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Found 3 papers, 2 papers with code
Nonparametric Control-Koopman Operator Learning: Flexible and Scalable Models for Prediction and Control
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.
Non-linear State-space Model Identification from Video Data using Deep Encoders
An encoder function, represented by a neural network, is introduced to learn a reconstructability map to estimate the model states from past inputs and outputs.
Nonlinear state-space identification using deep encoder networks
This paper introduces a method that approximates the simulation loss by splitting the data set into multiple independent sections similar to the multiple shooting method.
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