摘要翻译:
依赖于图灵机制的扩散驱动模式形成模型在许多科学领域都得到了应用。然而,为了预测空间模式的形成,许多此类模型都存在着需要微调参数或在系统组分的扩散中不切实际地分离尺度的缺陷。在一个非常一般的生态模式形成模型的背景下,我们表明在图灵模型中包含固有噪声导致了“准模式”的形成,这些“准模式”形成于参数空间的一般区域,在实验上可以与标准图灵模式区分。准模式的存在消除了将图灵模型应用于实际系统时对尺度进行非物理微调或分离的需要。
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英文标题:
《Fluctuation-driven Turing patterns》
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作者:
Thomas Butler, Nigel Goldenfeld
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最新提交年份:
2011
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分类信息:
一级分类:Quantitative Biology 数量生物学
二级分类:Other Quantitative Biology 其他定量生物学
分类描述:Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类:Physics 物理学
二级分类:Adaptation and Self-Organizing Systems 自适应和自组织系统
分类描述:Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应,自组织系统,统计物理,波动系统,随机过程,相互作用粒子系统,机器学习
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英文摘要:
Models of diffusion driven pattern formation that rely on the Turing mechanism are utilized in many areas of science. However, many such models suffer from the defect of requiring fine tuning of parameters or an unrealistic separation of scales in the diffusivities of the constituents of the system in order to predict the formation of spatial patterns. In the context of a very generic model of ecological pattern formation, we show that the inclusion of intrinsic noise in Turing models leads to the formation of "quasi-patterns" that form in generic regions of parameter space and are experimentally distinguishable from standard Turing patterns. The existence of quasi-patterns removes the need for unphysical fine tuning or separation of scales in the application of Turing models to real systems.
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PDF链接:
https://arxiv.org/pdf/1011.0466