Large scales in decaying turbulence repeat reliably across 30,000 trials with matched initial conditions, while small scales remain random and support statistical modeling.
The predictability of a flow which possesses many scales of motion
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A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
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On the repeatability of turbulence
Large scales in decaying turbulence repeat reliably across 30,000 trials with matched initial conditions, while small scales remain random and support statistical modeling.
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A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.