[OisinMoran]

This is really neat—thanks for sharing! I love the general idea of making materials more "self-aware" or inspectable. It's very sci fi!

The research I did before my current job touched ever so slightly on this too, so even cooler to see it on the front page. What we were doing was using complex valued neural nets to learn the transmission matrix of an optical fibre. It was previously done in the optics community by propagating Maxwell's equations, but we were able to beat the state of the art by a few orders of magnitude with a very simple architecture (the actual physics just boils down to a single complex matrix multiplication!). The connection to your work here is that if the fibre is bent you have to relearn a new matrix. It could even be possible to learn some parameterized characterisation of the fibre, so you could say do some input/output measurements and use that to model a spline of the fibre. We did not get that far though!

Here are the papers if you're interested:

CS-focussed one: https://papers.nips.cc/paper_files/paper/2018/hash/148510031...

Physics-focussed one: https://www.nature.com/articles/s41467-019-10057-8

[spiderxxxx]

could it detect a twist instead of a bend?

[OisinMoran]

Interesting! I'm not sure to be honest. I imagine in practice it's hard to get a pure twist without any bending, and if the system can't detect any difference with a twist then the need to detect it is also nullified as it is effectively the same system.

[wcrossbow]

Maybe with polarization maintaining fiber? If at the gap they are aligned a teist will make them more perpendicular reducing mode coupling.

Disclaimer: I know nothing about this field but have spent a lot of time in a dark lab surrounded by various types of optical fiber.