@inproceedings{cdfb5540a6184bd280666ad078369448,
title = "Data Assimilation in the Latent Space of a Convolutional Autoencoder",
abstract = "Data Assimilation (DA) is a Bayesian inference that combines the state of a dynamical system with real data collected by instruments at a given time. The goal of DA is to improve the accuracy of the dynamic system making its result as real as possible. One of the most popular technique for DA is the Kalman Filter (KF). When the dynamic system refers to a real world application, the representation of the state of a physical system usually leads to a big data problem. For these problems, KF results computationally too expensive and mandates to use of reduced order modeling techniques. In this paper we proposed a new methodology we called Latent Assimilation (LA). It consists in performing the KF in the latent space obtained by an Autoencoder with non-linear encoder functions and non-linear decoder functions. In the latent space, the dynamic system is represented by a surrogate model built by a Recurrent Neural Network. In particular, an Long Short Term Memory (LSTM) network is used to train a function which emulates the dynamic system in the latent space. The data from the dynamic model and the real data coming from the instruments are both processed through the Autoencoder. We apply the methodology to a real test case and we show that the LA has a good performance both in accuracy and in efficiency.",
keywords = "Convolutional autoencoder, Data assimilation, Long short term memory, Machine learning, Neural network",
author = "Maddalena Amendola and Rossella Arcucci and Laetitia Mottet and Casas, {C{\'e}sar Quilodr{\'a}n} and Shiwei Fan and Christopher Pain and Paul Linden and Guo, {Yi Ke}",
note = "Funding Information: Acknowledgements. This work is supported by the EPSRC Grand Challenge grant Managing Air for Green Inner Cities (MAGIC) EP/N010221/1, the EP/T003189/1 Health assessment across biological length scales for personal pollution exposure and its mitigation (INHALE), the EP/T000414/1 PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE) and the Leonardo Centre for Sustainable Business at Imperial College London. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 21st International Conference on Computational Science, ICCS 2021 ; Conference date: 16-06-2021 Through 18-06-2021",
year = "2021",
month = jun,
day = "9",
doi = "10.1007/978-3-030-77977-1_30",
language = "English",
isbn = "9783030779764",
series = "Lecture Notes in Computer Science",
publisher = "Springer Cham",
pages = "373--386",
editor = "Maciej Paszynski and Dieter Kranzlm{\"u}ller and Krzhizhanovskaya, {Valeria V.} and Dongarra, {Jack J.} and Sloot, {Peter M.}",
booktitle = "Computational Science – ICCS 2021",
edition = "1st",
url = "https://www.iccs-meeting.org/iccs2021/, https://link.springer.com/conference/iccs-computsci",
}