Use of BNNM for interference wave solutions of the gBS-like equation and comparison with PINNs

08/07/2022
by   Shashank Reddy Vadyala, et al.
0

In this work, the generalized broken soliton-like (gBS-like) equation is derived through the generalized bilinear method. The neural network model, which can fit the explicit solution with zero error, is found. The interference wave solution of the gBS-like equation is obtained by using the bilinear neural network method (BNNM) and physical informed neural networks (PINNs). Interference waves are shown well via three-dimensional plots and density plots. Compared with PINNs, the bilinear neural network method is not only more accurate but also faster.

READ FULL TEXT
research
04/30/2021

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

The third-order nonlinear Schrodinger equation (alias the Hirota equatio...
research
02/11/2022

Finding the Shape of Lacunae of the Wave Equation Using Artificial Neural Networks

We apply a fully connected neural network to determine the shape of the ...
research
07/08/2021

Existence of The Solution to The Quadratic Bilinear Equation Arising from A Class of Quadratic Dynamical Systems

A quadratic dynamical system with practical applications is taken into c...
research
01/21/2021

Traveling Wave Solutions of Partial Differential Equations via Neural Networks

This paper focuses on how to approximate traveling wave solutions for va...
research
07/14/2019

Wave solutions of Gilson-Pickering equation

In this work, we apply the (1/G')-expansion method to produce the novel ...
research
09/25/2022

A Deep Learning Approximation of Non-Stationary Solutions to Wave Kinetic Equations

We present a deep learning approximation, stochastic optimization based,...
research
03/06/2023

Single-shot phase retrieval: a holography-driven problem in Sobolev space

The phase-shifting digital holography (PSDH) is a widely used approach f...

Please sign up or login with your details

Forgot password? Click here to reset