フーリエ特徴量を用いたKoopman作用素のオンライン推定
Online Estimation of the Koopman Operator Using Fourier Features
フーリエ特徴量を活用し、観測関数とKoopman作用素をオンラインデータから同時学習する最適化手法を提案し、複雑な力学系の進化と大域的特徴を再現できることを示した。
著者: Tahiya Salam, Alice Kate Li, M. Ani Hsieh
分類: cs.RO, cs.LG
原文アブストラクト
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.
