Koopman-based Control for Stochastic Systems: Application to Enhanced Sampling

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.

Authors

Feliks Nüske (ORCID: https://orcid.org/0000-0003-2444-7889)
Jan Heiland (ORCID: https://orcid.org/0000-0003-0228-8522)
Lei Guo (ORCID: https://orcid.org/0000-0003-1789-5202)

Institutions

Technische Universität Ilmenau (DE)
Max Planck Institute for Dynamics of Complex Technical Systems (DE)
Otto-von-Guericke-Universität Magdeburg (DE)

Publication Details

Journal: IFAC-PapersOnLine
Published: 2025-01-01
DOI: https://doi.org/10.1016/j.ifacol.2025.03.049
Primary Topic: Model Reduction and Neural Networks
Type: article
Field-Weighted Citation Impact: 0.00

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article

Koopman-based Control for Stochastic Systems: Application to Enhanced Sampling

Feliks Nüske, Jan Heiland, Lei Guo

Jan 1, 2025

IFAC-PapersOnLine

Model Reduction and Neural Networks

article

Koopman-based Control for Stochastic Systems: Application to Enhanced Sampling

Feliks Nüske, Jan Heiland, Lei Guo

January 1, 2025article en

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.

IFAC-PapersOnLineVol. 59(1)

Technische Universität Ilmenau (DE), Max Planck Institute for Dynamics of Complex Technical Systems (DE), Otto-von-Guericke-Universität Magdeburg (DE)

Openalex Percentile: Top 95%

Model Reduction and Neural Networks

DOI Open Access PDF OpenAlex

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Koopman-based Control for Stochastic Systems: Application to Enhanced Sampling — Feliks Nüske, Jan Heiland, et al. · IFAC-PapersOnLine (2025) | TGRS Research Map | TGRS