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Found 9 papers, 5 papers with code
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
PyKoopman is a Python package for the data-driven approximation of the Koopman operator associated with a dynamical system.
Modern Koopman Theory for Dynamical Systems
The field of dynamical systems is being transformed by the mathematical tools and algorithms emerging from modern computing and data science.
Deep reinforcement learning for optical systems: A case study of mode-locked lasers
We demonstrate that deep reinforcement learning (deep RL) provides a highly effective strategy for the control and self-tuning of optical systems.
Principal component trajectories for modeling spectrally-continuous dynamics as forced linear systems
Delay embeddings of time series data have emerged as a promising coordinate basis for data-driven estimation of the Koopman operator, which seeks a linear representation for observed nonlinear dynamics.
Computational Physics Systems and Control Systems and Control
Learning Discrepancy Models From Experimental Data
First principles modeling of physical systems has led to significant technological advances across all branches of science.
Discovering conservation laws from data for control
In this work, we formulate a data-driven architecture for discovering conserved quantities based on Koopman theory.
Optimized Sampling for Multiscale Dynamics
The multiresolution DMD is capable of characterizing nonlinear dynamical systems in an equation-free manner by recursively decomposing the state of the system into low-rank spatial modes and their temporal Fourier dynamics.
Dynamical Systems Numerical Analysis Data Analysis, Statistics and Probability
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
These factors limit the use of these techniques for the online identification of a model in the low-data limit, for example following an abrupt change to the system dynamics.
Optimization and Control Dynamical Systems Data Analysis, Statistics and Probability
Data-driven discovery of Koopman eigenfunctions for control
In this work, we demonstrate a data-driven control architecture, termed Koopman Reduced Order Nonlinear Identification and Control (KRONIC), that utilizes Koopman eigenfunctions to manipulate nonlinear systems using linear systems theory.
Optimization and Control Dynamical Systems
