基于物理和数据驱动的发现,在异质介质中发现复杂现象的控制方程
1Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-1211, USA.
Physical review. E
|May 17, 2024
概括
在异质系统中分析复杂现象需要先进的建模. 新的基于物理和数据的方法为数据分析和预测提供了强大的工具.
科学领域:
- 复杂系统分析 复杂系统分析
- 计算物理学的计算物理.
- 数据科学是数据科学.
背景情况:
- 传感器技术,仪器仪表和数据采集的快速进步为异质介质中的复杂现象产生了庞大的数据集.
- 增加的科学计算能力可以模拟多尺度,多物理现象的随机性或异质性,产生大量的数值数据集.
- 分析和建模这些系统需要方法来解释数据和预测超出直接观察范围的尺度上的行为.
研究的目的:
- 根据管理方程和数据的可用性和知识对异质系统进行分类.
- 审查新兴的基于物理和数据的方法来分析和建模这些系统.
- 讨论这个快速发展的领域的最新发展和未来方向.
主要方法:
- 基于数据和管理方程知识,将系统分为三个类别.
- 机器学习的概述,符号回归,库普曼运算符,莫里-兹万齐格投影,非线性动态的稀疏识别,与神经网络的数据同化和随机优化.
- 描述基于物理和数据的分析,建模和预测方法.
主要成果:
- 根据数据和控制方程特征,确定了三类不同的异质系统.
- 强调了一系列适用于这些类的新兴计算和数据驱动技术.
- 强调将物理原理与数据驱动方法相结合越来越重要.
结论:
- 基于物理学的和数据驱动的方法的整合提供了一个强大的框架,用于处理异质系统中的复杂现象.
- 这些先进的技术对于从各种科学领域的大型数据集中提取见解和预测至关重要.
- 该领域的持续研究有望在理解和建模复杂的自然和工程系统方面取得重大进展.
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