Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 372, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2020.113291
Keywords
Variational Data Assimilation; Attention networks; Convolutional Autoencoders
Funding
- EPSRC, UK Grand Challenge grant Managing Air for Green Inner Cities (MAGIC) [EP/N010221/1]
- EPSRC, UK Centre for Mathematics of Precision Healthcare [EP/N0145291/1, EP/T003189/1]
- European Union [743623, 754446]
- EPSRC [EP/T003189/1, EP/N010221/1] Funding Source: UKRI
- Marie Curie Actions (MSCA) [743623] Funding Source: Marie Curie Actions (MSCA)
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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 our proposal with data from a real-world application: a pollution model of a site in Elephant and Castle (London, UK) and found that we could (1) reduce the size of the background covariance matrix representation by O(10(3)), and (2) increase our data assimilation accuracy with respect to existing reduced space methods. (C) 2020 Elsevier B.V. All rights reserved.
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