Optimal reduced space for variational data assimilation

DOI10.1016/j.jcp.2018.10.042OpenAlexW2900304936WikidataQ128962177 ScholiaQ128962177MaRDI QIDQ2169496

Laetitia Mottet, Rossella Arcucci, Christopher C. Pain, Yi Ke Guo

Publication date: 2 September 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2018.10.042

zbMATH Keywords

preconditioningregularisationvariational data assimilationbig datafluidity

Mathematics Subject Classification ID

Mathematical programming (90Cxx) Geophysics (86Axx) Numerical methods for mathematical programming, optimization and variational techniques (65Kxx)

Related Items (8)

A long short-term memory embedding for hybrid uplifted reduced order models ⋮ Variational Gaussian process for optimal sensor placement. ⋮ Numerical linear algebra in data assimilation ⋮ Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems ⋮ \textit{FastSVD-ML-ROM}: a reduced-order modeling framework based on machine learning for real-time applications ⋮ Attention-based convolutional autoencoders for 3D-variational data assimilation ⋮ A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping ⋮ Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models

Uses Software

• L-BFGS
• UTV
• ROMS

Cites Work

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• Conditioning and preconditioning of the variational data assimilation problem
• On the limited memory BFGS method for large scale optimization
• On the variational data assimilation problem solving and sensitivity analysis
• Numerical Experience with Limited-Memory Quasi-Newton and Truncated Newton Methods
• Deblurring Images
• Comparison of advanced large-scale minimization algorithms for the solution of inverse ill-posed problems
• Algorithm 778: L-BFGS-B
• An Analysis of a Hybrid Optimization Method for Variational Data Assimilation

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