论文标题
动态系统数据驱动发现的综述
A Review of Data-Driven Discovery for Dynamic Systems
论文作者
论文摘要
许多现实世界的科学过程受复杂的非线性动态系统的控制，可以用微分方程表示。最近，使用数据驱动的方法，对学习或发现方程式的方程式形式增加了兴趣。在本文中，我们回顾了有关动态系统数据驱动发现的当前文献。我们为数据驱动的发现的不同方法和一个统一的数学框架提供了分类，以显示方法之间的关系。重要的是，我们讨论统计在数据驱动的发现领域中的作用，描述一种可能在统计框架中提出问题的方法，并为将来的工作提供途径。
Many real-world scientific processes are governed by complex nonlinear dynamic systems that can be represented by differential equations. Recently, there has been increased interest in learning, or discovering, the forms of the equations driving these complex nonlinear dynamic system using data-driven approaches. In this paper we review the current literature on data-driven discovery for dynamic systems. We provide a categorization to the different approaches for data-driven discovery and a unified mathematical framework to show the relationship between the approaches. Importantly, we discuss the role of statistics in the data-driven discovery field, describe a possible approach by which the problem can be cast in a statistical framework, and provide avenues for future work.
