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Record W4231092039 · doi:10.1137/17m1129477

Steady Hydrodynamic Model of Semiconductors with Sonic Boundary: (II) Supersonic Doping Profile

2018· article· en· W4231092039 on OpenAlexfundno aff
Jingyu Li, Ming Mei, Guojing Zhang, Kai‐Jun Zhang

Bibliographic record

VenueSIAM Journal on Mathematical Analysis · 2018
Typearticle
Languageen
FieldMathematics
TopicDifferential Equations and Numerical Methods
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSupersonic speedTransonicBoundary value problemEuler's formulaRelaxation (psychology)Mathematical analysisDopingEuler equationsMathematicsPhysicsMechanicsClassical mechanicsCondensed matter physicsAerodynamics

Abstract

fetched live from OpenAlex

This is the second part of our series of studies concerning the well-posedness/ill-posedness and regularity of stationary solutions to the hydrodynamic model of semiconductors represented by Euler--Poisson equations with sonic boundary condition. In this paper, we consider the case of a supersonic doping profile, and prove that the system does not hold any interior subsonic solution; furthermore, the system doesn't admit any interior supersonic solution and any transonic solution if such a supersonic doping profile is small enough or the relaxation time is small, but it has at least one interior supersonic solution and infinitely many transonic solutions if the supersonic doping profile is close to the sonic line and the relaxation time is large. The nonexistence of any type of solutions in the case of a small doping profile or small relaxation time indicates that the semiconductor effect for the system is remarkable and cannot be ignored.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.060
GPT teacher head0.344
Teacher spread0.284 · 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

Citations32
Published2018
Admission routes1
Has abstractyes

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