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Record W4210686825 · doi:10.1214/21-aihp1161

Stochastic heat equation with general rough noise

2022· article· fr· W4210686825 on OpenAlexaff
Yaozhong Hu, Xiong Wang

Bibliographic record

VenueAnnales de l Institut Henri Poincaré Probabilités et Statistiques · 2022
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Nous étudions une équation de chaleur stochastique ā une dimension spatiale non linéaire dirigée par le bruit gaussien : ∂u∂t=∂2u∂x2+σ(u)W˙, où W˙ est blanc dans le temps et fractionnaire dans le espace avec le paramètre Hurst H∈(14,12). Dans un article récent (Ann. Probab. 45 (2017) 4561–616) par Hu, Huang, Lê, Nualart et Tindel une condition technique et inhabituelle σ(0)=0 a été supposée, ce qui est critique dans leur approche. Le principal effort de ce document est de supprimer cette condition. L’idée est de travailler sur un espace pondéré Zλ,Tp pour un certain poids de décroissance polynomiale λ(x)=cH(1+|x|2)H−1. Lorsque σ(u)=1 nous obtenons les asympotiques exactes de la solution uadd(t,x) quand t et x tendent vers l’infini. En particulier, nous trouvons la croissance exacte de sup|x|≤L|uadd(t,x)| et la croissance exacte des coefficients de Hölder, c’est-à-dire, sup|x|≤L|uadd(t,x+h)−uadd(t,x)| |h|β et sup|x|≤L|uadd(t+τ,x)−uadd(t,x)| τα.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.275
Teacher spread0.233 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations9
Published2022
Admission routes1
Has abstractyes

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