Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation

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 witho...

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Bibliographic Details

Published in:	Computer methods in applied mechanics and engineering Vol. 372; p. 113291
Main Authors:	Mack, Julian, Arcucci, Rossella, Molina-Solana, Miguel, Guo, Yi-Ke
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2020
Subjects:	Attention networks Convolutional Autoencoders Variational Data Assimilation Convolutional Autoencoders Attention networks Variational Data Assimilation
ISSN:	0045-7825, 1879-2138
Online Access:	Get full text
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