AI Feynman: A physics-inspired method for symbolic regression

> It seems to me like KANs should be able to find expressions like these given experimental data. Perhaps, but this is not something unique to KANs: any symbolic regression method can (at least in theory) find such simple expressions. Here is an example of such type of work (using non-KAN neural networks): https://www.science.org/doi/10.1126/sciadv.aay2631 Rephrasing: just because you can reach simple expressions with symbolic regression methods based on neural networks (or KANs) does not necessarily imply that neural networks (or KANs) are inherently interpretable (particularly once you start stacking multiple layers).

In order for that not to happen (uninterpretable ML models) some research on symbolic distillation, aka symbolic regression https://arxiv.org/abs/2006.11287 https://www.science.org/doi/10.1126/sciadv.aay2631

There's a subfield called symbolic regression that considers this problem. https://www.science.org/doi/10.1126/sciadv.aay2631 The Kepler equation is relatively simple compared to other equations re-discovered by ML.