A reduced adjoint approach to variational data assimilation
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Publication:465707
DOI10.1016/j.cma.2012.10.003zbMath1297.93053OpenAlexW2039064215MaRDI QIDQ465707
Ibrahim Hoteit, M. El Gharamti, M. Umer Altaf, Arnold W. Heemink
Publication date: 24 October 2014
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2012.10.003
Related Items (9)
Feasibility of DEIM for retrieving the initial field via dimensionality reduction ⋮ Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation ⋮ Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems ⋮ POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation ⋮ Numerical linear algebra in data assimilation ⋮ Accelerating inverse inference of ensemble Kalman filter via reduced-order model trained using adaptive sparse observations ⋮ Reduced order modeling for accelerated Monte Carlo simulations in radiation transport ⋮ First-order and second-order adjoint methods for parameter identification problems with an application to the elasticity imaging inverse problem ⋮ A variational approach for parameter estimation based on balanced proper orthogonal decomposition
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