Machine Learning for Southern Ocean Overturning ...
This code repository contains a Jupyter notebook that details the neural network architectures and parameters used in our research paper. Specifically, it includes the implementation of a fully connected neural network, a linear neural network, and a convolutional neural network. The notebook also o...
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ftdatacite:10.5281/zenodo.13381355 2024-09-15T18:37:03+00:00 Machine Learning for Southern Ocean Overturning ... Meng, Shuai 2024 https://dx.doi.org/10.5281/zenodo.13381355 https://zenodo.org/doi/10.5281/zenodo.13381355 unknown Zenodo https://dx.doi.org/10.5281/zenodo.13381356 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Text ScholarlyArticle article-journal 2024 ftdatacite https://doi.org/10.5281/zenodo.1338135510.5281/zenodo.13381356 2024-09-02T09:30:46Z This code repository contains a Jupyter notebook that details the neural network architectures and parameters used in our research paper. Specifically, it includes the implementation of a fully connected neural network, a linear neural network, and a convolutional neural network. The notebook also outlines the code used for constructing the training and testing datasets, providing a clear explanation of how the data was prepared. Additionally, it contains the relevant temporal filter functions used in our analysis. The numerical simulation data is downloaded here: https://doi.org/10.5281/zenodo.6850435 ... Text Southern Ocean DataCite |
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description |
This code repository contains a Jupyter notebook that details the neural network architectures and parameters used in our research paper. Specifically, it includes the implementation of a fully connected neural network, a linear neural network, and a convolutional neural network. The notebook also outlines the code used for constructing the training and testing datasets, providing a clear explanation of how the data was prepared. Additionally, it contains the relevant temporal filter functions used in our analysis. The numerical simulation data is downloaded here: https://doi.org/10.5281/zenodo.6850435 ... |
format |
Text |
author |
Meng, Shuai |
spellingShingle |
Meng, Shuai Machine Learning for Southern Ocean Overturning ... |
author_facet |
Meng, Shuai |
author_sort |
Meng, Shuai |
title |
Machine Learning for Southern Ocean Overturning ... |
title_short |
Machine Learning for Southern Ocean Overturning ... |
title_full |
Machine Learning for Southern Ocean Overturning ... |
title_fullStr |
Machine Learning for Southern Ocean Overturning ... |
title_full_unstemmed |
Machine Learning for Southern Ocean Overturning ... |
title_sort |
machine learning for southern ocean overturning ... |
publisher |
Zenodo |
publishDate |
2024 |
url |
https://dx.doi.org/10.5281/zenodo.13381355 https://zenodo.org/doi/10.5281/zenodo.13381355 |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
https://dx.doi.org/10.5281/zenodo.13381356 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.1338135510.5281/zenodo.13381356 |
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1810481362921062400 |