Need Diffractor.jl for State-of-the-Art Deep Learning Model

[ChrisRackauckas]

@oxinabox

So I write my loss function as as the sum of the squared difference both of bb and nn at x , and of their derivatives bb' and nn' at x

If NN is N → 1 sized, then you want to do reverse mode for the x values per x in the loss function, but the n train doing reverse mode once for the theta. That’s then a reason for reverse-over-reverse. Then for the second derivative, N->1 becomes N->N after the first, in that case you then want to do forward-over-reverse-over-reverse. This is exactly the PINN reasons BTW.