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Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

Said Ouala, Ronan Fablet, Cédric Herzet, Bertrand Chapron, Ananda Pascual, Fabrice Collard, Lucile Gaultier
2018 Remote Sensing  
Overall, the key features of the proposed approach are two-fold: (i) we propose a novel architecture for the stochastic representation of two dimensional (2D) geophysical dynamics based on a neural networks  ...  From this point of view, the recent advances in machine learning and especially neural networks and deep learning can provide a new infrastructure for dynamical modeling and interpolation within a data-driven  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs10121864 fatcat:ytgmlxee5fejlerlhs3vb52iui

Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flows [article]

Suraj Pawar, Omer San
2021 arXiv   pre-print
To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems.  ...  In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parameterizations of unresolved flow dynamics and formulate a data assimilation  ...  model as the dynamical core of the system, and a data-driven model to describe unresolved physics.  ... 
arXiv:2006.08901v2 fatcat:zjbpqigta5ej5polsbzoflp62u

A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations [article]

Suraj Pawar, Omer San, Adil Rasheed, Ionel M. Navon
2021 arXiv   pre-print
The successful synergistic integration of neural network and data assimilation for low-dimensional system shows the potential benefits of the proposed hybrid-neural physics model for complex systems.  ...  In this work, we propose machine learning to account for missing physics and then data assimilation to correct the prediction.  ...  Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness  ... 
arXiv:2104.00114v2 fatcat:22fbu7hw55ga7lyiee3ie2iwg4

Artificial Neural Network for Data Assimilation by WRF Model in Rio de Janeiro, Brazil

Vinícius Albuquerque de Almeida, Gutemberg Borges França, Haroldo Fraga Campos Velho, Nelson F. Favilla Ebecken
2020 Revista Brasileira de Geofisica  
This study investigates the use of neural networks for data assimilation of local data in the WRF model in Rio de Janeiro, Brazil.  ...  Periods of 168h from 2014 and 2015 are used with 6h and 12h assimilation cycles for surface and upper-air data, respectively.  ...  of Geophysics, 38(2),2020 ARTIFICIAL NEURAL NETWORK FOR DATA ASSIMILATION BY WRF MODEL IN RIO DE JANEIRO, BRAZIL Brazilian Journal of Geophysics, 38(2),2020  ... 
doi:10.22564/rbgf.v38i2.2042 fatcat:2bjapggbizfhjov4kdvsgif7ye

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows

Suraj Pawar, Shady E. Ahmed, Omer San, Adil Rasheed, Ionel M. Navon
2020 Physics of Fluids  
Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows but often require a set of ensemble forward simulations to estimate forecast covariance.  ...  Furthermore, our approach relies on the power of archival data, and the trained model can be retrained effectively due to the power of transfer learning in any neural network applications.  ...  DATA ASSIMILATION METHODOLOGY The central goal of DA is to extract the information from observational data to correct dynamical models and improve their prediction.  ... 
doi:10.1063/5.0012853 fatcat:5bufvwmphbevzp7aolirinhnti

Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering

Fangxin Fang
2021 Water  
This special issue aims at exploring advanced numerical techniques for real-time prediction and optimal management in coastal and hydraulic engineering [...]  ...  Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ...  Numerical simulations of fluid dynamics have been indispensable in many applications relevant to physics and engineering.  ... 
doi:10.3390/w13040509 fatcat:bel6v3tr5feifj2hhncw7fcf6e

PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations [article]

Olivier Pannekoucke, Ronan Fablet
2020 arXiv   pre-print
As an illustration, the workflow is first presented for the 2D diffusion equation, then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.  ...  In the spirit of physics-informed NNs, PDE-NetGen package provides new means to automatically translate physical equations, given as PDEs, into neural network architectures.  ...  ) and ANR through programs EUR Isblue, Melody and OceaniX.  ... 
arXiv:2002.01029v1 fatcat:cff6ivaisrdpfb5hmlfj7rr5je

PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations

Olivier Pannekoucke, Ronan Fablet
2020 Geoscientific Model Development  
As an illustration, the workflow is first presented for the 2D diffusion equation, then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.  ...  In the spirit of physics-informed neural networks (NNs), the PDE-NetGen package provides new means to automatically translate physical equations, given as partial differential equations (PDEs), into neural  ...  The UML class diagram has been generated from UMLlet (Auer et al., 2003) . Review statement. This paper was edited by Adrian Sandu and reviewed by two anonymous referees.  ... 
doi:10.5194/gmd-13-3373-2020 fatcat:ijm4rzyegbgxxfphwxa7n33bb4

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows [article]

