We recently released our ceviche package on github, which simulates electromagnetic physics using automatic differentiation. Thought it might be interesting to this community as an application of backpropagation techniques to science & engineering applications outside of ML.

https://github.com/twhughes/ceviche

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https://i.redd.it/rn724c41mjk31.png

Using automatic differentiation allows one to effortlessly differentiate the results of the simulation with respect to various design parameters defining the simulation. This allows you to do a lot of interesting things, for example:

– Perform automated, gradient-based optimization of photonic devices.

– Wrap the E&M solver in a machine learning model and do end to end training of physical hardware, like we did in this paper.

Most importantly, in contrast with what is common practice in the field of photonics, this can all be done *without* needing to do any tedious analytical calculations by hand, and one can rest assured that the derivatives are accurate and efficiently computed.

If you’re interested in some of the nitty gritty details about reverse vs. forward mode differentiation in electromagnetic simulations, check out our pre-print as well, linked here.