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arXiv 2012.09984nlin.PScs.LGquant-ph
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使用PINN深度学习的散焦NLS方程中的数据驱动的异常波和参数发现

Data-driven rogue waves and parameter discovery in the defocusing NLS equation with a potential using the PINN deep learning

Academy of Mathematics and Systems Science, Chinese Academy of Sciences(中国科学院数学与系统科学研究院);University of Chinese Academy of Sciences(中国科学院大学)

Li Wang, Zhenya Yan

摘要:

物理信息神经网络(PINN)可用于深度学习非线性偏微分方程和其他类型的物理模型。在本文中,我们使用多层PINN深度学习方法来研究具有时间依赖势的散焦非线性薛定谔(NLS)方程的数据驱动的流氓波解,考虑几个初始条件,如流氓波,Jacobi椭圆余弦函数,双高斯函数或三双曲正割函数,以及周期性边界条件。此外,多层PINN算法还可以在流氓波解的意义下学习含时势的离焦NLS方程中的参数。这些结果将有助于进一步讨论在深度学习神经网络研究中具有潜力的离焦NLS方程的流氓波解。

英文摘要:

The physics-informed neural networks (PINNs) can be used to deep learn the nonlinear partial differential equations and other types of physical models. In this paper, we use the multi-layer PINN deep learning method to study the data-driven rogue wave solutions of the defocusing nonlinear Schrödinger (NLS) equation with the time-dependent potential by considering several initial conditions such as the rogue wave, Jacobi elliptic cosine function, two-Gaussian function, or three-hyperbolic-secant function, and periodic boundary conditions. Moreover, the multi-layer PINN algorithm can also be used to learn the parameter in the defocusing NLS equation with the time-dependent potential under the sense of the rogue wave solution. These results will be useful to further discuss the rogue wave solutions of the defocusing NLS equation with a potential in the study of deep learning neural networks.

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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