131 citations · 268 across the 10 of their papers we have counts for
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Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization
Juncai Pu, Yong Chen
We investigate data-driven forward-inverse problems for Yajima-Oikawa (YO) system by employing two technologies which improve the performance of neural network in deep physics-info…
Data-driven vector localized waves and parameters discovery for Manakov system using deep learning approach
Juncai Pu, Yong Chen
An improved physics-informed neural network (IPINN) algorithm with four output functions and four physics constraints, which possesses neuron-wise locally adaptive activation funct…
The data-driven localized wave solutions of the derivative nonlinear Schrodinger equation by using improved PINN approach
Juncai Pu, Weiqi Peng, Yong Chen
The research of the derivative nonlinear Schrodinger equation (DNLS) has attracted more and more extensive attention in theoretical analysis and physical application. The improved…
PINN deep learning for the Chen-Lee-Liu equation: Rogue wave on the periodic background
Weiqi Peng, Juncai Pu, Yong Chen
We 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 transformat…
Double and Triple-Pole Solutions for the Third-Order Flow Equation of the Kaup-Newell System with Zero/Nonzero Boundary Conditions
Juncai Pu, Yong Chen
In this work, the double and triple-pole solutions for the third-order flow equation of Kaup-Newell system (TOFKN) with zero boundary conditions (ZBCs) and non-zero boundary condit…
Solving localized wave solutions of the derivative nonlinear Schrodinger equation using an improved PINN method
Juncai Pu, Jun Li, Yong Chen
The solving of the derivative nonlinear Schrodinger equation (DNLS) has attracted considerable attention in theoretical analysis and physical applications. Based on the physics-inf…