Online Estimation of the Koopman Operator Using Fourier Features
Abstract
Transfer operators offer linear representations and global, physically meaningful features of nonlinear dynamical systems. Discovering transfer operators, such as the Koopman operator, require careful crafted dictionaries of observables, acting on states of the dynamical system. This is ad hoc and requires the full dataset for evaluation. In this paper, we offer an optimization scheme to allow joint learning of the observables and Koopman operator with online data. Our results show we are able to reconstruct the evolution and represent the global features of complex dynamical systems.
Cite
Text
Salam et al. "Online Estimation of the Koopman Operator Using Fourier Features." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.Markdown
[Salam et al. "Online Estimation of the Koopman Operator Using Fourier Features." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.](https://mlanthology.org/l4dc/2023/salam2023l4dc-online/)BibTeX
@inproceedings{salam2023l4dc-online,
title = {{Online Estimation of the Koopman Operator Using Fourier Features}},
author = {Salam, Tahiya and Li, Alice Kate and Hsieh, M. Ani},
booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference},
year = {2023},
pages = {1271-1283},
volume = {211},
url = {https://mlanthology.org/l4dc/2023/salam2023l4dc-online/}
}