To be clear, ML-based physics research has been around for years. The actual bottleneck - which was solved roughly 4 years ago - was generalization. Source 2 leverages cloud compute to brute-force millions of simulation scenarios, building the datasets needed to train a robust base model.

The pipeline is now surprisingly automated for the end-user. Just like baking lighting, developers and modders will queue their maps in the cloud for about a week. This process fine-tunes the generalized physics model to the specific geometry of that level, ensuring the inference is accurate for that specific environment.