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Research Article | Open Access

Constructing abundant exact stochastic solutions to the Newell-Whitehead-Segel equation with multiplicative advection noise

Abeer H. Alblowy1Wael W. Mohammed1( )Hessa W. Alshammari1Elsayed M. Elsayed2,3
Department of Mathematics, College of Science, University of Ha'il, Ha'il 2440, Saudi Arabia
Mathematics Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
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Abstract

In this work, we consider the stochastic Newell-Whitehead-Segel (SNWS) equation, which is forced in the Itô sense by multiplicative advection noise. We demonstrate that the solution of the SNWS equation can be discovered by solving certain deterministic counterparts of the Newell-Whitehead-Segel equation with an extra diffusion term (DNWS), and then the results are combined with a solution for stochastic ordinary differential equations. We use two techniques to solve the DNWS equation: the ( G G )-expansion method and the exp ( ψ ( η ) ) -expansion method. The purpose of studying the Newell-Whitehead-Segel equation with multiplicative noise is to explore how stochastic fluctuations influence the stability and evolution of patterns near a bifurcation. Incorporating noise provides a more realistic description of physical systems and reveals impacts that are omitted from the deterministic model, such as changing thresholds, different pattern structures, and increased variability. Therefore, we use a MATLAB tool to exhibit numerous 3D and 2D graphs that show how multiplicative advection noise impacts the solutions of the SNWS equation.

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Electronic Research Archive
Pages 922-937

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Cite this article:
Alblowy AH, Mohammed WW, Alshammari HW, et al. Constructing abundant exact stochastic solutions to the Newell-Whitehead-Segel equation with multiplicative advection noise. Electronic Research Archive, 2026, 34(2): 922-937. https://doi.org/10.3934/era.2026043

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Received: 03 December 2025
Revised: 10 January 2026
Accepted: 24 January 2026
Published: 28 January 2026
©2026 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)