将机器学习与基于物理的建模相结合
Integrating Machine Learning with Physics-Based Modeling
摘要：
机器学习是一个非常强大的工具，可以大大提高我们进行科学研究的能力。然而，在这成为现实之前，需要解决许多问题。本文重点关注一个引起广泛兴趣的特定问题：我们如何将机器学习与基于物理的建模集成，以开发新的可解释且真正可靠的物理模型？在介绍了一般准则之后，我们讨论了开发基于机器学习的物理模型的两个最重要的问题：施加物理约束和获得最佳数据集。我们还为现代机器学习成功背后的根本原因提供了一个简单而直观的解释，并介绍了将机器学习与基于物理的建模相结合所需的并发机器学习框架。以分子动力学和动力学方程的矩封闭为例说明了所讨论的主要问题。最后，我们对这种整合将把我们带到哪里，以及在机器学习成功整合到科学建模中之后，新的前沿将在哪里进行了一般性的讨论。
英文摘要：
Machine learning is poised as a very powerful tool that can drastically improve our ability to carry out scientific research. However, many issues need to be addressed before this becomes a reality. This article focuses on one particular issue of broad interest: How can we integrate machine learning with physics-based modeling to develop new interpretable and truly reliable physical models? After introducing the general guidelines, we discuss the two most important issues for developing machine learning-based physical models: Imposing physical constraints and obtaining optimal datasets. We also provide a simple and intuitive explanation for the fundamental reasons behind the success of modern machine learning, as well as an introduction to the concurrent machine learning framework needed for integrating machine learning with physics-based modeling. Molecular dynamics and moment closure of kinetic equations are used as examples to illustrate the main issues discussed. We end with a general discussion on where this integration will lead us to, and where the new frontier will be after machine learning is successfully integrated into scientific modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
