论文标题
当物理学符合机器学习时：对物理信息的调查机器学习
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
论文作者
论文摘要
物理知识的机器学习（PIML）指的是物理学知识的组合，这是悠久历史上自然现象和人类行为的高水平抽象，具有数据驱动的机器学习模型，它已成为减轻训练数据的有效方法，以增加训练数据的概括性和确保物理上的物理质量的结果。在本文中，我们调查了PIML的最新作品，并从三个方面总结了它们：（1）PIML的动机，（2）PIML中的物理知识，（3）PIML中物理知识整合的方法。我们还讨论了PIML中当前的挑战和相应的研究机会。
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results. In this paper, we survey an abundant number of recent works in PIML and summarize them from three aspects: (1) motivations of PIML, (2) physics knowledge in PIML, (3) methods of physics knowledge integration in PIML. We also discuss current challenges and corresponding research opportunities in PIML.
