FourCastNeXt: Optimizing FourCastNet Training for Limited Compute
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911806547558400 |
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| author | Guo, Edison Ahmed, Maruf Sun, Yue Yang, Rui Cook, Harrison Leeuwenburg, Tennessee Evans, Ben |
| author_facet | Guo, Edison Ahmed, Maruf Sun, Yue Yang, Rui Cook, Harrison Leeuwenburg, Tennessee Evans, Ben |
| contents | FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements. This technical report presents strategies for model optimization that maintain similar performance as measured by the root-mean-square error (RMSE) of the modelled variables. By providing a model with very low comparative training costs, FourCastNeXt makes Neural Earth System Modelling much more accessible to researchers looking to conduct training experiments and ablation studies. FourCastNeXt training and inference code are available at https://github.com/nci/FourCastNeXt |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05584 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FourCastNeXt: Optimizing FourCastNet Training for Limited Compute Guo, Edison Ahmed, Maruf Sun, Yue Yang, Rui Cook, Harrison Leeuwenburg, Tennessee Evans, Ben Computer Vision and Pattern Recognition Artificial Intelligence FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements. This technical report presents strategies for model optimization that maintain similar performance as measured by the root-mean-square error (RMSE) of the modelled variables. By providing a model with very low comparative training costs, FourCastNeXt makes Neural Earth System Modelling much more accessible to researchers looking to conduct training experiments and ablation studies. FourCastNeXt training and inference code are available at https://github.com/nci/FourCastNeXt |
| title | FourCastNeXt: Optimizing FourCastNet Training for Limited Compute |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.05584 |