A new probabilistic framework maps noisy time series to Hopf normal form parameters and reconstructs sensitivity functions via state-space modeling and complex Gaussian processes, with improved robustness on van der Pol benchmarks.
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Data-driven inference of Hopf normal form representations from oscillatory time series
A new probabilistic framework maps noisy time series to Hopf normal form parameters and reconstructs sensitivity functions via state-space modeling and complex Gaussian processes, with improved robustness on van der Pol benchmarks.