基于无监督归一化流的稀有事件高效采样
Efficient Rare Event Sampling with Unsupervised Normalising Flows
AI总结：
本文提出 FlowRES 框架，利用无监督归一化流生成高质量非局域蒙特卡洛提议以高效采样稀有事件，无需先验数据或集体变量，且能处理多路径系统。
摘要：
从物理学、生物学到地震学和经济学，无数系统的行为由亚稳态之间影响重大但极不可能发生的跃迁所决定，这类跃迁被称为稀有事件，研究它们对于理解和控制这些系统的性质至关重要。用于采样稀有事件的经典计算方法仍然效率极低，并且成为需要先验数据的增强采样器的瓶颈。在此，我们提出一种新颖的框架 FlowRES，它利用无监督归一化流神经网络，通过生成高质量的非局域蒙特卡洛提议来增强稀有事件的蒙特卡洛采样。我们通过采样布朗粒子在日益复杂的势能面上探索的平衡态和非平衡态系统的跃迁路径系综来验证 FlowRES。除了不再需要先验数据之外，FlowRES 相较于已有采样器还具有关键优势：无需定义集体变量，即使事件变得越来越不可能发生，其效率也保持恒定，并且能够处理状态之间存在多条路径的系统。
英文摘要：
From Physics and Biology to Seismology and Economics, the behaviour of countless systems is determined by impactful yet unlikely transitions between metastable states known as \emph{rare events}, the study of which is essential for understanding and controlling these systems' properties. Classical computational methods to sample rare events remain prohibitively inefficient, and are a bottleneck for enhanced samplers requiring prior data. Here, we introduce a novel framework, FlowRES that uses unsupervised normalising flow neural networks to enhance Monte Carlo sampling of rare events by generating high-quality nonlocal Monte Carlo proposals. We validate FlowRES by sampling the transition path ensembles of equilibrium and non-equilibrium systems of Brownian particles exploring increasingly complex potential surfaces. Beyond eliminating requirements for prior data, FlowRES features key advantages over established samplers: no collective variables need defining, its efficiency remains constant even as events become increasingly unlikely, and it can handle systems with multiple routes between states.
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