A nudged-system optimization method recovers parameters in the Lorenz-63 system from partial noisy observations, with theoretical guarantees on synchronization and identifiability.
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Numerical experiments on Lorenz '63 and '96 systems indicate deterministic parameter recovery paired with deterministic data assimilation outperforms stochastic alternatives in accuracy, stability, and computational speed under white noise.
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A Data-Assimilation-Augmented Optimization Framework for Parameter Estimation in Dynamical Systems
A nudged-system optimization method recovers parameters in the Lorenz-63 system from partial noisy observations, with theoretical guarantees on synchronization and identifiability.