Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review-Reference-Cited by-同舟云学术

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review

Published:2023-06 Issue:6 Volume:10 Page:1361-1387
ISSN:2329-9266
Container-title:IEEE/CAA Journal of Automatica Sinica
language:
Short-container-title:IEEE/CAA J. Autom. Sinica

Author:

Cheng Sibo¹,Quilodrán-Casas César¹,Ouala Said²,Farchi Alban³,Liu Che¹,Tandeo Pierre²,Fablet Ronan²,Lucor Didier⁴,Iooss Bertrand⁵,Brajard Julien⁶,Xiao Dunhui⁷,Janjic Tijana⁸,Ding Weiping⁹,Guo Yike¹,Carrassi Alberto¹⁰,Bocquet Marc³,Arcucci Rossella¹

Affiliation:

1. Data Science Institute, Imperial College London,Department of Computing,London,UK,SW7 2AZ

2. IMT Atlantique, UMR CNRS 6285,Lab-STICC,France

3. CEREA, École des Ponts and EDF R&D,île-de-France,France

4. CNRS, Paris-Saclay University,Laboratoire Interdisciplinaire des Sciences du Numérique,Orsay,France,F-91403

5. Electricité de France (EDF),Chatou,France,78401

6. Sorbonne University,Paris,France

7. School of Mathematical Sciences, Tongji University,Shanghai,China,200092

8. Mathematical Institute for Machine Learning and Data Science, KU Eichstaett-Ingolstadt,Bavaria,Germany

9. School of Information Science and Technology, Nantong University,Nantong,China,226019

10. University of Bologna,Department of Physics and Astronomy “Augusto Righi”,Bologna,Italy,40124

Funder

EPSRC

Fundamental Research Funds for the Central Universities

DFG

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Information Systems,Control and Systems Engineering,Control and Optimization

Link

http://xplorestaging.ieee.org/ielx7/6570654/10141540/10141545.pdf?arnumber=10141545

Reference310 articles.

1. Data Assimilation for Chaotic Dynamics

2. Learning latent dynamics for partially observed chaotic systems

3. Particle filters for high‐dimensional geoscience applications: A review

4. Ensemble Kalman methods: A mean field perspective;calvello;ArXiv Preprint,2022

Cited by 56 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions;Computer Physics Communications;2025-01

2. Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint;Computer Methods in Applied Mechanics and Engineering;2024-12

3. Reliability based optimisation of composite plates under aeroelastic constraints via adapted surrogate modelling and genetic algorithms;Composite Structures;2024-11

4. CGNSDE: Conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation;Computer Physics Communications;2024-11

5. Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems;Computer Methods in Applied Mechanics and Engineering;2024-10