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Kernel-based parameter estimation of dynamical systems with unknown observation functions.
Ofir Lindenbaum1, Amir Sagiv2, Gal Mishne3
1Program in Applied Mathematics, Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA.
This study introduces a new kernel-based score to estimate unknown parameters in low-dimensional dynamical systems from high-dimensional signals. The method accurately identifies system parameters using a single time-evolution measurement.
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Area of Science:
- Dynamical systems theory
- Nonlinear dynamics
- Time series analysis
Background:
- Observing low-dimensional dynamical systems through high-dimensional signals (e.g., videos) is common in experiments.
- Estimating underlying system parameters from limited observational data presents a significant challenge.
Purpose of the Study:
- To develop a method for estimating unknown parameters of a dynamical system using only a single measurement of its time-evolution.
- To address the challenge of inferring system dynamics from high-dimensional observational data.
Main Methods:
- Proposing a kernel-based score to quantify temporal inter-dependencies between observed signals and a dynamical model.
- Generalizing maximum likelihood estimation to nonlinear settings and unknown feature spaces.
- Estimating system parameters by maximizing the proposed kernel-based score.
Main Results:
- Demonstrated accuracy and efficiency in parameter estimation for chaotic dynamical systems.
- Successfully applied the method to the double pendulum and the Lorenz '63 model.
- Validated the kernel-based score's ability to capture essential temporal inter-dependencies.
Conclusions:
- The proposed kernel-based score offers an effective approach for parameter estimation in nonlinear dynamical systems.
- Single-measurement time-evolution data is sufficient for accurate parameter inference using this method.
- This technique advances the analysis of complex systems in fields like physics and engineering.