When an AI searches for the equation behind your data, one dial steers it
Our lab trained a model to rediscover physics equations from raw data. We wanted to know which training dial actually controls how hard it searches. The simplicity penalty does. A popular clipping knob barely does.
Picture a table of numbers. One column is how far a pendulum swings, another is how long each swing takes, and a third is the answer you care about. Somewhere behind those numbers is a short formula that produced them. A human physicist might stare at the table and guess that the swing time grows with the square root of the pendulum's length. The goal of this project was to get a machine to make that kind of guess on its own: read the data, propose a compact equation, and check whether it matches the law that really generated the numbers.