L. Ruby Leung

Imagine an interpreter in a huge hall who hears not only the person next to them but also a whisper in the far corner, and from those fragments reconstructs the entire conversation. That's how the CAS-Canglong neural network works: it picks up connections between pressure, wind, and heat across the whole planet and uses them to predict what the ocean will look like three months from now.

A student learned physics from a textbook and is taking an exam. The first two problems are solved brilliantly, but on the third, they err on something not covered in the book. That is roughly what the hybrid atmospheric model NeuralGCM faced when run through three levels of testing: cyclones, El Niño, and anomalous warming.