Fine-tuning neural PDE operators to regime endpoints reveals a physical direction in weight space that CCM uses to compose accurate merged models for new or extrapolated regimes from metadata or short prefixes.
Neural general circulation models for weather and climate.Nature, 632(8027):1060–1066
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AOT-POT adaptively reshapes complex PDE solution operators via input-dependent transformations and parallel stream mixing to enable effective large-scale pre-training, yielding SOTA results on 12 benchmarks with minimal added parameters.
QuantWeather is an end-to-end dual-head neural network that produces calibrated quantile-based probabilistic forecasts for subseasonal precipitation, achieving higher skill and lower inference cost than ensemble methods requiring post-hoc calibration.
citing papers explorer
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Discovering Physical Directions in Weight Space: Composing Neural PDE Experts
Fine-tuning neural PDE operators to regime endpoints reveals a physical direction in weight space that CCM uses to compose accurate merged models for new or extrapolated regimes from metadata or short prefixes.
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AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training
AOT-POT adaptively reshapes complex PDE solution operators via input-dependent transformations and parallel stream mixing to enable effective large-scale pre-training, yielding SOTA results on 12 benchmarks with minimal added parameters.
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QuantWeather: Quantile-Aware Probabilistic Forecasting for Subseasonal Precipitation
QuantWeather is an end-to-end dual-head neural network that produces calibrated quantile-based probabilistic forecasts for subseasonal precipitation, achieving higher skill and lower inference cost than ensemble methods requiring post-hoc calibration.