Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challenging or even impossible in many real-world applications, such as structural risk estimation and medical diagnosis. This presents a significant barrier to the practical implementation of DL in these fields. Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. This can be applied to any systems that are controlled or governed by physics laws, such as mechanics, finance and medical applications. It has been demonstrated that, with the additional information provided by physics laws, PGDL achieves great accuracy and generalisation in the presence of data scarcity. This review provides a detailed examination of PGDL and offers a structured overview of its use in addressing data scarcity across various fields, including physics, engineering and medical applications. Moreover, the review identifies the current limitations and opportunities for PGDL in relation to data scarcity and offers a thorough discussion on the future prospects of PGDL.

翻译：深度学习（DL）高度依赖于数据，且数据质量对其性能有显著影响。然而，在许多实际应用中（如结构风险估计和医学诊断），获取高质量、标注完善的数据集可能具有挑战性，甚至无法实现。这严重阻碍了深度学习在这些领域的实际应用。物理引导的深度学习（PGDL）是一种新型的深度学习范式，能够融合物理定律来训练神经网络。该方法适用于任何受物理定律控制或支配的系统，例如力学、金融和医疗应用。研究表明，借助物理定律提供的额外信息，PGDL在数据稀缺条件下仍能实现较高的准确性和泛化能力。本综述对PGDL进行了详细探讨，并系统概述了其在物理、工程和医疗等多个领域应对数据稀缺问题的应用。此外，本文指出了PGDL当前在数据稀缺背景下的局限性与发展机遇，并对PGDL的未来前景进行了深入讨论。