Topological Data Analysis: Concepts, Computation, and Applications in Chemical Engineering

TL;DR

This paper reviews Topological Data Analysis (TDA), a mathematical approach that captures data structure through geometric features, demonstrating its stability and usefulness in chemical engineering applications.

Contribution

It provides a comprehensive overview of TDA concepts, computation methods, and diverse applications specifically in chemical engineering.

Findings

TDA identifies persistent topological features across scales.

TDA methods are robust to noise and data perturbations.

Applications demonstrate TDA's effectiveness in chemical engineering problems.

Abstract

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 spaces that are composed of elementary geometric objects. Key property of these elementary objects (also known as topological features) are that they persist at different scales and that they are stable under perturbations (e.g., noise, stretching, twisting, and bending). In this work, we review key mathematical concepts and methods of TDA and present different applications in chemical engineering.