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Found 6 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.
Koopman Kernel Regression
Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of interest, e. g., the state of an agent or the reward of a policy.
Towards Data-driven LQR with Koopmanizing Flows
To utilize it for efficient LTI control design, we learn a finite representation of the Koopman operator that is linear in controls while concurrently learning meaningful lifting coordinates.
Diffeomorphically Learning Stable Koopman Operators
System representations inspired by the infinite-dimensional Koopman operator (generator) are increasingly considered for predictive modeling.
Learning the Koopman Eigendecomposition: A Diffeomorphic Approach
We present a novel data-driven approach for learning linear representations of a class of stable nonlinear systems using Koopman eigenfunctions.
Koopman Operator Dynamical Models: Learning, Analysis and Control
The Koopman operator allows for handling nonlinear systems through a (globally) linear representation.
