arXiv:2605. 01835v2 Announce Type: replace Abstract: Nonlinear coupled systems are ubiquitous in science and engineering.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 7

Nonparametric Control Koopman Operators

arXiv:2405. 07312v5 Announce Type: replace-cross Abstract: This paper presents a novel Koopman composition operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictionary or input parametrizations.

By Petar Bevanda, Bas Driessen, Lucian Cristian Iacob, Stefan Sosnowski, Roland T\'oth, Sandra Hirche