基于物理的机器学习的平衡方程
Sandor M Molnar1, Joseph Godfrey2, Binyang Song3
1Institute of Astronomy and Astrophysics, Academia Sinica, Taipei, Taiwan, Republic of China.
Heliyon
|December 10, 2024
概括
本研究引入了一个新的框架,使用平衡方程来系统地构建基于物理的机器学习 (PIML) 剩余损失术语. 这种方法确保了机器学习模型在各种科学和工程领域都能遵守物理定律.
科学领域:
- 基于物理的机器学习 (PIML)
- 计算科学是一种计算科学.
- 应用数学 应用数学 应用数学
背景情况:
- 传统的机器学习 (ML) 模型可能会违反物理定律.
- 基于物理学的模型通过损失术语使用物理定律来限制ML.
- 在ML中为物理定律推导复杂微分方程是具有挑战性的.
研究的目的:
- 提出一个系统的框架,用于在PIML中构建剩余损失条款.
- 确保ML解决方案与物理定律一致.
- 在多个领域推进PIML开发.
主要方法:
- 为PIML开发了一个基于平衡方程的新框架.
- 提出了一种统一的方法来从通用平衡方程中推导微分方程.
- 通过工作示例展示了实际应用.
主要成果:
- 余额方程方法为制定剩余损失条款提供了一种系统的方法.
- 一个统一的框架可以将物理定律纳入机器学习模型.
- 这种方法确保了部分微分方程 (PDE) 解决方案的物理完整性.
结论:
- 拟议的平衡方程方法为PIML提供了通用的方法.
- 这个框架统一了对基本物理方程的处理.
- 它可能会导致对复杂系统的基于物理的ML的更有效的开发.
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