Pages that link to "Item:Q2124589"
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The following pages link to Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics (Q2124589):
Displaying 14 items.
- Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques (Q783088) (← links)
- Ensemble Kalman method for learning turbulence models from indirect observation data (Q5038553) (← links)
- Ensemble Gradient for Learning Turbulence Models from Indirect Observations (Q5065144) (← links)
- Learning the tangent space of dynamical instabilities from data (Q5205672) (← links)
- An efficient data-driven multiscale stochastic reduced order modeling framework for complex systems (Q6048418) (← links)
- A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parameterization (Q6098251) (← links)
- Combining direct and indirect sparse data for learning generalizable turbulence models (Q6107115) (← links)
- Launching Drifter Observations in the Presence of Uncertainty (Q6444907) (← links)
- Conditional Gaussian nonlinear system: a fast preconditioner and a cheap surrogate model for complex nonlinear systems (Q6563632) (← links)
- Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: a chaotic Kuramoto-Sivashinsky test case (Q6565142) (← links)
- Lagrangian descriptors with uncertainty (Q6584212) (← links)
- A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation (Q6584213) (← links)
- CGNSDE: conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation (Q6592766) (← links)
- Large-scale circulation reversals explained by pendulum correspondence (Q6609858) (← links)
