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Identification of nonlinear spatiotemporal systems via partitioned filtering
1Center for Dynamics of Complex Systems, University of Potsdam, 14469 Potsdam, Germany.
Summary
We developed a computationally feasible method for identifying nonlinear spatiotemporal systems using nonlinear state space filtering and state partitioning. This approach accurately estimates unobserved states and parameters from noisy experimental data.
Area of Science:
- Complex Systems
- Nonlinear Dynamics
- Data Assimilation
Background:
- Identifying nonlinear spatiotemporal systems from observational data is computationally challenging.
- Existing methods struggle with noisy and indirect measurements typical in experimental settings.
Purpose of the Study:
- To address the computational complexity of identifying nonlinear spatiotemporal systems.
- To propose a computationally feasible solution for state and parameter estimation.
Main Methods:
- Nonlinear state space filtering combined with a state partition technique.
- Application to simulated chaotic data from a reaction-diffusion system.
Main Results:
- The proposed method is computationally feasible for typical experimental spatiotemporal data.
- Accurate estimation of an unobserved state component was achieved.
- Estimation of the diffusion constant was also successful.
Conclusions:
- The developed technique offers a practical approach for analyzing complex nonlinear spatiotemporal systems.
- This method enhances the ability to extract meaningful information from noisy experimental recordings.