A statistically consistent Koopman identification method for nonlinear systems with inputs and general noise, based on deep state-space encoders and multiple-shooting prediction error minimization.
Takens, Detecting strange attractors in turbulence , in Dynamical Systems and Turbulence, Warwick LEARNING KOOPMAN MODELS FROM DATA UNDER GENERAL NOISE CONDITIONS 29 1980, D
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Learning Koopman Models From Data Under General Noise Conditions
A statistically consistent Koopman identification method for nonlinear systems with inputs and general noise, based on deep state-space encoders and multiple-shooting prediction error minimization.