Recover the symbolic equations behind a time series - deterministic, reproducible, no neural net.
Discovering the governing equations behind measured dynamics is usually done with opaque trained models that are not reproducible and offer no built-in residual check.
This plugin runs SINDy in the browser - finite-difference derivatives, a polynomial-plus-trig candidate library, and sequentially thresholded least squares - to read off the symbolic equations deterministically, with a residual-based trust verdict.
Run discovery in the browser and save the structured result to Sandbox, attach it to a Worklog engagement, or route it into a Gate client portal. Nothing is uploaded to anyone's cloud.