Rogue wave solutionGenuine rational soliton solutionsOne-rational soliton solutionThe solving of the derivative nonlinear Schrodinger equation (DNLS) has attracted considerable attention in theoretical analysis and physical applications. Based on the physics-informed neural network (PINN) which has been put...
independent numerical experiments, we evaluate the efficacy of PTS-PINN in tackling the forward and inverse problems for the nonlocal nonlinear Schrdinger (NLS) equation, the nonlocal derivative NLS equation, the nonlocal (2+1)-dimensional NLS equation, and the nonlocal three wave interaction ...
Optimization of Physics-Informed Neural Networks for Solving the Nolinear Schrdinger Equation Physics Informed Neural Networks (PINN) is a promising method for solving partial differential equations using machine learning. In this paper we consider ... I Chuprov,J Gao,D Efremenko,... - 《Doklady...
Neural network method for solving analytical solution: (神经网络求解偏微分方程解析解的方法:) [1]R. F. Zhang, M. C. Li, M. Albishari, F. C. Zheng, Z. Z. Lan, "[Generalized lump solutions, classical lump solutions and rogue waves of the (2+1)-dimensional Caudrey-Dodd-Gibbon-Kotera...
The Chen-Lee-Liu equationWe consider the exact rogue periodic wave (rogue wave on the periodic background) and periodic wave solutions for the Chen-Lee-Liu equation via the odd-th order Darboux transformation. Then, the multi-layer physics-informed neural networks (PINNs) deep learning method ...
The proposed 2W-PE method has high computational accuracy and efficiency, which reflects the applicability of machine learning in solving the computational efficiency problem of radio wave propagation. Therefore, this study provides a very effective and reliable method for solving the ...
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 2019, 378, 686–707. [Google Scholar] [CrossRef] Kelley, H.J. Gradient theory of optimal flight paths. Ars J. 1960, 30, ...
[38] introduced an improved method for solving ordinary DEs and partial differential equations by employing Artificial Neural Networks (ANNs). In their newly introduced method, the differential equation’s trial solution was represented by the sum of two parts. There were no adjustable parameters in...
[38] introduced an improved method for solving ordinary DEs and partial differential equations by employing Artificial Neural Networks (ANNs). In their newly introduced method, the differential equation's trial solution was represented by the sum of two parts. There were no adjustable parameters in ...