2019/06/30 by Mohammad Farazmand, Themistoklis P. Sapsis · 1 citation
Economics, Econometrics and Finance · Environmental Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Attractor #Chaos control and synchronization #Chaotic #Complex Systems and Time Series Analysis #Computer science #Control (management) #Control theory (sociology) #Controller (irrigation) #Dynamical systems theory #Ecosystem dynamics and resilience #Event (particle physics) #Flow (mathematics) #Mathematics #Mechanics #Physics #Probabilistic logic #Turbulence #math.OC #nlin.CD #physics.flu-dyn
paper · pdf · doi:10.1103/physreve.100.033110
published as Phys. Rev. E 100, 033110 (2019) · In press in Phys. Rev. E. Includes minor revisions
arxiv created 2019/09/04 · openalex publication_date 2019/09/16 · arxiv updated 2019/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Extreme events that arise spontaneously in chaotic dynamical systems often have an adverse impact on the system or the surrounding environment. As such, their mitigation is highly desirable. Here, we introduce a control strategy for mitigating extreme events in a turbulent shear flow. The controller combines a probabilistic prediction of the extreme events with a deterministic actuator. The predictions are used to actuate the controller only when an extreme event is imminent. When actuated, the controller only acts on the degrees of freedom that are involved in the formation of the extreme events, exerting minimal interference with the flow dynamics. As a result, the attractors of the controlled and uncontrolled systems share the same chaotic core (containing the nonextreme events) and only differ in the tail of their distributions. We propose that such adaptive low-dimensional controllers should be used to mitigate extreme events in general chaotic dynamical systems, beyond the shear flow considered here.