Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration
A new way to discover equations hidden in messy, real-world data
Researchers created SORT, a technique that learns mathematical equations directly from noisy, irregularly collected measurements by representing patterns in a carefully chosen coordinate system. Unlike existing methods that pick from fixed libraries of equations, SORT first captures the underlying structure as a compact mathematical expansion, then uses that to find simpler analytic forms—and works better when the usual equation libraries are incomplete or misleading.
Scientists constantly need to reverse-engineer equations from experimental data—from climate models to drug metabolism to mechanical systems. SORT handles the messy realities of real measurements (noise, gaps, sampling errors) better than existing tools, and shifts the burden from brittle trial-and-error selection to intentional design of the mathematical framework. The same learned representation also enables fast approximation and calculation of complex integrals, making it useful across system modeling, prediction, and simulation.