1 citations · 1 across the 6 of their papers we have counts for
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U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
Salva Rühling Cachay, Duncan Watson-Parris, Rose Yu
AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets,…
Discovering Latent Causal Graphs from Spatiotemporal Data
Kun Wang, Sumanth Varambally, Duncan Watson-Parris +2
Many important phenomena in scientific fields like climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. Infer…
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
Bohan Lyu, Yadi Cao, Duncan Watson-Parris +3
Large Language Models (LLMs) demonstrate promising capabilities in solving scientific problems but often suffer from the issue of hallucination. While integrating LLMs with tools c…
ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models
Veeramakali Vignesh Manivannan, Yasaman Jafari, Srikar Eranky +6
The use of Large Language Models (LLMs) in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation fram…
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
Ruijia Niu, Dongxia Wu, Kai Kim +3
Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian…