Dictionary learning for Kernel EDMD
The paper extends dictionary learning for Kernel Extended Dynamic Mode Decomposition (kEDMD) to streamline finding useful kernels for approximating Koopman operators in nonlinear systems. It introduces a method that takes a weighted list of initial kernels and optimizes their parameters via gradient descent, allowing the system to automatically select and prune unimportant kernels based on their contribution to the approximation.