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Online Estimation of the Koopman Operator Using Fourier Features

2022-12-03 01:39:05
Tahiya Salam, Alice Kate Li, M. Ani Hsieh

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.

Abstract (translated)

URL

https://arxiv.org/abs/2212.01503

PDF

https://arxiv.org/pdf/2212.01503.pdf


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