Biological & Soft Matter Seminar: Stochastic and adaptive resetting: optimizing search strategies and the design of non-equilibrium steady-states

Abstract:

Stochastic resetting, the procedure of stopping and re-initializing random processes, has recently emerged as a powerful tool for accelerating processes ranging from queuing systems to molecular sim- ulations and for creating non-equilibrium steady-states. However, its usefulness is severely limited by assuming that the resetting protocol is completely decoupled from the state and age of the process that is being reset. In this talk, I will present a general formulation for state- and time-dependent resetting of stochastic processes, which we call adaptive resetting. This allows us to predict, using a single set of trajectories without resetting and via a simple reweighing procedure, all key observ- ables of processes with adaptive resetting. This formulation enables efficient exploration of informed search strategies and facilitates the prediction and design of complex non-equilibrium steady-states, eliminating the need for extensive brute-force sampling across different resetting protocols. Finally, I will present a general machine learning framework to optimize the adaptive resetting protocol for an arbitrary task beyond the current state of the art.