用于推断非平衡系统中相边界的可解释机器学习
Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system
摘要：
机器学习是否能够超越复杂物理系统的黑盒建模仍在争论之中。我们研究学习算法的泛化性和可解释性。为此，我们使用监督和无监督学习来推断活跃的伊辛模型的相边界，从系统的配置的集合开始。我们说明，无监督学习技术是强大的在控制参数空间中的相边界识别，即使在相共存的情况下。它表明，监督学习与神经网络是能够学习的相图的特性，这样，在一组有限的控制变量获得的知识可以用来确定跨相图的相边界。通过这种方式，我们表明，适当设计的监督学习为相图中不包括在算法训练阶段的区域提供了预测能力。我们强调引入可解释性方法的重要性，以便通过深度学习对阶段进行物理相关的分类。
英文摘要：
Still under debate is the question of whether machine learning is capable of going beyond black-box modeling for complex physical systems. We investigate the generalizing and interpretability properties of learning algorithms. To this end, we use supervised and unsupervised learning to infer the phase boundaries of the active Ising model, starting from an ensemble of configurations of the system. We illustrate that unsupervised learning techniques are powerful at identifying the phase boundaries in the control parameter space, even in situations of phase coexistence. It is demonstrated that supervised learning with neural networks is capable of learning the characteristics of the phase diagram, such that the knowledge obtained at a limited set of control variables can be used to determine the phase boundaries across the phase diagram. In this way, we show that properly designed supervised learning provides predictive power to regions in the phase diagram that are not included in the training phase of the algorithm. We stress the importance of introducing interpretability methods in order to perform a physically relevant classification of the phases with deep learning.
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