A new study , led by Aakash Sane, introduces a two step method to improve how ocean surface mixing is represented in models. First, neural networks predict vertical diffusivity while respecting key physical constraints. Then, symbolic regression converts these predictions into simple equations that match the neural network accuracy but are easier to interpret. The resulting formulas reveal how friction velocity, buoyancy flux, Earth’s rotation and boundary layer depth shape mixing and expose a flaw in the standard physics based scheme. This approach provides a transparent, efficient and physically grounded way to model ocean vertical mixing.