Treffer: Deep learning and variational inversion to quantify and attribute climate change (Jupyter Notebook) published in the Environmental Data Science book

Title:
Deep learning and variational inversion to quantify and attribute climate change (Jupyter Notebook) published in the Environmental Data Science book
Authors:
Domazetoski, Viktor, orcid:0000-0001-9830-, Zúñiga-González, Andrés, Allemang, Owen
Publisher Information:
Zenodo
Publication Year:
2025
Collection:
Zenodo
Subject Terms:
Atmosphere, Modelling, Special Issue, Python
Document Type:
E-Ressource software
Language:
unknown
Relation:
https://github.com/eds-book/93463cac-471a-469d-ad52-0514fd9b67f2/tree/v2025.6.0; https://zenodo.org/records/15706519; oai:zenodo.org:15706519; https://doi.org/10.5281/zenodo.15706519
DOI:
10.5281/zenodo.15706519
Availability:
https://doi.org/10.5281/zenodo.15706519
https://zenodo.org/records/15706519
Rights:
MIT License ; mit ; https://opensource.org/licenses/MIT
Accession Number:
edsbas.59868499
Database:
BASE

Weitere Informationen

Notebook developed to demonstrate the computational reproduction of the paper Detection and attribution of climate change: A deep learning and variational approach, published in Environmental Data Science journal. ; Please cite the following works when using this project.