The last thing we need to do is to see our results.
Just for this method, I will just I want to write the name of it.
It's basically this is it's name limited memory.
Limited memory.
Broyden-fletcher-goldfarb-shanno this the the name.
So this is the method and.
Yeah.
So the one thing I want is, um.
The last thing is see the results, because there are many ways to see it.
But I will just use the simplest way, which is we just write d d e dot save plot.
A lost history.
And trains that.
It is safe equals true.
Basically.
We'll put it in your file.
ISO.
ISO plot.
It's our results equals also true.
Shift enter so we can see how the loss is reducing.
And of course, also the train loss and the test loss.
So test means that testing points is like validation in deep neural network.
It is, actually.
So basically here what you can see is that first is the sine wave.
This is the input of our like our like the computation.
And this is the initial condition.
And then we have the boundary condition of this is two and.
This point will be two and this point will be zero.
This is the boundary condition.
And as time goes so as the time goes, the actual computation of the U.
Values is going to converge.
So this is basically the results.
And it's actually makes sense because.
The things will have to diffuse towards the boundary condition that we define.