物理信息神经网络的应用与研究进展

Applications and Advancements of Physics-Informed Neural Networks:An Overview

物理信息神经网络(PINNs)是将物理建模与深度学习相结合的创新方法,较于纯数据驱动的神经网络,PINNs通过结合物理信息,极大地降低了对数据量的依赖,在解决复杂物理问题的科学计算方面表现出卓越的实用性.通过梳理PINNs在不通领域发展现状,首先系统阐述了PINNs的基本原理,强调了其独特的物理定律融入神经网络的能力.其次,详细介绍了PINNs在科学和工程领域的广泛应用,展示了PINNs成功解决各个领域正问题和反问题的显著成果.进一步,深入探讨了PINNs的研究进展,包括不同方面的创新成果,以及在此基础上产生的改进方法.最后,详细分析总结了PINNs发展所面临的机遇和挑战,以期为该领域的研究提供了有价值的参考.

Physical information neural networks(PINNs)represent an innovative approach that integrates physical modeling with deep learning.Compared to purely data-driven neural networks,PINNs significantly reduce data dependency by incorporating physical information,demonstrating excellent utility in solving complex physical problems.Firstly,the current development of PINNs in inaccessible fields is reviewed,systematically elaborating on the basic principles and emphasizing their unique ability to incorporate physical laws into neural networks.Secondly,the extensive applications of PINNs in science and engineering are detailed,showcasing their remarkable success in solving both direct and inverse problems across various fields.Further,the research progress of PINNs is thoroughly discussed,covering various innovative results and the improved methods developed on this basis.Finally,the opportunities and challenges facing the development of PINNs are analyzed and summarized in detail,aiming to provide valuable references for future research in this field.