[CrimpCity]

This is cool and I personally believe this type of work may lead to breakthroughs in messy data rich fields like biology where we can arrive at a higher levels of abstraction maybe not exactly to "laws" like physics but highly correlative rules around phenomena. I think this is more on the side of knowledge creation and is human friendly as opposed to being more of a black box prediction like deep neural networks. Though I think both things are complimentary since human curiosity isn't satisfied by prediction alone.

If anyone else is interested in this line of work I recommend checking out Kathleen Champion, Steve Brunton, and J. Nathan Kutz's work on Discovering governing equations from data by sparse identification of nonlinear dynamical systems(https://www.pnas.org/doi/full/10.1073/pnas.1517384113).

Also this intro video is great! https://youtu.be/Z-l7G8zq8I0

[jamesakirk]

Thank you for mentioning J. Nathan Kutz! Reading through this article, I saw similarities to Dynamic Mode Decomposition (I am not literate enough on the topic to elaborate). His Coursera courses and book were a fascinating dive into orthogonal basis functions, lower-rank approximations like PCA... I'm not sharp enough anymore (over a decade since grad school) to fully grok it, but damn his work is so cool!