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Record W2583127447 · doi:10.1137/16m108121x

The Stability and Slow Dynamics of Localized Spot Patterns for the 3-D Schnakenberg Reaction-Diffusion Model

2017· article· en· W2583127447 on OpenAlexfundno aff
Justin C. Tzou, Shuangquan Xie, Théodore Kolokolnikov, M. J. Ward

Bibliographic record

VenueSIAM Journal on Applied Dynamical Systems · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaPacific Institute for the Mathematical Sciences
KeywordsEigenvalues and eigenvectorsPhysicsBounded functionOmegaSingularityMathematical analysisInstabilityOrder (exchange)Mathematical physicsCombinatoricsMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

On a bounded three-dimensional domain $\Omega$, a hybrid asymptotic-numerical method is employed to analyze the existence, linear stability, and slow dynamics of localized quasi-equilibrium multispot patterns of the Schnakenberg activator-inhibitor model with bulk feed-rate $A$ in the singularly perturbed limit of small diffusivity $\varepsilon^2$ of the activator component. By approximating each spot as a Coulomb singularity, a nonlinear system of equations is formulated for the strength of each spot. To leading order in $\varepsilon$, two types of solutions are identified: symmetric patterns for which all strengths are identical, and asymmetric patterns for which each strength takes on one of two distinct values. The $\mathcal{O}(\varepsilon)$ correction to the strengths is found to depend on the spatial configuration of the spots through a certain Neumann Green's matrix $\mathcal{G}$. When $\mathbf{e} = (1,\dots,1)^T$ is not an eigenvector of $\mathcal{G}$, a detailed numerical and (in the case of two spots) asymptotic characterization is performed for the resulting imperfection-sensitive bifurcation structure. For symmetric multispot patterns, a leading-order global threshold in terms of $|\Omega|$ and parameters of the Schnakenberg model is obtained, below which a competition instability is triggered leading to the annihilation of one or more spots. A corresponding refined threshold is established in terms of eigenvalues of $\mathcal{G}$ in the special case when $\mathcal{G}\mathbf{e} = k\mathbf{e}$. Additionally, a local self-replication threshold for the strength of each spot is derived numerically, above which a spot splits into two. By examining $\mathcal{O}(\varepsilon)$ corrections to spot strengths, a prediction is made as to which spot will be next to split as $A$ is slowly tuned. When the pattern is stable to $\mathcal{O}(1)$ instabilities, it is shown that the locations of spots in a quasi-equilibrium configuration evolve on a long $\mathcal{O}(\varepsilon^{-3})$ time-scale according to an ODE system characterized by a gradient flow of a certain discrete energy $\mathcal{H}$, the minima of which define stable equilibrium points of the ODE. The theory also illustrates that new equilibrium points can be created when $A = A(\mathbf{x})$ is spatially variable, and that finite-time pinning away from minima of $\mathcal{H}$ can occur when $A(\mathbf{x})$ is localized. The theory for linear stability and slow dynamics when $\Omega$ is the unit ball are compared favorably to numerical solutions of the Schnakenberg PDE.

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)0.001
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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