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.
| Original language | American English |
|---|---|
| Pages (from-to) | 719-732 |
| Number of pages | 14 |
| Journal | Mathematics and Computers in Simulation |
| Volume | 185 |
| DOIs | |
| State | Published - Jul 2021 |
| Externally published | Yes |
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science
- Numerical Analysis
- Modeling and Simulation
- Applied Mathematics
Keywords
- KNeighbors
- Machine learning
- Parameter estimation
- Persistence diagrams
- Persistent homology
- SVR
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