{"volume":71,"user_id":"47427","_id":"38031","publisher":"Springer ","status":"public","oa":"1","external_id":{"arxiv":["2301.08637"]},"citation":{"chicago":"Philipp, Friedrich, Manuel Schaller, Karl Worthmann, Sebastian Peitz, and Feliks Nüske. “Error Bounds for Kernel-Based Approximations of the Koopman Operator.” Applied and Computational Harmonic Analysis 71 (2024). https://doi.org/10.1016/j.acha.2024.101657.","ama":"Philipp F, Schaller M, Worthmann K, Peitz S, Nüske F. Error bounds for kernel-based approximations of the Koopman operator. Applied and Computational Harmonic Analysis . 2024;71. doi:10.1016/j.acha.2024.101657","short":"F. Philipp, M. Schaller, K. Worthmann, S. Peitz, F. Nüske, Applied and Computational Harmonic Analysis 71 (2024).","bibtex":"@article{Philipp_Schaller_Worthmann_Peitz_Nüske_2024, title={Error bounds for kernel-based approximations of the Koopman operator}, volume={71}, DOI={10.1016/j.acha.2024.101657}, number={101657}, journal={Applied and Computational Harmonic Analysis }, publisher={Springer }, author={Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl and Peitz, Sebastian and Nüske, Feliks}, year={2024} }","apa":"Philipp, F., Schaller, M., Worthmann, K., Peitz, S., & Nüske, F. (2024). Error bounds for kernel-based approximations of the Koopman operator. Applied and Computational Harmonic Analysis , 71, Article 101657. https://doi.org/10.1016/j.acha.2024.101657","mla":"Philipp, Friedrich, et al. “Error Bounds for Kernel-Based Approximations of the Koopman Operator.” Applied and Computational Harmonic Analysis , vol. 71, 101657, Springer , 2024, doi:10.1016/j.acha.2024.101657.","ieee":"F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error bounds for kernel-based approximations of the Koopman operator,” Applied and Computational Harmonic Analysis , vol. 71, Art. no. 101657, 2024, doi: 10.1016/j.acha.2024.101657."},"doi":"10.1016/j.acha.2024.101657","language":[{"iso":"eng"}],"article_number":"101657","main_file_link":[{"url":"https://arxiv.org/pdf/2301.08637","open_access":"1"}],"intvolume":" 71","publication_status":"published","date_updated":"2024-04-11T12:41:13Z","author":[{"last_name":"Philipp","first_name":"Friedrich","full_name":"Philipp, Friedrich"},{"first_name":"Manuel","last_name":"Schaller","full_name":"Schaller, Manuel"},{"full_name":"Worthmann, Karl","last_name":"Worthmann","first_name":"Karl"},{"id":"47427","full_name":"Peitz, Sebastian","last_name":"Peitz","orcid":"0000-0002-3389-793X","first_name":"Sebastian"},{"full_name":"Nüske, Feliks","first_name":"Feliks","last_name":"Nüske"}],"year":"2024","title":"Error bounds for kernel-based approximations of the Koopman operator","department":[{"_id":"655"}],"type":"journal_article","date_created":"2023-01-23T07:03:39Z","abstract":[{"text":"We consider the data-driven approximation of the Koopman operator for\r\nstochastic differential equations on reproducing kernel Hilbert spaces (RKHS).\r\nOur focus is on the estimation error if the data are collected from long-term\r\nergodic simulations. We derive both an exact expression for the variance of the\r\nkernel cross-covariance operator, measured in the Hilbert-Schmidt norm, and\r\nprobabilistic bounds for the finite-data estimation error. Moreover, we derive\r\na bound on the prediction error of observables in the RKHS using a finite\r\nMercer series expansion. Further, assuming Koopman-invariance of the RKHS, we\r\nprovide bounds on the full approximation error. Numerical experiments using the\r\nOrnstein-Uhlenbeck process illustrate our results.","lang":"eng"}],"publication":"Applied and Computational Harmonic Analysis "}