<i>"Their process begins by taking the derivatives of every variable observed with respect to every other -- a mathematical way of measuring how one quantity changes as another changes.</i>"<p>In an ideal world, that could be done. But am I the only one who is wondering where noise comes in? All measurements are noisy, and computing derivatives from noisy data sounds sketchy.<p><i>"Then the computer creates equations at random using various constants and variables from the data. It tests these against the known derivatives, keeps the equations that come closest to predicting correctly, modifies them at random and tests again, repeating until it literally evolves a set of equations that accurately describe the behavior of the real system. Technically, the computer does not output equations, but finds "invariants" -- mathematical expressions that remain true all the time, from which human insights can derive equations."</i><p>Once again, if the data is noisy, then the program can't find true invariants. Perhaps it can "guess" them. I would like to see that source code and the data sets these guys have used.