The following pages link to Data Assimilation (Q5261548):
Displaying 50 items.
- Efficient nonlinear optimal smoothing and sampling algorithms for complex turbulent nonlinear dynamical systems with partial observations (Q777550) (← links)
- A stochastic version of Stein variational gradient descent for efficient sampling (Q782348) (← links)
- Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques (Q783088) (← links)
- Approximate continuous data assimilation of the 2D Navier-Stokes equations via the Voigt-regularization with observable data (Q827649) (← links)
- Signal classification with a point process distance on the space of persistence diagrams (Q1630882) (← links)
- Continuous data assimilation for the 3D primitive equations of the ocean (Q1633406) (← links)
- A coherent structure approach for parameter estimation in Lagrangian data assimilation (Q1691285) (← links)
- Sequential data assimilation with multiple nonlinear models and applications to subsurface flow (Q1691889) (← links)
- Data assimilation methods for neuronal state and parameter estimation (Q1710228) (← links)
- Downscaling data assimilation algorithm with applications to statistical solutions of the Navier-Stokes equations (Q1719938) (← links)
- On one-dimensional Riccati diffusions (Q1737966) (← links)
- Nonlinear Kalman filtering for censored observations (Q1740241) (← links)
- Weak-norm posterior contraction rate of the 4DVAR method for linear severely ill-posed problems (Q1745629) (← links)
- Sequential data assimilation for 1D self-exciting processes with application to urban crime data (Q1796944) (← links)
- Global in time stability and accuracy of IMEX-FEM data assimilation schemes for Navier-Stokes equations (Q1986844) (← links)
- Multilevel ensemble Kalman filtering for spatio-temporal processes (Q1996221) (← links)
- Continuous data assimilation applied to a velocity-vorticity formulation of the 2D Navier-Stokes equations (Q2030407) (← links)
- Reproducing kernel Hilbert space compactification of unitary evolution groups (Q2036491) (← links)
- Linear Kalman-Bucy filter with vector autoregressive signal and noise (Q2038522) (← links)
- Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
- Markov chain simulation for multilevel Monte Carlo (Q2069944) (← links)
- Optimal sensor placement for joint parameter and state estimation problems in large-scale dynamical systems with applications to thermo-mechanics (Q2071419) (← links)
- Reduced basis approximation and a posteriori error bounds for 4D-Var data assimilation (Q2071421) (← links)
- A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation (Q2072658) (← links)
- Stability of non-linear filter for deterministic dynamics (Q2072662) (← links)
- Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix (Q2072672) (← links)
- Supervised learning from noisy observations: combining machine-learning techniques with data assimilation (Q2077682) (← links)
- Estimation for dynamical systems using a population-based Kalman filter -- applications in computational biology (Q2094857) (← links)
- Data-driven gradient flows (Q2100871) (← links)
- Affine-mapping based variational ensemble Kalman filter (Q2103971) (← links)
- Simulating surface height and terminus position for marine outlet glaciers using a level set method with data assimilation (Q2112450) (← links)
- Recovering the Eulerian energy spectrum from noisy Lagrangian tracers (Q2115381) (← links)
- Non-asymptotic error estimates for the Laplace approximation in Bayesian inverse problems (Q2117305) (← links)
- Gibbs posterior convergence and the thermodynamic formalism (Q2117451) (← links)
- Efficient estimation of cardiac conductivities: a proper generalized decomposition approach (Q2123851) (← links)
- Calibrate, emulate, sample (Q2123875) (← links)
- Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics (Q2124589) (← links)
- ISALT: inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems (Q2129142) (← links)
- A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings (Q2129328) (← links)
- Kernel learning backward SDE filter for data assimilation (Q2133767) (← links)
- Continuous data assimilation and long-time accuracy in a \(C^0\) interior penalty method for the Cahn-Hilliard equation (Q2139750) (← links)
- Error analysis of proper orthogonal decomposition data assimilation schemes with grad-div stabilization for the Navier-Stokes equations (Q2141571) (← links)
- Model reduction of linear dynamical systems via balancing for Bayesian inference (Q2144962) (← links)
- Model and data reduction for data assimilation: particle filters employing projected forecasts and data with application to a shallow water model (Q2147287) (← links)
- A comparison of nonlinear extensions to the ensemble Kalman filter. Gaussian anamorphosis and two-step ensemble filters (Q2147573) (← links)
- Continuous data assimilation for two-phase flow: analysis and simulations (Q2157114) (← links)
- A metric tensor approach to data assimilation with adaptive moving meshes (Q2157134) (← links)
- A multi-fidelity ensemble Kalman filter with hyperreduced reduced-order models (Q2160470) (← links)
- Continuous data assimilation for the 3D Ladyzhenskaya model: analysis and computations (Q2171193) (← links)
- A 4D-Var method with flow-dependent background covariances for the shallow-water equations (Q2172113) (← links)
