tdma-practical-session

Practical session at the summer school "TDMA"

https://github.com/cerea-daml/tdma-practical-session

Science Score: 67.0%

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    Found 1 DOI reference(s) in README
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    Links to: zenodo.org
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    Low similarity (6.8%) to scientific vocabulary
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Repository

Practical session at the summer school "TDMA"

Basic Info
  • Host: GitHub
  • Owner: cerea-daml
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 21.5 KB
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  • Watchers: 1
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  • Open Issues: 0
  • Releases: 1
Created almost 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

Introduction to surrogate modelling in the geosciences

Marc Bocquet¹ marc.bocquet@enpc.fr and Alban Farchi¹ alban.farchi@enpc.fr

(1) CEREA, École des Ponts and EdF R&D, IPSL, Île-de-France, France

DOI

During this session, we will apply standard machine learning methods to learn the dynamics of the Lorenz 1996 model. The objective here is to get a preview of how machine learning can be applied to geoscientific models in a low-order models where testing is quick.

These practical sessions are part of the TDMA summer school held in 2023 in Grenoble, France.

Installation

Install conda, for example through miniconda or through mamba.

Clone the repertory:

$ git clone git@github.com:cerea-daml/tdma-practical-session.git

Go to the repertory. Once there, create a dedicated anaconda environment for the sessions:

$ conda env create -f environment.yaml

Activate the newly created environment:

$ conda activate tdma

Open the notebook (e.g. with Jupyter) and follow the instructions:

$ jupyter-notebook questions.ipynb

Owner

  • Name: CEREA DA-ML team
  • Login: cerea-daml
  • Kind: organization
  • Location: Paris, France

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Farchi"
  given-names: "Alban"
  orcid: "https://orcid.org/0000-0002-4162-8289"
- family-names: "Bocquet"
  given-names: "Marc"
title: "Introduction to surrogate modelling in the geosciences."
version: 1.0.0
doi: 10.5281/zenodo.10479132
date-released: 2024-01-10
url: "https://github.com/cerea-daml/tdma-practical-session"

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Dependencies

environment.yaml pypi
  • tensorflow *