Controlling chaotic energy events in fluids with reinforcement learning
Extreme energy events arise spontaneously in turbulent flow, and often lead to catastrophic outcomes of the system and its surrounding environment.This work focuses on controlling extreme events in the canonical Kolmogorov flow, which is a sinusoidally-driven, two-dimensional turbulent flow that exhibits extreme energy dissipation events due to non-linear energy transfers at different scales.
Authors
- Steven L. Brunton (ORCID: https://orcid.org/0000-0002-6565-5118)
- Esther Lagemann (ORCID: https://orcid.org/0000-0002-3694-3129)
- Christian Lagemann (ORCID: https://orcid.org/0000-0003-1150-4987)
- Sajeda Mokbel
Institutions
- University of Washington (US)
- Dynamic Systems (United States) (US)
Publication Details
- Published
- 2025-06-16
- DOI
- https://doi.org/10.1145/3679240.3734669
- Primary Topic
- Advanced Thermodynamics and Statistical Mechanics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation