REVIEW 1 cited by
FourCastNeXt: Optimizing FourCastNet Training for Limited Compute
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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
Forward citations
Cited by 1 Pith paper
-
ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting
ArchesWeatherGen, a flow-matching model trained on residuals of a deterministic transformer, generates ensemble forecasts that outperform IFS ENS and NeuralGCM on most WeatherBench headline variables at 1.5 degrees re...
Discussion (0). Continue with ORCID to comment.