论文标题
利用物理引导的深度学习克服数据稀缺
Utilising physics-guided deep learning to overcome data scarcity
论文作者
论文摘要
深度学习（DL）在很大程度上依赖数据，并且数据质量显着影响其性能。但是，在许多现实世界中，例如结构性风险估计和医学诊断，获得高质量，良好的数据集可能具有挑战性，甚至是不可能的。这为在这些领域的实际实施中实施了一个重大障碍。物理学引导的深度学习（PGDL）是一种新型的DL类型，可以整合物理定律来训练神经网络。这可以应用于由机械，金融和医疗应用等物理法律控制或管辖的任何系统。已经证明，借助物理法律提供的其他信息，PGDL在存在数据稀缺的情况下实现了很高的准确性和泛化。这篇评论提供了对PGDL的详细检查，并提供了其在解决各个领域（包括物理，工程和医疗应用程序）中数据稀缺方面使用的结构化概述。此外，该评论确定了PGDL在数据稀缺方面的当前局限性和机会，并就PGDL的未来前景进行了详尽的讨论。
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.
