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arXiv 2101.08593nlin.PSnlin.SI
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用改进的PINN方法求解导数非线性Schrodinger方程的局域波解

Solving localized wave solutions of the derivative nonlinear Schrodinger equation using an improved PINN method

Juncai Pu, Jun Li, Yong Chen

摘要:

导数非线性薛定谔方程(DNLS)的求解在理论分析和物理应用方面都引起了广泛的关注。在物理信息神经网络(PINN)直接从时空数据中揭示非线性偏微分方程动力学行为的基础上,提出了一种基于神经元局部自适应激活函数的改进PINN方法,用于求解复空间DNLS的局域波解.为了比较这两种方法的性能,我们用两种方法分别给出了DNLS的单有理孤子解、真有理孤子解和流氓波解的动力学行为和误差分析,并给出了生动的图形和详细的分析。数值结果表明,改进后的方法具有更快的收敛速度和更好的模拟效果.在改进方法的基础上,考虑了初始采样点数、剩余配置采样点数、网络层数、每隐层神经元数对DNLS二阶真有理孤子解动力学的影响,并对局部自适应激励函数选取不同的可伸缩参数初值进行了相关分析。订购流氓波解决方案。

英文摘要:

The 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 forward to uncover dynamical behaviors of nonlinear partial different equation from spatiotemporal data directly, an improved PINN method with neuron-wise locally adaptive activation function is presented to derive localized wave solutions of the DNLS in complex space. In order to compare the performance of above two methods, we reveal the dynamical behaviors and error analysis for localized wave solutions which include one-rational soliton solution, genuine rational soliton solutions and rogue wave solution of the DNLS by employing two methods, also exhibit vivid diagrams and detailed analysis. The numerical results demonstrate the improved method has faster convergence and better simulation effect. On the bases of the improved method, the effects for different numbers of initial points sampled, residual collocation points sampled, network layers, neurons per hidden layer on the second order genuine rational soliton solution dynamics of the DNLS are considered, and the relevant analysis when the locally adaptive activation function chooses different initial values of scalable parameters are also exhibited in the simulation of the two-order rogue wave solution.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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