KAN-SAE applies nonlinear per-feature B-spline activations in sparse autoencoders to discover 72% more alive climate features and interpretable patterns such as European heatwaves and Pacific typhoons in deep learning weather models.
Stormer: Pretraining language models for weather forecasting
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
AdaWeather adaptively mixes probabilistic weather forecasts and achieves logarithmic regret relative to the best static mixture of experts in hindsight.
Hybrid neural world models train one network with horizon conditioning to predict multi-horizon physical states and extract a per-trajectory error map from forward passes alone for hybrid accuracy-speed operation across PDE and rigid-body domains.
PINN-Cast combines continuous-depth Neural ODEs inside transformer blocks with a two-branch attention module and physics-informed loss to produce short-term weather forecasts that respect governing physical principles.
The work introduces WaLeF/FIDLAr for flood forecasting, CoDiCast for probabilistic weather, and Hypercube-RAG for explainable environmental QA, claiming superior accuracy, efficiency, and interpretability over baselines.
citing papers explorer
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Beyond Linear Superposition: Discovering Climate Features in AI Weather Models with KAN-SAE
KAN-SAE applies nonlinear per-feature B-spline activations in sparse autoencoders to discover 72% more alive climate features and interpretable patterns such as European heatwaves and Pacific typhoons in deep learning weather models.
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AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
AdaWeather adaptively mixes probabilistic weather forecasts and achieves logarithmic regret relative to the best static mixture of experts in hindsight.
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Hybrid Neural World Models
Hybrid neural world models train one network with horizon conditioning to predict multi-horizon physical states and extract a per-trajectory error map from forward passes alone for hybrid accuracy-speed operation across PDE and rigid-body domains.
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PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
PINN-Cast combines continuous-depth Neural ODEs inside transformer blocks with a two-branch attention module and physics-informed loss to produce short-term weather forecasts that respect governing physical principles.
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Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems
The work introduces WaLeF/FIDLAr for flood forecasting, CoDiCast for probabilistic weather, and Hypercube-RAG for explainable environmental QA, claiming superior accuracy, efficiency, and interpretability over baselines.