Control-aware echo state networks (Ca-ESN) for the suppression of extreme events
This addresses the suppression of extreme events in chaotic systems, which can have adverse consequences in various scientific domains, representing a domain-specific incremental improvement.
The authors tackled the problem of suppressing extreme events in chaotic nonlinear systems by introducing control-aware echo state networks (Ca-ESN), which combine ESNs with control strategies like PID and MPC, resulting in a reduction of extreme events by two orders of magnitude compared to traditional methods.
Extreme event are sudden large-amplitude changes in the state or observables of chaotic nonlinear systems, which characterize many scientific phenomena. Because of their violent nature, extreme events typically have adverse consequences, which call for methods to prevent the events from happening. In this work, we introduce the control-aware echo state network (Ca-ESN) to seamlessly combine ESNs and control strategies, such as proportional-integral-derivative and model predictive control, to suppress extreme events. The methodology is showcased on a chaotic-turbulent flow, in which we reduce the occurrence of extreme events with respect to traditional methods by two orders of magnitude. This works opens up new possibilities for the efficient control of nonlinear systems with neural networks.