基于路径的扩散模型的模式形成:线性稳定性分析和渐近保持方法

IF 1.9 4区 数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Multiscale Modeling & Simulation Pub Date : 2023-03-15 DOI:10.1137/22m1490958
Yaming Zhang, Ning Jiang, Jiangyan Liang, Yi-Long Luo, Min Tang
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引用次数: 0

摘要

我们研究了基于路径的扩散模型(PBDM)的线性稳定性分析,该模型表征了工程大肠杆菌群体的动态[X]。薛,薛C.,唐M., PLoS computing。医学杂志。生态学报,14(2018),[1006178]。这种稳定性分析考虑了密度和化学浓度在两个非平凡稳态附近的小扰动,并将线性化方程转化为广义特征值问题。通过形式化分析,当内部变量对外部信号的响应足够快时,PBDM收敛为各向异性扩散模型,此时内部变量的概率密度分布成为一个δ函数。我们引入了PBDM的渐近保持格式,该格式收敛到与各向异性扩散模型一致的稳定极限格式。进一步的数值模拟验证了线性稳定性分析的理论结果,即模式的形成和AP方案的收敛性。
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Pattern Formation of a Pathway-Based Diffusion Model: Linear Stability Analysis and an Asymptotic Preserving Method
We investigate the linear stability analysis of a pathway-based diffusion model (PBDM), which characterizes the dynamics of the engineered Escherichia coli populations [X. Xue, C. Xue, and M. Tang, PLoS Comput. Biol., 14 (2018), e1006178]. This stability analysis considers small perturbations of the density and chemical concentration around two nontrivial steady states, and the linearized equations are transformed into a generalized eigenvalue problem. By formal analysis, when the internal variable responds to the outside signal fast enough, the PBDM converges to an anisotropic diffusion model, for which the probability density distribution in the internal variable becomes a delta function. We introduce an asymptotic preserving (AP) scheme for the PBDM that converges to a stable limit scheme consistent with the anisotropic diffusion model. Further numerical simulations demonstrate the theoretical results of linear stability analysis, i.e., the pattern formation, and the convergence of the AP scheme.
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来源期刊
Multiscale Modeling & Simulation
Multiscale Modeling & Simulation 数学-数学跨学科应用
CiteScore
2.80
自引率
6.20%
发文量
45
审稿时长
6-12 weeks
期刊介绍: Centered around multiscale phenomena, Multiscale Modeling and Simulation (MMS) is an interdisciplinary journal focusing on the fundamental modeling and computational principles underlying various multiscale methods. By its nature, multiscale modeling is highly interdisciplinary, with developments occurring independently across fields. A broad range of scientific and engineering problems involve multiple scales. Traditional monoscale approaches have proven to be inadequate, even with the largest supercomputers, because of the range of scales and the prohibitively large number of variables involved. Thus, there is a growing need to develop systematic modeling and simulation approaches for multiscale problems. MMS will provide a single broad, authoritative source for results in this area.
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