We propose effective scheme of deep learning method for high-order nonlinear soliton equation and compare the activation function for high-order soliton equation. The neural network approximates the solution of the equation under the conditions of differential operator, initial condition and boundary condition. We apply this method to high-order nonlinear soliton equation, and verify its efficiency by solving the fourth-order Boussinesq equation and the fifth-order Korteweg de Vries equation. The results show that deep learning method can solve the high-order nonlinear soliton equation and reveal the interaction between solitons
In the current paper, a neural network method to solve sixth-order differential equations and their ...
Deep learning—in particular, deep neural networks (DNNs)—as a mesh-free and self-adapting method has...
We put forth two physics-informed neural network (PINN) schemes based on Miura transformations and t...
In the process of the deep learning, we integrate more integrable information of nonlinear wave mode...
We introduce a deep neural network learning scheme to learn the B\"acklund transforms (BTs) of solit...
This work aims to provide an effective deep learning framework to predict the vector-soliton solutio...
In an attempt to find alternatives for solving partial differential equations (PDEs)with traditional...
The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding the informat...
Nonlinear evolution equations play enormous significant roles to work with complicated physical phen...
In this article, a new deep learning architecture, named JDNN, has been proposed to approximate a nu...
Korteweg de Vries (KdV) equation has been used as a mathematical model of shallow water waves. In ...
In this thesis, we focus on developing neural networks algorithms for scientific computing. First, w...
PhD ThesisAfter introducing the nonlinear evolution equations of interest: the finite depth fluid (...
High-dimensional PDEs have been a longstanding computational challenge. We propose to solve high-dim...
In this paper, we study deep neural networks (DNNs) for solving high-dimensional evolution equations...
In the current paper, a neural network method to solve sixth-order differential equations and their ...
Deep learning—in particular, deep neural networks (DNNs)—as a mesh-free and self-adapting method has...
We put forth two physics-informed neural network (PINN) schemes based on Miura transformations and t...
In the process of the deep learning, we integrate more integrable information of nonlinear wave mode...
We introduce a deep neural network learning scheme to learn the B\"acklund transforms (BTs) of solit...
This work aims to provide an effective deep learning framework to predict the vector-soliton solutio...
In an attempt to find alternatives for solving partial differential equations (PDEs)with traditional...
The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding the informat...
Nonlinear evolution equations play enormous significant roles to work with complicated physical phen...
In this article, a new deep learning architecture, named JDNN, has been proposed to approximate a nu...
Korteweg de Vries (KdV) equation has been used as a mathematical model of shallow water waves. In ...
In this thesis, we focus on developing neural networks algorithms for scientific computing. First, w...
PhD ThesisAfter introducing the nonlinear evolution equations of interest: the finite depth fluid (...
High-dimensional PDEs have been a longstanding computational challenge. We propose to solve high-dim...
In this paper, we study deep neural networks (DNNs) for solving high-dimensional evolution equations...
In the current paper, a neural network method to solve sixth-order differential equations and their ...
Deep learning—in particular, deep neural networks (DNNs)—as a mesh-free and self-adapting method has...
We put forth two physics-informed neural network (PINN) schemes based on Miura transformations and t...