MétaCan
Menu
Back to cohort
Record W1993892019 · doi:10.4171/ifb/148

Periodic phase separation: the periodic Cahn-Hilliard and isoperimetric problems

2006· article· en· W1993892019 on OpenAlexafffund
Rustum Choksi, Peter Sternberg

Bibliographic record

VenueInterfaces and Free Boundaries Mathematical Analysis Computation and Applications · 2006
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of MinnesotaNational Science Foundation
KeywordsIsoperimetric inequalityMathematicsCahn–Hilliard equationTorusMean curvatureConstant (computer programming)Mathematical analysisConvergence (economics)CurvatureMinificationApplied mathematicsMathematical optimizationGeometryComputer scienceDifferential equation

Abstract

fetched live from OpenAlex

We consider here two well-known variational problems associated with the phenomenon of phase separation: the isoperimetric problem and minimization of the Cahn-Hilliard energy. The two problems are related through a classical result in \Gamma -convergence and we explore the behavior of global and local minimizers for these problems in the periodic setting. More precisely, we investigate these variational problems for competitors defined on the flat 2 or 3 -torus. We view these two problems as prototypes for periodic phase separation. We give here a complete analysis of stable critical points of the 2 -d periodic isoperimetric problem and also obtain stable solutions to the 2 -d and 3 -d periodic Cahn-Hilliard problem. We also discuss some intriguing open questions regarding triply periodic constant mean curvature surfaces in 3 d and possible counterparts in the Cahn-Hilliard setting.

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)0.002
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.282
Teacher spread0.270 · 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

Citations40
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueInterfaces and Free Boundaries Mathematical Analysis Computation and ApplicationsSame topicBlock Copolymer Self-AssemblyFrench-language works237,207