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Found 5 papers, 2 papers with code
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.
