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.
6
Pith papers citing it
fields
cs.LG 6years
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.