Design of the control set in the framework of variational data assimilation
From MaRDI portal
Publication:1685116
DOI10.1016/j.jcp.2016.08.029zbMath1375.93072OpenAlexW2510855569MaRDI QIDQ1685116
Pierre-Olivier Malaterre, Igor Yu. Gejadze
Publication date: 13 December 2017
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2016.08.029
automatic differentiationvariational data assimilationuncertainty quantification1D hydraulic network modelcontrol set design
Related Items (2)
A Bayesian-variational cyclic method for solving estimation problems characterized by non-uniqueness (equifinality) ⋮ On the use of derivatives in the polynomial chaos based global sensitivity and uncertainty analysis applied to the distributed parameter models
Uses Software
Cites Work
- Lagrangian data assimilation for river hydraulics simulations
- Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the antarctic ice sheet
- On optimal solution error covariances in variational data assimilation problems
- Efficient real-time reservoir management using adjoint-based optimal control and model updating
- Stochastic processes and filtering theory
- The variational Kalman filter and an efficient implementation using limited memory BFGS
- A Hessian-Based Method for Uncertainty Quantification in Global Ocean State Estimation
- Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems
- Adjoint Equations and Perturbation Algorithms in Nonlinear Problems
- MAP estimators and their consistency in Bayesian nonparametric inverse problems
- Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion
- Unnamed Item
- Unnamed Item
This page was built for publication: Design of the control set in the framework of variational data assimilation
