GAIA-Chem: A Framework for Global AI-Accelerated Atmospheric Chemistry Modelling-Reference-Cited by-同舟云学术

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GAIA-Chem: A Framework for Global AI-Accelerated Atmospheric Chemistry Modelling

Published:2024-06-03 Issue: Volume: Page:1-5
ISSN:
Container-title:Proceedings of the Platform for Advanced Scientific Computing Conference
language:
Short-container-title:

Author:

Adie Jeff¹²^ORCID,Chin Cheng Siong¹^ORCID,Li Jichun³^ORCID,See Simon²^ORCID

Affiliation:

1. Faculty of Science, Agriculture, and Engineering, Newcastle University, Singapore, Singapore

2. NVIDIA AI Technology Centre, NVIDIA Inc., Singapore, Singapore

3. Faculty of Science, Agriculture, and Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom

Publisher

ACM

Reference20 articles.

1. Jeff Adie, Cheng Siong Chin, Jichun Li, and Simon See. 2024. GAIANet: Accelerating Atmospheric Chemistry Modelling with Deep Learning and Fourier Neural Operators. In Supercomputing Asia 2024. Newcastle University, Sydney, Australia, 10. eprints.ncl.ac.uk/295800

2. The International GEOS-Chem User Community. 2022. GEOS-Chem. 10.5281/zenodo.4437678

3. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

4. Technical Note: Unsupervised classification of ozone profiles in UKESM1

5. Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

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