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

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.