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Data Assimilation in the Latent Space of a Convolutional Autoencoder
Maddalena Amendola
, Rossella Arcucci
*
, Laetitia Mottet
, César Quilodrán Casas
, Shiwei Fan
, Christopher Pain
, Paul Linden
, Yi Ke Guo
*
Corresponding author for this work
Office of the Vice-President (Research and Development)
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
19
Citations (Scopus)
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Keyphrases
Data Assimilation
100%
Convolutional Autoencoder
100%
System Dynamics
100%
Latent Space
100%
Kalman Filter
75%
Autoencoder
50%
Linear Encoder
50%
Latent Assimilation
50%
Popular
25%
Real-world Application
25%
Recurrent Neural Network
25%
Physical Systems
25%
Dynamical Systems
25%
Bayesian Inference
25%
Dynamic Model
25%
Modeling Techniques
25%
Big Data Challenges
25%
Surrogate Model
25%
Real Tests
25%
Long Short-term Memory Network
25%
Reduced Order Modeling
25%
Mathematics
Kalman Filtering
100%
Autoencoder
100%
Real Data
66%
Nonlinear
66%
Neural Network
33%
Dynamical System
33%
Dynamic Model
33%
Bayesian Inference
33%
Physical System
33%
Lstm
33%
Big Data
33%