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:

https://diffeq.sciml.ai/stable/analysis/sensitivity/

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