Topological data analysis: Concepts, computation, and applications in chemical engineering

Topological data analysis: Concepts, computation, and applications in chemical engineering
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拓扑数据分析:化学工程中的概念、计算和应用

DOI:
10.1016/j.compchemeng.2020.107202
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发表时间:
2021
影响因子:
4.3
通讯作者:
Zavala, Victor M.
Zavala, Victor M.
中科院分区:
工程技术2区
文献类型:
--
作者:
Smith, Alexander D.;Dłotko, Paweł;Zavala, Victor M.

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推动科学和工程研究的一个主要假设是数据具有结构。描述这种结构的主要范式是统计(例如,矩、相关函数)和信号处理(例如,卷积神经网络、傅立叶级数)。拓扑数据分析(TDA)是一个从根本不同的角度分析数据的数学领域。 TDA 将数据集表示为几何对象,并提供将此类对象投影到低维描述符上的降维技术。这些描述符(也称为拓扑特征)的关键属性是它们提供多尺度信息并且在扰动(例如噪声、平移和旋转)下保持稳定。在这项工作中,我们回顾了 TDA 的关键数学概念和方法,并介绍了化学工程中的不同应用。
A primary hypothesis that drives scientific and engineering studies is that data has structure. The dominant paradigms for describing such structure are statistics (e.g., moments, correlation functions) and signal processing (e.g., convolutional neural nets, Fourier series). Topological Data Analysis (TDA) is a field of mathematics that analyzes data from a fundamentally different perspective. TDA represents datasets as geometric objects and provides dimensionality reduction techniques that project such objects onto low-dimensional descriptors. The key properties of these descriptors (also known as topological features) are that they provide multiscale information and that they are stable under perturbations (e.g., noise, translation, and rotation). In this work, we review the key mathematical concepts and methods of TDA and present different applications in chemical engineering.
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