Scaling Other Dimensions

Background: Research in machine learning, particularly in Scientific Machine Learning (SciML), seeks to understand how model performance scales with model size, data quantity, and computational resources.

Question / Future Work: Exploring the impact of scaling other dimensions, such as fixed patch size or internal transformer resolution (sequence length), on neural scaling laws for weather emulation. This involves systematically varying these factors across controlled scaling regimes.