Now after we.
It will define the network.
We define the initial initial condition.
It will be random, the boundary condition and of course, why it's initial condition we didn't care
about because it's a steady state.
If we want to define the initial condition, this is okay.
But because it's a steady state problem, we don't really care if it's just defined as a random.
So in order to train the network, what we need to do is we start by model defining the model.
We want to d e.
Dot model.
It will have the data and will have the network.
This one.
Of course, the data is this one.
What we defined here and the network is this one.
And.
Model.
Dot com compile.
Pyle and the first Adam.
We will use the same methods.
Both.
Knee to the polls, minus three.
Shift into a mode.
Uh, this is a capital letter.
More.
Yeah.
Now is okay so this one now.
We will have lost history.
History.
Train State.
And model dot train with epochs of.
10,000.
It will take this time a little bit longer than before.
And we need a.
This is the same thing.
Like, we will start with Adam and after Adam we need to do the other method.
So.
I'll just.
I will not start this one.
It will take like the e dot of to.
And this is compiled already.
Start up my Zeus dot.
Config.
Dot set.
L.
P.
F.
G.
L.
P.
If these options.
This is just max iteration.
To set basically the max iteration.
3000.
Max iteration.
And model dot compile.
And slash bfgs the same method before and we need to do the same here.
But we don't have to consider this one because we already define it here.
Epok.
No need.
And.
Yeah, we can save.
Like the values.
Dot save.
Plot.
Uh, just lost history and strained.
Nisei save equals false.
Pulse.
Is plot.
Because I don't really want to plot them.
Yeah.
Okay.
So this is basically we defined the model, we compile it using Adam.
We will train it once we push this one shift enter.
It's now training and after that training it will go to optimizers and will um, well basically use
the limited memory this, this one LPF optimizer and it will have a.
Maximum iteration of 3000 and that's it.
And shift enter after this.
It will train with that.
We already finished training our module.
Well, that's it.
Let's wait.
And once it's finished, we will go to the post-processing and we will see our results that hopefully
it will converge.