论文标题
通过拓扑数据分析揭示模式形成过程机理的程序
Procedure to Reveal the Mechanism of Pattern Formation Process by Topological Data Analysis
论文作者
论文摘要
拓扑数据分析（TDA）是一种多功能工具，可用于从复杂的模式形成过程中提取科学知识。但是，从TDA获得的特征与模式动力学之间的物理对应关系并不同意，并且需要根据要分析的现象适当设置TDA特征的物理解释。在这项研究中，我们提出了一种分析程序，以通过TDA和机器学习技术对模式动态进行物理解释。提出的程序应用于磁性域模式形成的过程，以量化非平凡域模式分类并揭示基础动力学的性质。在这些发现的基础上，我们还提出了一个候选模型，以了解磁性域形成的性质。
Topological data analysis (TDA) is a versatile tool that can be used to extract scientific knowledge from complex pattern formation processes. However, the physics correspondence between the features obtained from TDA and pattern dynamics does not agree one-to-one, and the physical interpretation of the TDA features needs to be set appropriately according to the phenomenon to be analyzed. In this study, we propose an analytical procedure to physically interpret pattern dynamics through TDA and machine learning techniques. The proposed procedure was applied to the process of magnetic domain pattern formation to quantify non-trivial domain pattern classifications and reveal the nature of the underlying dynamics. On the basis of these findings, we also propose a candidate reduction model to understand the nature of magnetic domain formation.
