Comprehensive Review of Physics-Guided Deep Learning: Advancements, Challenges, and Perspectives - CHEN Chong, ZHU Xiaoyu, WANG Fang, XU Yaqian, ZHANG Wei (Jisuanji kexue yu tansuo)
Although deep learning has significant achievements in addressing nonlinear and high-dimensional problems, it faces challenges in complex scientific and engineering domains (such as high computational costs and data requirements, the difficulties in interpreting its black-box nature, and the lack of capabilities for following the physical laws). Therefore, a novel framework called physics-guided deep learning has emerged which enhances the performance, explainability, and physical consistency of deep learning by integrating domain-specific physical knowledge into the construction and training process of deep learning models. This paper reviews and analyzes the researches (e.g., methodologies, applications, etc.) on physics-guided deep learning thoroughly. Firstly, the main motivations and theoretical foundations of the physics-guided deep learning are introduced. Secondly, a detailed discussion is conducted on the two modes: the combination of physical information with deep learning and the fusion of physical information with deep learning. The characteristics, limitations and application scenarios of the two modes are summarized and discussed. Finally, the performance of physics-guided deep learning on various applications is analyzed. Furthermore, the challenges of the physics-guided deep learning are discussed from four perspectives: computational complexity and convergence, biases while involving control equations, dependence on observational data, and difficulties in knowledge fusion, based on which, an outlook for the future direction of this domain is provided. This paper strives for providing research reference and multidimensional perspectives of physics-guided deep learning for the researchers.