Evidence map›Paper›PMID 41912550›Full record

ArticleScientific reports2026

Modeling and analysis of stochastic quantum magnetohydrodynamics equations with energy estimates.

K Divyabala, N Durga

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

K Divyabala *Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, 600127, India.
N Durga *Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, 600127, India. durga.nagarajan@vit.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This manuscript investigates the three-dimensional stochastic compressible quantum magnetohydrodynamic equation with density-dependent and stochastic external forces. First, we approximate the modelled system. In this work, the existence of a martingale solution within a given time interval is established by deriving energy estimates and applying the fixed-point argument. The proposed system is approximated using the Faedo-Galerkin method, the Jakubowski-Skorokhod theorem, and the compactness method. Furthermore, the result is analyzed in the limit [Formula: see text] and [Formula: see text]. Finally, numerical results for the quantum magnetohydrodynamic system are presented for the demonstrated theoretical findings.

Identifiers

PMID41912550
PMCPMC13039708

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.