AI 中文总结
研究随机重置从简单模型发展成非平衡框架，回顾其更新理论，展示预测与推断方法，讨论在多方面的应用及相关进展，为控制随机动力学带来新机遇。
AI 中文摘要
随机重置已从扩散搜索加速的简单模型发展成为预测、推断和控制远离平衡的随机动力学的通用框架。其创建非平衡稳态和加速首次通过动力学的特性在物理化学中愈发重要。本文回顾了随机重置的更新理论，展示了如何根据基础过程的属性预测重置动力学，以及如何从重置加速的动力学中推断基础过程。接着讨论了其在状态制备、增强采样、动力学推断以及机器学习模型训练和采样方面的应用。最后回顾了自适应重置、环境反馈、多体动力学和重置的热力学成本等方面的最新进展。这些发展为跨理论、模拟和实验控制随机动力学提供了新机会。
英文摘要
Stochastic resetting has evolved from a simple model of diffusive search acceleration into a general framework for predicting, inferring, and controlling stochastic dynamics far from equilibrium. Its defining features, i.e., the creation of non-equilibrium steady states and the acceleration of first-passage kinetics, are increasingly relevant across physical chemistry, from biological restart mechanisms to molecular simulations and colloidal experiments. We review the renewal theory underlying stochastic resetting and show how it enables prediction of reset dynamics from properties of the underlying process, while also allowing the latter to be inferred from the resetting-accelerated dynamics. We then discuss applications to state preparation, enhanced sampling, kinetic inference, and training and sampling of machine learning models. Finally, we review recent advances in adaptive resetting, environmental feedback, many-body dynamics, and thermodynamic costs of resetting. These developments establish new opportunities for controlling stochastic dynamics with resetting across theory, simulations, and experiments.
