Work

An optimized hybrid block technique framework for partial differential equations by exploring radial basis function neural networks

Year: 2025

Type: article

Abstract: This article explores recent advances in integrating artificial neural networks (ANNs) with numerical methods, focusing on an optimized two-stage hybrid block method for solving complex partial differential equations (PDEs). The method combines ANNs with traditional solvers to improve computational efficiency and accuracy, using a Radial Basis Function Neural Network (RBFNN) for optimization. This approach enhances stability, convergence, and adaptability, especially in multidimensional problems, and reduces computational costs while maintaining precision in fields like fluid dynamics and electromagnetism. Simulations show the method outperforms conventional solvers, enabling more efficient real-time simulations in engineering and mathematics.

Source: Franklin Open

DOI: 10.1016/j.fraope.2025.100332

Authors: Sabastine Emmanuel, Saratha Sathasivam, Muideen O. Ogunniran, Mustafa Bayram

Institutions: Universiti Sains Malaysia, Federal University Lokoja, Osun State University, Biruni University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,900

OpenAlex ID: W4413640860

FWCI:

Cites: 18

Cited by:

Related to: 10