The following pages link to Data Assimilation (Q5898277):
Displaying 50 items.
- Application of the EnKF and localization to automatic history matching of facies distribution and production data (Q934754) (← links)
- Scaled unscented transform Gaussian sum filter: theory and application (Q968520) (← links)
- A direct inverse model to determine permeability fields from pressure and flow rate measurements (Q972669) (← links)
- On numerical properties of the ensemble Kalman filter for data assimilation (Q995315) (← links)
- A parallel time integrator for noisy nonlinear oscillatory systems (Q1640871) (← links)
- Bayesian estimation of agent-based models (Q1655642) (← links)
- A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF) (Q1663452) (← links)
- Waterflooding optimization in uncertain geological scenarios (Q1663480) (← links)
- Bayesian model selection for nonlinear aeroelastic systems using wind-tunnel data (Q1668780) (← links)
- On the efficient low cost procedure for estimation of high-dimensional prediction error covariance matrices (Q1679123) (← links)
- A new data assimilation technique based on ensemble Kalman filter and Brownian bridges: an application to Richards' equation (Q1682762) (← links)
- Optimal strategies for the control of autonomous vehicles in data assimilation (Q1691207) (← links)
- Displacement data assimilation (Q1691769) (← links)
- Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations (Q1693616) (← links)
- Localization and regularization for iterative ensemble smoothers (Q1702330) (← links)
- Gaussian processes for history-matching: application to an unconventional gas reservoir (Q1702365) (← links)
- A multiresolution ensemble Kalman filter using the wavelet decomposition (Q1702387) (← links)
- Data assimilation methods for neuronal state and parameter estimation (Q1710228) (← links)
- Identifying influence areas with connectivity analysis -- application to the local perturbation of heterogeneity distribution for history matching (Q1710278) (← links)
- A modified randomized maximum likelihood for improved Bayesian history matching (Q1710279) (← links)
- A simple numerical method based simultaneous stochastic perturbation for estimation of high dimensional matrices (Q1710944) (← links)
- Feedback control of an HBV model based on ensemble Kalman filter and differential evolution (Q1714894) (← links)
- Particle Gaussian mixture filters. I. (Q1716620) (← links)
- Distributed Gauss-Newton optimization method for history matching problems with multiple best matches (Q1785159) (← links)
- Implicit sampling, with application to data assimilation (Q1943077) (← links)
- The digital twin of discrete dynamic systems: initial approaches and future challenges (Q1988748) (← links)
- A shadowing-based inflation scheme for ensemble data assimilation (Q2000278) (← links)
- Parallel probabilistic graphical model approach for nonparametric Bayesian inference (Q2000451) (← links)
- Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source (Q2001935) (← links)
- Simulating the spread of COVID-19 \textit{via} a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion (Q2006320) (← links)
- Reduced model of macro-scale stochastic plasticity identification by Bayesian inference: application to quasi-brittle failure of concrete (Q2021054) (← links)
- Ensemble Kalman inversion: mean-field limit and convergence analysis (Q2029092) (← links)
- Balanced data assimilation for highly oscillatory mechanical systems (Q2041572) (← links)
- Supervised learning from noisy observations: combining machine-learning techniques with data assimilation (Q2077682) (← links)
- Analysis of COVID-19 in Japan with extended SEIR model and ensemble Kalman filter (Q2088868) (← links)
- Performance assessment of the maximum likelihood ensemble filter and the ensemble Kalman filters for nonlinear problems (Q2093059) (← links)
- Controlled interacting particle algorithms for simulation-based reinforcement learning (Q2107628) (← links)
- Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering (Q2114043) (← links)
- Kernel learning backward SDE filter for data assimilation (Q2133767) (← links)
- Bayesian identification of a projection-based reduced order model for computational fluid dynamics (Q2176873) (← links)
- Gaussian mixture model fitting method for uncertainty quantification by conditioning to production data (Q2185981) (← links)
- Data-space inversion with ensemble smoother (Q2192795) (← links)
- Identification of physical processes via combined data-driven and data-assimilation methods (Q2222261) (← links)
- Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients (Q2222341) (← links)
- A direct filter method for parameter estimation (Q2222556) (← links)
- Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics (Q2227162) (← links)
- A physics-constrained data-driven approach based on locally convex reconstruction for noisy database (Q2309342) (← links)
- Bayesian model selection using automatic relevance determination for nonlinear dynamical systems (Q2309843) (← links)
- Learning wind fields with multiple kernels (Q2324342) (← links)
- A heuristic reference recursive recipe for adaptively tuning the Kalman filter statistics. I: Formulation and simulation studies (Q2360156) (← links)
