Pages that link to "Item:Q1626167"
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The following pages link to Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation (Q1626167):
Displaying 14 items.
- Non-linear wave data assimilation with an ANN-type wind-wave model and ensemble Kalman filter (EnKF) (Q988959) (← links)
- A new data assimilation technique based on ensemble Kalman filter and Brownian bridges: an application to Richards' equation (Q1682762) (← links)
- Attention-based convolutional autoencoders for 3D-variational data assimilation (Q2020890) (← links)
- A multi-fidelity ensemble Kalman filter with hyperreduced reduced-order models (Q2160470) (← links)
- Combined state and parameter estimation in level-set methods (Q2222638) (← links)
- Model order reduction for real-time data assimilation through extended Kalman filters (Q2310084) (← links)
- Efficient characterization of uncertain model parameters with a reduced-order ensemble Kalman filter (Q2875012) (← links)
- A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates (Q4997361) (← links)
- An efficient data-driven multiscale stochastic reduced order modeling framework for complex systems (Q6048418) (← links)
- State estimation in nonlinear parametric time dependent systems using tensor train (Q6071444) (← links)
- A reduced basis ensemble Kalman method (Q6082208) (← links)
- A reduced order with data assimilation model: theory and practice (Q6100085) (← links)
- Efficient deep data assimilation with sparse observations and time-varying sensors (Q6198151) (← links)
- Multi-domain encoder-decoder neural networks for latent data assimilation in dynamical systems (Q6595866) (← links)
