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The last thing we need to do is to see our results.

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Just for this method, I will just I want to write the name of it.

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It's basically this is it's name limited memory.

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Limited memory.

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Broyden-fletcher-goldfarb-shanno this the the name.

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So this is the method and.

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Yeah.

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So the one thing I want is, um.

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The last thing is see the results, because there are many ways to see it.

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But I will just use the simplest way, which is we just write d d e dot save plot.

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A lost history.

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And trains that.

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It is safe equals true.

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Basically.

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We'll put it in your file.

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ISO.

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ISO plot.

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It's our results equals also true.

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Shift enter so we can see how the loss is reducing.

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And of course, also the train loss and the test loss.

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So test means that testing points is like validation in deep neural network.

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It is, actually.

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So basically here what you can see is that first is the sine wave.

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This is the input of our like our like the computation.

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And this is the initial condition.

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And then we have the boundary condition of this is two and.

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This point will be two and this point will be zero.

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This is the boundary condition.

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And as time goes so as the time goes, the actual computation of the U.

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Values is going to converge.

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So this is basically the results.

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And it's actually makes sense because.

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The things will have to diffuse towards the boundary condition that we define.