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.
Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification
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A framework builds stable neural models of turbulent dynamics by enforcing energy-preserving nonlinearities and causal constraints in discrete-time flow maps, demonstrated on Charney-DeVore and Lorenz-96 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.
citing papers explorer
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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.
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Physics and causally constrained discrete-time neural models of turbulent dynamical systems
A framework builds stable neural models of turbulent dynamics by enforcing energy-preserving nonlinearities and causal constraints in discrete-time flow maps, demonstrated on Charney-DeVore and Lorenz-96 systems.
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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.