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Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation
- Source :
- Computer Methods in Applied Mechanics and Engineering 372 (2020) 113291
- Publication Year :
- 2021
-
Abstract
- We propose a new 'Bi-Reduced Space' approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders. We prove that our approach has the same solution as previous methods but has significantly lower computational complexity; in other words, we reduce the computational cost without affecting the data assimilation accuracy. We tested the new method with data from a real-world application: a pollution model of a site in Elephant and Castle, London and found that we could reduce the size of the background covariance matrix representation by O(10^3) and, at the same time, increase our data assimilation accuracy with respect to existing reduced space methods.<br />Comment: Published in Computer Methods in Applied Mechanics and Engineering in Dec 2020
Details
- Database :
- arXiv
- Journal :
- Computer Methods in Applied Mechanics and Engineering 372 (2020) 113291
- Publication Type :
- Report
- Accession number :
- edsarx.2101.02121
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1016/j.cma.2020.113291