The topology of a chaotic attractor in the Kuramoto-Sivashinsky equation.

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED Chaos Pub Date : 2025-01-01 DOI:10.1063/5.0237476
Marie Abadie, Pierre Beck, Jeremy P Parker, Tobias M Schneider
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Abstract

The Birman-Williams theorem gives a connection between the collection of unstable periodic orbits (UPOs) contained within a chaotic attractor and the topology of that attractor, for three-dimensional systems. In certain cases, the fractal dimension of a chaotic attractor in a partial differential equation (PDE) is less than three, even though that attractor is embedded within an infinite-dimensional space. Here, we study the Kuramoto-Sivashinsky PDE at the onset of chaos. We use two different dimensionality-reduction techniques-proper orthogonal decomposition and an autoencoder neural network-to find two different mappings of the chaotic attractor into three dimensions. By finding the image of the attractor's UPOs in these reduced spaces and examining their linking numbers, we construct templates for the branched manifold, which encodes the topological properties of the attractor. The templates obtained using two different dimensionality reduction methods are equivalent. The organization of the periodic orbits is identical and consistent symbolic sequences for low-period UPOs are derived. While this is not a formal mathematical proof, this agreement is strong evidence that the dimensional reduction is robust, in this case, and that an accurate topological characterization of the chaotic attractor of the chaotic PDE has been achieved.

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Kuramoto-Sivashinsky方程中混沌吸引子的拓扑。
对于三维系统,Birman-Williams定理给出了包含在混沌吸引子中的不稳定周期轨道(UPOs)集合与该吸引子的拓扑之间的联系。在某些情况下,偏微分方程(PDE)中混沌吸引子的分形维数小于3,即使该吸引子嵌入在无限维空间中。在这里,我们研究混沌开始时的Kuramoto-Sivashinsky PDE。我们使用两种不同的降维技术-适当的正交分解和自编码器神经网络-来找到混沌吸引子在三维中的两种不同映射。通过在这些约简空间中找到吸引子的upo的映像并检查它们的连接数,我们构造了分支流形的模板,该模板编码了吸引子的拓扑性质。采用两种不同降维方法得到的模板是等效的。周期轨道的组织是相同的,并导出了低周期upo的一致符号序列。虽然这不是一个正式的数学证明,但这种一致性是强有力的证据,证明在这种情况下,降维是鲁棒的,并且已经实现了混沌PDE的混沌吸引子的精确拓扑表征。
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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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