Keplearn recovers exact physics laws from raw data in milliseconds
Keplearn: Extract Equations from Data in Seconds
Keplearn is a deterministic symbolic regression engine written in Rust that reads a CSV or TSV from stdin and outputs candidate equations as JSON. It uses best-first recursive reduction to find compact closed-form laws, snapping fitted constants to rationals and multiples of pi, e, and sqrt(2). On the Feynman benchmark it achieves a 64.7% exact symbolic solution rate at zero noise, beating AIFeynman's 55.8% with far less compute. Solved cases often finish in under a second.
Keplearn is named after Johannes Kepler, who deduced his laws of planetary motion by studying tables of astronomical observations taken by Tycho Brahe. That is what this program attempts to do: given columns of measurements, find the compact law that relates them.