Communications in Theoretical Physics ›› 2020, Vol. 72 ›› Issue (11): 115003. doi: 10.1088/1572-9494/abb7c8

• Mathematical Physics • Previous Articles     Next Articles

A deep learning method for solving third-order nonlinear evolution equations

Jun Li(李军)1,Yong Chen(陈勇)2,3,4,()   

  1. 1 Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, 200062, China
    2 School of Mathematical Sciences, Shanghai Key Laboratory of PMMP, Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, 200062, China
    3 College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China
    4 Department of Physics, Zhejiang Normal University, Jinhua, 321004, China
  • Received: 2020-05-15 Revised: 2020-07-29 Accepted: 2020-07-29 Published: 2020-11-01
  • Contact: Yong Chen(陈勇) E-mail:ychen@sei.ecnu.edu.cn

Abstract:

It has still been difficult to solve nonlinear evolution equations analytically. In this paper, we present a deep learning method for recovering the intrinsic nonlinear dynamics from spatiotemporal data directly. Specifically, the model uses a deep neural network constrained with given governing equations to try to learn all optimal parameters. In particular, numerical experiments on several third-order nonlinear evolution equations, including the Korteweg–de Vries (KdV) equation, modified KdV equation, KdV–Burgers equation and Sharma–Tasso–Olver equation, demonstrate that the presented method is able to uncover the solitons and their interaction behaviors fairly well.

Key words: deep learning, nonlinear evolution equations, soliton interaction, nonlinear dynamics