I’m new to Julia and ML and try to understand the basics.
I have defined an ODE problem with 2 parameters. This is used for the prediction in my loss function. This loss function is now used in turn to optimize the parameters of my ODE using DiffEqFlux.sciml_train (loss, … BFGS (), …).
Now I have discovered the local sensitivity analysis in the documentation:
but didn’t quite understand what is happening.
Can I use the sensitivity algorithm to determine how the gradient of my loss function is calculated?
I understood that the sciml_train function uses zygote and reverse-mode AD by default.
Or does the sensitivity analysis not refer to the loss function?
Any help is highly appreciated