In this paper,by modifying loss function MSE(adding the mean square error of the complex conjugate term to the loss function)and training area of the physics-informed neural network(PINN),the authors proposed two neur...In this paper,by modifying loss function MSE(adding the mean square error of the complex conjugate term to the loss function)and training area of the physics-informed neural network(PINN),the authors proposed two neural network models:Mix-training PINN and prior information mix-training PINN.The authors demonstrated the advantages of these models by simulating rogue waves in the nonlocal PT-symmetric Schrödinger equation.Numerical experiments showed that the proposed models not only simulate first-order rogue waves,but also significantly improve the simulation capability.Compared with original PINN,the prediction accuracy of the first-order rouge waves are improved by one to three orders of magnitude.By testing the inverse problem of first-order rogue waves,it is also proved that these models have good performance.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.12175111,12275144,and 12235007K.C.Wong Magna Fund in Ningbo University。
文摘In this paper,by modifying loss function MSE(adding the mean square error of the complex conjugate term to the loss function)and training area of the physics-informed neural network(PINN),the authors proposed two neural network models:Mix-training PINN and prior information mix-training PINN.The authors demonstrated the advantages of these models by simulating rogue waves in the nonlocal PT-symmetric Schrödinger equation.Numerical experiments showed that the proposed models not only simulate first-order rogue waves,but also significantly improve the simulation capability.Compared with original PINN,the prediction accuracy of the first-order rouge waves are improved by one to three orders of magnitude.By testing the inverse problem of first-order rogue waves,it is also proved that these models have good performance.
