STUDY OF SCHRÖDINGER-CHOQUARD PROBLEM WITH <i>P</i>(·)-LAPLACIAN OPERATOR

In this paper, our focus is on a specific class of non-linear $ \psi $-Hilfer fractional generalized Schrödinger-Choquard differential equations involving the $ p(\cdot) $-Laplacian operator with Dirichlet boundary conditions. By employing the mountain pass theorem without the Palais–Smale condition, along with the Hardy-Littlewood-Sobolev inequality with variable exponents, we establish the existence of a weak solution to our problem. Our main results are novel and contribute to the literature on problems involving $ \psi $-Hilfer derivatives with the $ p(\cdot) $-Laplacian operator. This investigation enhances the scope of understanding in this specific class of problems.

Authors

Publication Details

Journal
Journal of Applied Analysis & Computation
Published
2025-09-04
DOI
https://doi.org/10.11948/20240146
Primary Topic
Advanced Mathematical Modeling in Engineering
Type
article
Field-Weighted Citation Impact
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article

STUDY OF SCHRÖDINGER-CHOQUARD PROBLEM WITH P(·)-LAPLACIAN OPERATOR

Hamza El‐Houari, Elhoussain Arhrrabi, H. Moussa
Journal of Applied Analysis & Computation
Advanced Mathematical Modeling in Engineering
article

STUDY OF SCHRÖDINGER-CHOQUARD PROBLEM WITH P(·)-LAPLACIAN OPERATOR

Hamza El‐Houari, Elhoussain Arhrrabi, H. Moussa
article en

Abstract

In this paper, our focus is on a specific class of non-linear $ \psi $-Hilfer fractional generalized Schrödinger-Choquard differential equations involving the $ p(\cdot) $-Laplacian operator with Dirichlet boundary conditions. By employing the mountain pass theorem without the Palais–Smale condition, along with the Hardy-Littlewood-Sobolev inequality with variable exponents, we establish the existence of a weak solution to our problem. Our main results are novel and contribute to the literature on problems involving $ \psi $-Hilfer derivatives with the $ p(\cdot) $-Laplacian operator. This investigation enhances the scope of understanding in this specific class of problems.

Journal of Applied Analysis & ComputationVol. 16(1)
Openalex Percentile: Top 75%
Advanced Mathematical Modeling in Engineering
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