In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@inproceedings{Amendola:2021:10.1007/978-3-030-77977-1_30,
author = {Amendola, M and Arcucci, R and Mottet, L and Casas, CQ and Fan, S and Pain, C and Linden, P and Guo, YK},
doi = {10.1007/978-3-030-77977-1_30},
pages = {373--386},
title = {Data Assimilation in the Latent Space of a Convolutional Autoencoder},
url = {http://dx.doi.org/10.1007/978-3-030-77977-1_30},
year = {2021}
}
TY - CPAPER
AB - 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.
AU - Amendola,M
AU - Arcucci,R
AU - Mottet,L
AU - Casas,CQ
AU - Fan,S
AU - Pain,C
AU - Linden,P
AU - Guo,YK
DO - 10.1007/978-3-030-77977-1_30
EP - 386
PY - 2021///
SN - 0302-9743
SP - 373
TI - Data Assimilation in the Latent Space of a Convolutional Autoencoder
UR - http://dx.doi.org/10.1007/978-3-030-77977-1_30
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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