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.
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Comparing Deterministic and Stochastic Parameter Recovery Algorithms Applied to Chaotic Systems
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.