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Machine learning with data assimilation and uncertainty quantification
  for dynamical systems: a review

Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

18 March 2023
Sibo Cheng
César Quilodrán-Casas
Said Ouala
A. Farchi
Che Liu
P. Tandeo
Ronan Fablet
Didier Lucor
Bertrand Iooss
J. Brajard
Dunhui Xiao
T. Janjić
Weiping Ding
Yike Guo
A. Carrassi
Marc Bocquet
Rossella Arcucci
    AI4CE
ArXivPDFHTML

Papers citing "Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review"

21 / 21 papers shown
Title
Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
Gérome Andry
François Rozet
Sacha Lewin
Omer Rochman
Victor Mangeleer
Matthias Pirlet
Elise Faulx
Marilaure Grégoire
Gilles Louppe
AI4Cl
61
0
0
25 Apr 2025
Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
Siddharth Rout
Eldad Haber
Stéphane Gaudreault
AI4TS
AI4CE
55
0
0
15 Mar 2025
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation
Chuanqi Chen
Nan Chen
Yinling Zhang
Jin-Long Wu
AI4CE
30
2
0
26 Oct 2024
Deep learning surrogate models of JULES-INFERNO for wildfire prediction
  on a global scale
Deep learning surrogate models of JULES-INFERNO for wildfire prediction on a global scale
Sibo Cheng
Hector Chassagnon
M. Kasoar
Yike Guo
Rossella Arcucci
24
2
0
30 Aug 2024
Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
Melissa Adrian
Daniel Sanz-Alonso
Rebecca Willett
26
3
0
21 May 2024
Explainable Global Wildfire Prediction Models using Graph Neural
  Networks
Explainable Global Wildfire Prediction Models using Graph Neural Networks
Dayou Chen
Sibo Cheng
Jinwei Hu
M. Kasoar
Rossella Arcucci
31
4
0
11 Feb 2024
Observation Error Covariance Specification in Dynamical Systems for Data
  assimilation using Recurrent Neural Networks
Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks
Sibo Cheng
Mingming Qiu
19
22
0
11 Nov 2021
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
258
7,337
0
11 Nov 2021
Enhancing Computational Fluid Dynamics with Machine Learning
Enhancing Computational Fluid Dynamics with Machine Learning
Ricardo Vinuesa
Steven L. Brunton
AI4CE
98
351
0
05 Oct 2021
Auto-differentiable Ensemble Kalman Filters
Auto-differentiable Ensemble Kalman Filters
Yuming Chen
D. Sanz-Alonso
Rebecca Willett
19
33
0
16 Jul 2021
Attention-based Convolutional Autoencoders for 3D-Variational Data
  Assimilation
Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation
Julian Mack
Rossella Arcucci
Miguel Molina-Solana
Yi-Ke Guo
3DPC
45
34
0
06 Jan 2021
Adversarially trained LSTMs on reduced order models of urban air
  pollution simulations
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
César Quilodrán-Casas
Rossella Arcucci
Christopher C. Pain
Yike Guo
33
7
0
05 Jan 2021
An autoencoder-based reduced-order model for eigenvalue problems with
  application to neutron diffusion
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion
Toby R. F. Phillips
C. Heaney
Paul N. Smith
Christopher C. Pain
18
56
0
15 Aug 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain
  Decomposition
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
E. Kharazmi
Zhongqiang Zhang
George Karniadakis
113
503
0
11 Mar 2020
Model error covariance estimation in particle and ensemble Kalman
  filters using an online expectation-maximization algorithm
Model error covariance estimation in particle and ensemble Kalman filters using an online expectation-maximization algorithm
T. Cocucci
M. Pulido
M. Lucini
P. Tandeo
28
11
0
04 Mar 2020
Machine Learning for Stochastic Parameterization: Generative Adversarial
  Networks in the Lorenz '96 Model
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
D. Gagne
H. Christensen
A. Subramanian
A. Monahan
AI4CE
BDL
33
137
0
10 Sep 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,635
0
05 Dec 2016
A Bayesian adaptive ensemble Kalman filter for sequential state and
  parameter estimation
A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation
Jonathan R. Stroud
Matthias Katzfuss
C. Wikle
31
55
0
11 Nov 2016
Convolutional LSTM Network: A Machine Learning Approach for
  Precipitation Nowcasting
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Xingjian Shi
Zhourong Chen
Hao Wang
Dit-Yan Yeung
W. Wong
W. Woo
201
7,816
0
13 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,042
0
06 Jun 2015
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
132
3,260
0
09 Jun 2012
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