MétaCan
Menu
Back to cohort
Record W2809754117 · doi:10.1002/mma.5139

Kinetic‐fluid derivation and mathematical analysis of the cross‐diffusion–Brinkman system

2018· article· en· W2809754117 on OpenAlexaff
Mostafa Bendahmane, Fahd Karami, Mohamed Zagour

Bibliographic record

VenueMathematical Methods in the Applied Sciences · 2018
Typearticle
Languageen
FieldMathematics
TopicMathematical Biology Tumor Growth
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMathematicsFluid dynamicsFlow (mathematics)ComputationDiffusionKinetic energyKinetic theoryNonlinear systemVorticityStatistical physicsMathematical analysisClassical mechanicsMechanicsVortexPhysicsGeometryThermodynamics

Abstract

fetched live from OpenAlex

In this paper, we propose a new nonlinear model describing the dynamical interaction of two species within a viscous flow. The proposed model is a cross‐diffusion system coupled with the Brinkman problem written in terms of velocity fluid, vorticity, and pressure and describing the flow patterns driven by an external source depending on the distribution of species. In the first part, we derive macroscopic models from the kinetic‐fluid equations by using the micro‐macro decomposition method. On the basis of the Schauder fixed‐point theory, we prove the existence of weak solutions for the derived model in the second part. The last part is devoted to developing a one‐dimensional finite volume approximation for the kinetic‐fluid model, which is uniformly stable along the transition from kinetic to macroscopic regimes. Our computation method is validated with various numerical tests.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.085
GPT teacher head0.425
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Methods in the Applied SciencesSame topicMathematical Biology Tumor GrowthFrench-language works237,207