Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction-Reference-Cited by-同舟云学术

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Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction

Published:2021 Issue: Volume: Page:1-14
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Meng Yuxin^ORCID,Rigall Eric^ORCID,Chen Xueen,Gao Feng^ORCID,Dong Junyu^ORCID,Chen Sheng^ORCID

Funder

National Key Research and Development Program of China

National Key Scientific Instrument and Equipment Development Projects of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09610615.pdf?arnumber=9610615

Cited by 25 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. Physics-Informed Explainable Continual Learning on Graphs;IEEE Transactions on Neural Networks and Learning Systems;2024-09

2. Deep learning for the super resolution of Mediterranean sea surface temperature fields;Ocean Science;2024-08-28

3. A Best-Fitting B-Spline Neural Network Approach to the Prediction of Advection–Diffusion Physical Fields with Absorption and Source Terms;Entropy;2024-07-04

4. Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction;Remote Sensing;2024-06-20

5. Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application;Atmospheric Research;2024-06

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