Suppressing Modulation Instability with Reinforcement Learning
Modulation instability is a phenomenon of spontaneous pattern formation in nonlinear media, oftentimes leading to an unpredictable behaviour and a degradation of a signal of interest. We propose an approach based on reinforcement learning to suppress the unstable modes by optimizing the parameters for the time modulation of the potential in the nonlinear system. We test our approach in 1D and 2D cases and propose a new class of physically-meaningful reward functions to guarantee tamed instability.
PDF AbstractCode
Spaces
Tasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
