arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.14809nlin.PScs.LGmath-phmath.MPnlin.SI
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三阶非线性薛定谔方程的深度学习神经网络:孤子,呼吸子和流氓波

Deep learning neural networks for the third-order nonlinear Schrodinger equation: Solitons, breathers, and rogue waves

Chinese Academy of Sciences(中国科学院);University of Chinese Academy of Sciences(中国科学院大学)

Zijian Zhou, Zhenya Yan

摘要:

利用深度学习神经网络研究了三阶非线性薛定谔方程(Hirota方程),该方程描述了等离子体中的强色散离子声波、光纤中超短光脉冲的波传播以及深水中的宽带波.在本文中,我们使用物理信息神经网络(PINN)深度学习方法来探索数据驱动的解决方案(例如,孤立子,呼吸者,和流氓波)的Hirota方程时,两种类型的未扰动和未扰动(2%噪声)的训练数据被认为是。此外,我们使用PINNs深度学习来研究在孤子的帮助下,Hirota方程中出现的参数的数据驱动发现。

英文摘要:

The third-order nonlinear Schrodinger equation (alias the Hirota equation) is investigated via deep leaning neural networks, which describes the strongly dispersive ion-acoustic wave in plasma and the wave propagation of ultrashort light pulses in optical fibers, as well as broader-banded waves on deep water. In this paper, we use the physics-informed neural networks (PINNs) deep learning method to explore the data-driven solutions (e.g., soliton, breather, and rogue waves) of the Hirota equation when the two types of the unperturbated and unperturbated (a 2% noise) training data are considered. Moreover, we use the PINNs deep learning to study the data-driven discovery of parameters appearing in the Hirota equation with the aid of solitons.

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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