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

2022/08/07 by Shashank Reddy Vadyala, Vadyala, Shashank Reddy, Sai Nethra Betgeri +1
Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Fractional Differential Equations Solutions #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural and Evolutionary Computing (cs.NE) #Numerical Analysis (math.NA)

paper · pdf · doi:10.48550/arxiv.2210.12154

openalex publication_date 2022/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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