Suraj Pawar, Shady E. Ahmed, Omer San, Adil Rasheed, Ionel M. Navon
2020 arXiv   pre-print
Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast covariance.  ...  Furthermore, our approach relies on the power of archival data and the trained model can be retrained effectively due to power of transfer learning in any neural network applications.  ...  The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.  ... 
arXiv:2005.11296v1 fatcat:fnzodjd3xfg7ni7ppgaj5coizm

Hyperparameter Search using Genetic Algorithm for Surrogate Modeling of Geophysical Flows [article]

Suraj Pawar, Omer San, Gary G. Yen
2022 arXiv   pre-print
The computational models for geophysical flows are computationally very expensive to employ in multi-query tasks such as data assimilation, uncertainty quantification, and hence surrogate models sought  ...  Researchers have started applying machine learning algorithms, particularly neural networks, to build data-driven surrogate models for geophysical flows.  ...  The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.  ... 
arXiv:2201.02389v1 fatcat:mesfx2y22vgnrbxo2o7pemjsna

Deep Data Assimilation: Integrating Deep Learning with Data Assimilation

Rossella Arcucci, Jiangcheng Zhu, Shuang Hu, Yi-Ke Guo
2021 Applied Sciences  
In particular, a recurrent neural network, trained with the state of the dynamical system and the results of the DA process, is applied for this purpose.  ...  In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML).  ...  Acknowledgments: This work is supported by the EP/T000414/1 PREdictive Modelling with Quan-tIfication of UncERtainty for MultiphasE Systems (PREMIERE) and the EP/T003189/1 Health assessment across biological  ... 
doi:10.3390/app11031114 fatcat:ta6sklksvbh33exwpjwxpl2yiy

Data Assimilation in the Latent Space of a Neural Network [article]

Maddalena Amendola, Rossella Arcucci, Laetitia Mottet, Cesar Quilodran Casas, Shiwei Fan, Christopher Pain, Paul Linden, Yi-Ke Guo
2020 arXiv   pre-print
We use a Convolutional neural network to reduce the dimensionality of the problem, a Long-Short-Term-Memory to build a surrogate model of the dynamic system and an Optimal Interpolated Kalman Filter to  ...  The accuracy of the model, that represent a dynamic system, is improved integrating real data coming from sensors using Data Assimilation techniques.  ...  Acknowledgments This work is supported by the EPSRC Grand Challenge grant Managing Air for Green Inner Cities (MAGIC) EP/N010221/1 and the EP/T003189/1 Health assessment across biological length scales  ... 
arXiv:2012.12056v1 fatcat:amta5kceqrgnferlfja4cswqoy

A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications

Wing Kam Liu, George Karniakis, Shaoqiang Tang, Julien Yvonnet
2019 Computational Mechanics  
Data assimilation is not a new field and has been going on for years in the geophysics community but less so in computational mechanics.  ...  Deep learning provides multiple opportunities of fusing data and simulations in a seamless manner creating a new paradigm in the form of physics informed learning machines.  ... 
doi:10.1007/s00466-019-01741-z fatcat:aon3rff43bgdbfjmow7r2csmza

Remote sensing from the infrared atmospheric sounding interferometer instrument 2. Simultaneous retrieval of temperature, water vapor, and ozone atmospheric profiles

F. Aires
2002 Journal of Geophysical Research  
A principal component analysis neural network using this a guess information is developed here to retrieve simultaneously temperature, water vapor and ozone atmospheric profiles.  ...  The performance of the resulting fast and accurate inverse model is evaluated with a climatological data set including rare events: temperature is retrieved with an error 1 K, and total amount of water  ...  This is due to the fact that the neural network simulates the inverse of the radiative transfer equation globally, once and for all, and uses the distribution P(x) for this purpose.  ... 
doi:10.1029/2001jd001591 fatcat:fsx6wzqtqrdn5pd5asqngktosu

Learning Variational Data Assimilation Models and Solvers

R. Fablet, B. Chapron, L. Drumetz, E. Mmin, O. Pannekoucke, F. Rousseau
2021 Journal of Advances in Modeling Earth Systems  
Data assimilation is at the core of a wide range of applications, including operational ones, with the aim to make the most of available observation data sets, including for instance both in situ and satellite-derived  ...  In earth science, the reconstruction of the dynamics of a given state or process from a sequence of partial and noisy observations is referred to as a data assimilation issue.  ...  It benefited from HPC and GPU resources from Azure (Microsoft EU Ocean awards) and from GENCI-IDRIS (Grant 2020-101030).  ... 
doi:10.1029/2021ms002572 fatcat:kt3jrxqugnhvfioqurngf7vvly
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