Connection between sensitivity algorithm and gradient calculation of the loss function

Specific Domains Machine Learning
1

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 :blush: :thinking:

2

It’s doing that automatically.

The default is a lot more complicated. With 2 parameters, it probably automatically switched to forward sensitivities.

3

Thank you for your help, but I still need some information to understand. If I add a sensealg to my prediction, what does it do?

4

It overrides the default and chooses which sensitivity method to use.

5

To calculate the gradients of my loss function at every data point?

6

yes

7

Thank you, now I finally get it! Sorry for the clumsy language, I am not very fluent in english