Parameter estimation in systems exhibiting spatially complex solutions via persistent homology and machine learning

Sabrina S. Calcina and Marcio Gameiro

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 185, issue C, 719-732

Abstract: We use persistent homology to extract topological information from complex spatio-temporal data generated by differential equations and use this information to estimate the corresponding parameters of the differential equation using regression methods in machine learning. We apply this technique to a predator–prey system and to the complex Ginzburg–Landau equation.

Keywords: Persistent homology; Persistence diagrams; Parameter estimation; Machine learning; KNeighbors; SVR (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:185:y:2021:i:c:p:719-732

DOI: 10.1016/j.matcom.2021.01.013

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