电子科技大学学报2024，Vol.53Issue(2)：227-234,8.DOI:10.12178/1001-0548.2023036
基于频域控制约束的物理神经网络非线性系统预测方法
Nonlinear System Prediction Method of Physical Neural Networks Based on Frequency Domain Control Constraints
摘要
Abstract
To address the problems of high computational cost and boundary condition limitations associated with the existing physical information neural network using numerical simulation to approximate the physical control equations,a nonlinear system prediction method of physical neural networks based on frequency domain control constraints is proposed.Firstly,a nonlinear prediction network model with alternating updates of temporal features is constructed,followed by a physical control equation constraint based on the Fourier spectrum method(FSM)in the frequency domain,and then the spatio-temporal data are trained without labels under the coupling of the network model and the frequency domain control constraint to complete the system evolution learning.The experimental results show that the proposed method can achieve unlabeled nonlinear complex modeling under physical rule constraints,and has faster learning speed and prediction accuracy compared with the mainstream Physics Informed Neural Network(PINN)model and its variants.In the case of t≤0.25 s and t≤0.5 s short-time prediction,the Mean Square Error(MSE)of the system is reduced by 86%and 95%compared with that of the mainstream baseline model in the same period of time after 20 times of pre-training,and the MSE of the system can be reduced by 80%in the case oft≤2 s long-time prediction after sufficient training.关键词
物理信息神经网络/傅里叶谱方法/频域控制方程约束/Burgers系统/非线性系统预测Key words
physical information neural network/Fourier spectrum method/frequency domain governing equation constraints/Burgers system/nonlinear system prediction分类
信息技术与安全科学引用本文复制引用
钱夔,宋爱国,田磊..基于频域控制约束的物理神经网络非线性系统预测方法[J].电子科技大学学报,2024,53(2):227-234,8.基金项目
国家自然科学基金(61902179) （61902179）
江苏省自然科学基金(BK20210931) （BK20210931）
深圳市自由探索类基础研究项目(2021szvup025) （2021szvup025）
