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.
A unified framework for the analysis of accuracy and stability of a class of approximate Gaussian filters for the Navier-Stokes Equations
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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.