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Record W7109608167 · doi:10.1080/07362994.2025.2589118

Parabolic Anderson model with rough initial condition: continuity in law of the solution

2025· article· en· W7109608167 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStochastic Analysis and Applications · 2025
Typearticle
Languageen
FieldMathematics
TopicNonlinear Partial Differential Equations
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAnderson impurity modelLaw of large numbersParabolic partial differential equationStochastic processProbability theoryHeat equation

Abstract

fetched live from OpenAlex

.The parabolic Anderson model (PAM) is one of the most interesting and challenging SPDEs related to various physical phenomena and can be described mathematically as a stochastic heat equation driven by linear multiplicative noise. In this article, we consider PAM with an initial condition given by a signed Borel measure on ℝd. The forcing term under investigation is examined in two cases: (i) the regular noise, with the spatial covariance given by the Riesz kernel of order α∈(0,d) in spatial dimension d≥1; and (ii) the rough noise, which is a fractional noise in space with Hurst index H<1/2 and d = 1. In both cases, the noise is assumed to be colored in time, and we consider a general initial condition. The objective of this article is to show that the solution is continuous in law with respect to the spatial noise parameter. The similar problem for the constant initial condition has been recently studied in [Citation1 c1].

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.952
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.340
Teacher spread0.312 · 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