重庆理工大学学报2025，Vol.39Issue(5)：172-176,5.DOI:10.3969/j.issn.1674-8425(z).2025.03.022
内嵌物理知识网络驱动的振动离散模型求解方法研究
Solving research on vibration discrete models based on PINN
摘要
Abstract
Currently,with the growing sophistication of mechanical equipment,solving dynamic model becomes ever more pressing.Fortunately,machine learning provides new ways for solving complex problems.To explore the applications of Physics-informed neural networks(PINNs)in solving dynamic systems,from the perspective of vibration mechanics,the input/output and loss functions of the network are first modified.Then,the residual network is introduced to improve traditional PINNs.Taking the system dynamic response analysis and system identification of the two-dimensional vibration discrete system as an example,our results show the improved PINN accurately solves forward and inverse problems of vibration mechanics model,providing some references for solving the complex dynamic model by employing the neural network.关键词
内嵌物理知识网络/系统动力响应分析/系统识别Key words
physics-informed neural networks/system dynamic response analysis/system identification分类
机械制造引用本文复制引用
赵科炜,何国林..内嵌物理知识网络驱动的振动离散模型求解方法研究[J].重庆理工大学学报,2025,39(5):172-176,5.基金项目
国家自然科学基金项目(52075182) （52075182）
