.png){kind=icon}
返回
Discovering multiscale and self-similar structure with data-driven wavelets
.png){kind=icon}
OA
AI
DOI：10.1073/pnas.2021299118.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many materials, processes, and structures in science and engineering have important features at multiple scales of time and/or space; examples include biological tissues, active matter, oceans, networks, and images. Explicitly extracting, describing, and defining such features are difficult tasks, at least in part because each system has a unique set of features. Here, we introduce an analysis method that, given a set of observations, discovers an energetic hierarchy of structures localized in scale and space. We call the resulting basis vectors a data-driven wavelet decomposition. We show that this decomposition reflects the inherent structure of the dataset it acts on, whether it has no structure, structure dominated by a single scale, or structure on a hierarchy of scales. In particular, when applied to turbulence-a high-dimensional, nonlinear, multiscale process-the method reveals self-similar structure over a wide range of spatial scales, providing direct, model-free evidence for a century-old phenomenological picture of turbulence. This approach is a starting point for the characterization of localized hierarchical structures in multiscale systems, which we may think of as the building blocks of these systems.
Keyword:
wavelet
multiscale
data-driven decomposition
machine learning
turbulence
期刊
P
IF:
9.1
论文数:
10.8W
被引数:
73.5W
机构
引用论文
.png){kind=icon}
Dissipation, enstrophy and pressure statistics in turbulence simulations at high Reynolds number
PREAI
分享收藏分享收藏Low power gas discharge plasma mediated inactivation and removal of biofilms formed on biomaterials
OAAI
OAAI分享收藏
查看更多内容