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pro vyhledávání: '"Iacob, Lucian Cristian"'
Autor:
Bevanda, Petar, Driessen, Bas, Iacob, Lucian Cristian, Toth, Roland, Sosnowski, Stefan, Hirche, Sandra
Linearity of Koopman operators and simplicity of their estimators coupled with model-reduction capabilities has lead to their great popularity in applications for learning dynamical systems. While nonparametric Koopman operator learning in infinite-d
Externí odkaz:
http://arxiv.org/abs/2405.07312
The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-called observable functions. While there is an extensive theory on infin
Externí odkaz:
http://arxiv.org/abs/2301.06557
The Koopman framework proposes a linear representation of finite-dimensional nonlinear systems through a generally infinite-dimensional globally linear embedding. Originally, the Koopman formalism has been derived for autonomous systems. In applicati
Externí odkaz:
http://arxiv.org/abs/2207.12132
A popular technique used to obtain linear representations of nonlinear systems is the so-called Koopman approach, where the nonlinear dynamics are lifted to a (possibly infinite dimensional) linear space through nonlinear functions called observables
Externí odkaz:
http://arxiv.org/abs/2206.07534
The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep state-space encoders. Through this method, the usual drawback of needing to choose a dictionary of lifting functions a priori is circumvented. The enc
Externí odkaz:
http://arxiv.org/abs/2110.02583
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