Keidar Tommer D, Blumer Ofir, Hirshberg Barak, Reuveni Shlomi
School of Chemistry, Tel Aviv University, Tel Aviv, 6997801, Israel.
The Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv, Israel.
Nat Commun. 2025 Aug 6;16(1):7259. doi: 10.1038/s41467-025-62398-2.
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 simulations. 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. We 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 observables of processes with adaptive resetting. These include the first-passage time distribution, the propagator, and the steady-state. Our 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, we develop a general machine learning framework to optimize the adaptive resetting protocol for an arbitrary task beyond the current state of the art. We use it to discover efficient protocols for accelerating molecular dynamics simulations.
随机重置，即停止并重新初始化随机过程的过程，最近已成为一种强大的工具，可用于加速从排队系统到分子模拟等各种过程。然而，其有效性受到严重限制，因为它假设重置协议与被重置过程的状态和时长完全解耦。我们提出了一种用于随机过程的状态和时间相关重置的通用公式，我们称之为自适应重置。这使我们能够通过一组无重置的轨迹，并通过简单的重新加权程序，预测具有自适应重置的过程的所有关键可观测量。这些包括首次通过时间分布、传播子和稳态。我们的公式能够有效地探索有信息的搜索策略，并有助于预测和设计复杂的非平衡稳态，无需在不同的重置协议上进行广泛的蛮力采样。最后，我们开发了一个通用的机器学习框架，以针对超出当前技术水平的任意任务优化自适应重置协议。我们用它来发现加速分子动力学模拟的有效协议。
