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

Calcina, Sabrina S.; Gameiro, Marcio

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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):	Calcina, Sabrina S. ^[1] ; Gameiro, Marcio ^[1] Total Authors: 2
Affiliation:	^[1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Caixa Postal 668, BR-66813560 Sao Carlos, SP - Brazil Total Affiliations: 1
Document type:	Journal article
Source:	MATHEMATICS AND COMPUTERS IN SIMULATION; v. 185, p. 719-732, JUL 2021.
Web of Science Citations:	0
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. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved. (AU)

FAPESP's process:	19/06249-7 - Applications of Computational and Topological Methods to Dynamical Systems
Grantee:	Marcio Fuzeto Gameiro
Support Opportunities:	Regular Research Grants

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