机器学习回归不确定性揭示自驱动活性粒子的群集相变

Reveal flocking phase transition of self-propelled active particles by machine learning regression uncertainty

本文发展了一种利用逆统计问题中的回归不确定性来自动探索物质相的新方法.以自驱动活性粒子的群集相变为例,展示了对于这一类涉及非平衡、非晶格、一阶相变等复杂要素的多体系统,在训练人工神经网络处理其中的逆统计问题回归任务,成功重构出系统的噪声强度这一参数之后,回归结果的不确定性关于实际噪声强度的分布具有非平庸的规律性,可用于揭示该系统中的群集相变,并自动提取相变的临界噪声强度.本文还与两种基于神经网络分类能力的常见方法进行直接对比,讨论了它们的异同和各自特点.结果表明,本文发展的新方法不仅具有使用效率较高和所需预设的物理知识较少等实用优势,而且更有在理论层面较为自然地与传统物理概念建立联系的可能性,对于跨领域的不同物理系统都有良好的通用性和有效性.

We develop the neural network based“learning from regression uncertainty”approach for the automatic detection of phases of matter in nonequilibrium active systems.Taking the flocking phase transition of selfpropelled active particles described by the Vicsek model for example,we find that after training a neural network for solving the inverse statistical problem,i.e.for performing the regression task of reconstructing the noise level from given samples of such a nonequilibrium many-body complex system’s steady state configurations,the uncertainty of regression results obtained by the well-trained network can actually be utilized to reveal possible phase transitions in the system under study.The noise level dependence of regression uncertainty is assumed to be in a non-trivial M-shape,and its valley appears at the critical point of the flocking phase transition.By directly comparing this regression-based approach with the widely-used classification-based“learning by confusion”and“learning with blanking”approaches,we show that our approach has practical effectiveness,efficiency,good generality for various physical systems across interdisciplinary fields,and a greater possibility of being interpretable via conventional notions of physics.These approaches can complement each other to serve as a promising generic toolbox for investigating rich critical phenomena and providing datadriven evidence on the existence of various phase transitions,especially for those complex scenarios associated with first-order phase transitions or nonequilibrium active systems where traditional research methods in physics could face difficulties.