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Record W2232642567 · doi:10.1088/1361-6420/33/2/025008

Size estimates of an obstacle in a stationary Stokes fluid

2017· article· en· W2232642567 on OpenAlexfundno aff
Elena Beretta, Cecilia Cavaterra, Jaime H. Ortega, Sebastián Zamorano

Bibliographic record

VenueInverse Problems · 2017
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchComisión Nacional de Investigación Científica y TecnológicaGruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro ApplicazioniUniversidad Autónoma de MadridEuropean Research CouncilInstituto de Ciencias MatemáticasMinisterio de Economía y CompetitividadNew York University Abu DhabiFP7 Ideas: European Research CouncilYork UniversityIstituto Nazionale di Alta Matematica "Francesco Severi"
KeywordsMathematicsBounded functionStokes flowObstacleDomain (mathematical analysis)Boundary (topology)Mathematical analysisWork (physics)Stokes numberStokes' lawGeometryFlow (mathematics)MechanicsPhysicsReynolds number

Abstract

fetched live from OpenAlex

Abstract In this work we are interested in estimating the size of a cavity D immersed in a bounded domain Ω ⊂ R d , d = 2, 3, filled with a viscous fluid governed by the Stokes system, by means of velocity and Cauchy forces on the external boundary ∂ Ω . More precisely, we establish some lower and upper bounds in terms of the difference between the external measurements when the obstacle is present and without the object. The proof of the result is based on interior regularity results and quantitative estimates of unique continuation for the solution of the Stokes system.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.281
Teacher spread0.250 · 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 designSimulation or modeling
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

Citations5
Published2017
Admission routes1
Has abstractyes

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