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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Main Author: Meng, Shuai
Format: Text
Language:unknown
Published: Zenodo 2024
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.13381355
https://zenodo.org/doi/10.5281/zenodo.13381355
id ftdatacite:10.5281/zenodo.13381355
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spelling 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
institution Open Polar
collection DataCite
op_collection_id ftdatacite
language unknown
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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