Deep learning for Koopman Operator Optimal Control

SciPy 1.0: fundamental algorithms for scientific computing in Python

(2020) Pauli Virtanen et al.   NATURE METHODS

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control

(2020) Stefan Klus et al.   PHYSICA D-NONLINEAR PHENOMENA

Optimal Construction of Koopman Eigenfunctions for Prediction and Control

(2020) Milan Korda et al.   IEEE TRANSACTIONS ON AUTOMATIC CONTROL

Linearly Recurrent Autoencoder Networks for Learning Dynamics

(2019) Samuel E. Otto et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Koopman operator-based model reduction for switched-system control of PDEs

(2019) Sebastian Peitz et al.   AUTOMATICA

Active Learning of Dynamics for Data-Driven Control Using Koopman Operators

(2019) Ian Abraham et al.   IEEE Transactions on Robotics

Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control

(2018) Milan Korda et al.   AUTOMATICA

Deep learning for universal linear embeddings of nonlinear dynamics

(2018) Bethany Lusch et al.   Nature Communications

Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator

(2017) Hassan Arbabi et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Chaos as an intermittently forced linear system

(2017) Steven L. Brunton et al.   Nature Communications

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control

(2016) Steven L. Brunton et al.   PLoS One