Journal ArticleDOI
A Reduced Order Deep Data Assimilation model
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TLDR
The RODDA framework is applied to a CFD simulation for air pollution, using the CFD software Fluidity, in South London and it is shown that the data forecasted by the coupled model CFD+RODDA are closer to the observations with a gain in terms of execution time with respect to the classic prediction–correction cycle given by coupling CFD with a standard DA.About:
This article is published in Physica D: Nonlinear Phenomena.The article was published on 2020-11-01. It has received 43 citations till now.read more
Citations
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Journal ArticleDOI
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
TL;DR: This study demonstrates that a truncated system of only two latent-space dimensions can reproduce a sharp advecting shock profile for the viscous Burgers equation with very low viscosities, and a twelve-dimensional latent space can recreate the evolution of the inviscid shallow water equations.
Journal ArticleDOI
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
TL;DR: In this paper, an encoding using convolutional autoencoders (CAEs) followed by a reduced-space time evolution by recurrent neural networks overcomes the limitation of truncation by discarding important interactions between higher-order modes during time evolution.
Journal ArticleDOI
Deep Data Assimilation: Integrating Deep Learning with Data Assimilation
TL;DR: The DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system, and it is proved that the DDA approach implies a reduction of the model error, which decreases at each iteration.
Journal ArticleDOI
Using machine learning to correct model error in data assimilation and forecast applications
TL;DR: This article proposes to use the data assimilation method to correct the error of an existent, knowledge-based model, and demonstrates numerically the feasibility of the method using a two-layer, two-dimensional quasi-geostrophic channel model.
Journal ArticleDOI
Using machine learning to correct model error in data assimilation and forecast applications
TL;DR: This article proposes a method to use this method to correct the error of an existing, knowledge‐based model and demonstrates the feasibility of the method numerically using a two‐layer, two‐dimensional, quasi‐geostrophic channel model.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Posted Content
Sequence to Sequence Learning with Neural Networks
TL;DR: This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
Journal ArticleDOI
LSTM: A Search Space Odyssey
TL;DR: This paper presents the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling, and observes that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.
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