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

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 Analysi...

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Bibliographic Details
Published in:Computers & chemical engineering Vol. 146; p. 107202
Main Authors: Smith, Alexander D., Dłotko, Paweł, Zavala, Victor M.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.03.2021
Subjects:
Data
Engineering
Geometry
Space
Time
Topology
Space
Geometry
Engineering
Data
Time
Topology
ISSN:0098-1354, 1873-4375
Online Access:Get full text
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Summary: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.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2020.107202