Cross-Domain Scaling Validation

Background: The generalizability of empirical scaling laws is often validated within a single, domain-specific data landscape. Performance limitations in large models can arise from factors like insufficient dataset size relative to model capacity, leading to multi-epoch training and potential overfitting.

Question / Future Work: Extending the systematic neural scaling analysis beyond the ERA5 dataset to include diverse datasets from across the Earth Sciences (and potentially other scientific domains) to evaluate the robustness and applicability of the derived compute-optimal scaling laws in a broader, more complex data landscape. This is crucial for evaluating cross-domain foundation models.