A DNN Framework for Learning Lagrangian Drift With Uncertainty-Reference-Cited by-同舟云学术

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A DNN Framework for Learning Lagrangian Drift With Uncertainty

Published:2023-07-13 Issue:20 Volume:53 Page:23729-23739
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Jenkins Joseph,Paiement Adeline,Ourmières Yann,Le Sommer Julien,Verron Jacques,Ubelmann Clément,Glotin Hervé

Funder

ANRT

Ocean Next

Datlas

FEDER MARITTIMO GIAS

Chaire Intelligence Artificielle ADSIL

GENCI-IDRIS

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-04625-1.pdf

Reference27 articles.

1. Gill AE (1982) Atmosphere-ocean dynamics. Academic Press 30

2. Van Sebille E, Griffies SM, Abernathey R et al (2018) Lagrangian ocean analysis: Fundamentals and practices. Ocean Model 121:49–75

3. Koszalka IM, Haine TW, Magaldi MG (2013) Fates and travel times of Denmark Strait overflow water in the Irminger Basin. J Phys Oceanogr 43(12):2611–2628

4. Visser AW (2008) Lagrangian modelling of plankton motion: From deceptively simple random walks to fokker-planck and back again. J Mar Syst 70(3–4):287–299

5. LaCasce J (2008) Statistics from lagrangian observations. Prog Oceanogr 77(1):1–29

Cited by 2 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. Developing an Artificial Intelligence-Based Method for Predicting the Trajectory of Surface Drifting Buoys Using a Hybrid Multi-Layer Neural Network Model;Journal of Marine Science and Engineering;2024-06-07

2. A DNN Framework for Learning Lagrangian Drift With Uncertainty;Applied Intelligence;2023-07-13

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