Koopman-based Control for Stochastic Systems: Application to Enhanced Sampling
Lei Guo, Jan Heiland, Feliks N\"uske
TL;DR
This paper introduces a data-driven Koopman generator approach for predicting and controlling stochastic systems, specifically to accelerate rare event simulations in metastable systems.
Contribution
It presents a novel conceptual framework and proof-of-principle for using Koopman generators to determine optimal control policies for stochastic systems.
Findings
Koopman-based methods can effectively predict stochastic system behavior.
Optimal control policies can be derived to accelerate rare event sampling.
The approach enhances simulation efficiency for metastable stochastic systems.
Abstract
We present a data-driven approach to use the Koopman generator for prediction and optimal control of control-affine stochastic systems. We provide a novel conceptual approach and a proof-of-principle for the determination of optimal control policies which accelerate the simulation of rare events in metastable stochastic systems.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Control Systems Optimization · Model Reduction and Neural Networks · Control Systems and Identification