Search Results for author:
Found 4 papers, 0 papers with code
Learning Invariant Subspaces of Koopman Operators--Part 2: Heterogeneous Dictionary Mixing to Approximate Subspace Invariance
The SILL dictionary's nonlinear functions are homogeneous, a norm in data-driven dictionary learning of Koopman operators.
Learning Invariant Subspaces of Koopman Operators--Part 1: A Methodology for Demonstrating a Dictionary's Approximate Subspace Invariance
Recently, deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD).
Heterogeneous mixtures of dictionary functions to approximate subspace invariance in Koopman operators
We discover a novel class of dictionary functions to approximate Koopman observables.
A Class of Logistic Functions for Approximating State-Inclusive Koopman Operators
An outstanding challenge in nonlinear systems theory is identification or learning of a given nonlinear system's Koopman operator directly from data or models.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
