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Record W4308441578 · doi:10.48550/arxiv.2006.00401

Optimal decay rates of the compressible Euler equations with time-dependent damping in $\mathbb R^n$: (I) under-damping case

2020· preprint· en· W4308441578 on OpenAlexfundno aff
Shanming Ji, Mei Ming

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldMathematics
TopicNavier-Stokes equation solutions
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaMcGill University
KeywordsPhysicsLambdaMathematical physicsCombinatoricsQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

This paper is concerned with the multi-dimensional compressible Euler equations with time-dependent damping of the form $-\fracμ{(1+t)^λ}ρ\boldsymbol u$ in $\mathbb R^n$, where $n\ge2$, $μ>0$, and $λ\in[0,1)$. When $λ>0$ is bigger, the damping effect time-asymptotically gets weaker, which is called under-damping. We show the optimal decay estimates of the solutions such that $\|\partial_x^α(ρ-1)\|_{L^2(\mathbb R^n)}\approx (1+t)^{-\frac{1+λ}{2}(\frac{n}{2}+|α|)}$, and $\|\partial_x^α\boldsymbol u\|_{L^2(\mathbb R^n)}\approx (1+t)^{-\frac{1+λ}{2}(\frac{n}{2}+|α|)-\frac{1-λ}{2}}$, and see how the under-damping effect influences the structure of the Euler system. Different from the traditional view that the stronger damping usually makes the solutions decaying faster, here surprisingly we recognize that the weaker damping with $0\leλ<1$ enhances the faster decay for the solutions. The adopted approach is the technical Fourier analysis and the Green function method. The main difficulties caused by the time-dependent damping lie in twofold: non-commutativity of the Fourier transform of the linearized operator precludes explicit expression of the fundamental solution; time-dependent evolution implies that the Green matrix $G(t,s)$ is not translation invariant, i.e., $G(t,s)\ne G(t-s,0)$. We formulate the exact decay behavior of the Green matrices $G(t,s)$ with respect to $t$ and $s$ for both linear wave equations and linear hyperbolic system, and finally derive the optimal decay rates for the nonlinear Euler system.

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)0.002
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0020.002
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 head0.250
GPT teacher head0.277
Teacher spread0.027 · 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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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