activity
20192024
most citedData-driven rogue waves and parameter discovery in the defocusing NLS equation with a potential using the PINN deep learning

148 citations · 324 across the 13 of their papers we have counts for

collaborators

13 papers

stat.ML2024

Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ?

Zijian Zhou, Zhenya Yan

In this paper, we study the neural tangent kernel (NTK) for general partial differential equations (PDEs) based on physics-informed neural networks (PINNs). As we all know, the tra…

nlin.PS2023★ 1 cited

Deep learning soliton dynamics and complex potentials recognition for 1D and 2D PT-symmetric saturable nonlinear Schrödinger equations

Jin Song, Zhenya Yan

In this paper, we firstly extend the physics-informed neural networks (PINNs) to learn data-driven stationary and non-stationary solitons of 1D and 2D saturable nonlinear Schröding…

nlin.PS2023

Data-driven localized waves and parameter discovery in the massive Thirring model via extended physics-informed neural networks with interface zones

Junchao Chen, Jin Song, Zijian Zhou +1

In this paper, we study data-driven localized wave solutions and parameter discovery in the massive Thirring (MT) model via the deep learning in the framework of physics-informed n…

nlin.PS2023★ 5 cited

Symmetry breaking bifurcations and excitations of solitons in linearly coupled NLS equations with PT-symmetric potentials

Jin Song, Boris A. Malomed, Zhenya Yan

We address symmetry breaking bifurcations (SBBs) in the ground-state (GS) and dipole-mode (DM) solitons of the 1D linearly coupled NLS equations, modeling the propagation of light…

quant-ph2023★ 7 cited

Formations and dynamics of two-dimensional spinning asymmetric quantum droplets controlled by a PT-symmetric potential

Jin Song, Zhenya Yan, Boris A. Malomed

In this paper, vortex solitons are produced for a variety of 2D spinning quantum droplets (QDs) in a PT-symmetric potential, modeled by the amended Gross-Pitaevskii equation with L…

nlin.PS2022★ 29 cited

Spontaneous symmetry breaking and ghost states supported by the fractional nonlinear Schrödinger equation with focusing saturable nonlinearity and PT-symmetric potential

Ming Zhong, Li Wang, Pengfei Li +1

We report a novel spontaneous symmetry breaking phenomenon and ghost states existed in the framework of the fractional nonlinear Schrödinger (FNLS) equation with focusing saturable…

nlin.SI2022★ 22 cited

Interactions of fractional N-solitons with anomalous dispersions for the integrable combined fractional higher-order mKdV hierarchy

Minghe Zhang, Weifang Weng, Zhenya Yan

In this paper, we investigate the anomalous dispersive relations, inverse scattering transform with a Riemann-Hilbert (RH) problem, and fractional multi-solitons of the integrable…

nlin.PS2021★ 1 cited

Deep neural networks for solving forward and inverse problems of (2+1)-dimensional nonlinear wave equations with rational solitons

Zijian Zhou, Li Wang, Zhenya Yan

In this paper, we investigate the forward problems on the data-driven rational solitons for the (2+1)-dimensional KP-I equation and spin-nonlinear Schrödinger (spin-NLS) equation v…

cs.LG2021★ 29 cited

Data-driven discoveries of Bäcklund transforms and soliton evolution equations via deep neural network learning schemes

Zijian Zhou, Li Wang, Weifang Weng +1

We introduce a deep neural network learning scheme to learn the Bäcklund transforms (BTs) of soliton evolution equations and an enhanced deep learning scheme for data-driven solito…

nlin.PS2021★ 59 cited

Data-driven peakon and periodic peakon travelling wave solutions of some nonlinear dispersive equations via deep learning

Li Wang, Zhenya Yan

In the field of mathematical physics, there exist many physically interesting nonlinear dispersive equations with peakon solutions, which are solitary waves with discontinuous firs…

nlin.PS2020★ 148 cited

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

Li Wang, Zhenya Yan

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 mu…

nlin.PS2020

Rogue wave formation and interactions in the defocusing nonlinear Schrödinger equation with external potentials

Li Wang, Zhenya Yan

The defocusing nonlinear Schrödinger (NLS) equation has no the modulational instability, and was not found to possess the rogue wave (RW) phenomenon up to now. In this paper, we fi…

nlin.PS2019★ 23 cited

Attraction centers and PT-symmetric delta-functional dipoles in critical and supercritical self-focusing media

Li Wang, Boris A. Malomed, Zhenya Yan

We introduce a model based on the one-dimensional nonlinear Schroedinger equation (NLSE) with the critical (quintic) or supercritical self-focusing nonlinearity. We demonstrate tha…