Regarding trained model about neuraloperator HOT 8 CLOSED

And on super resolution when I test my dataset which is 6464 grid with 20 examples and 100 timesteps I get a error at
test_a = test_a.reshape(ntest,S,S,1,T_in).repeat([1,1,1,T,1]) saying shape '[20, 256, 256, 1, 10]' is invalid for input of size 819200
so is there any specification for super resolution code like for say grid 6464 we have to keep indent value this and S and T value this?
And the model you have provided on drive can I evaluate the results on any grid with any boundary conditions for naiver stokes?

from neuraloperator.

1. Yes, it could be used to model different boundary conditions. If the mesh is still 64*64, the same model should apply.
2. For super-resolution, assume we train on 64*64 and want to evaluate at 256*256, there are two scenarios: (a) if your input is 256*256, then you may directly apply the same model (that trained on 64*64). (b) if your input is still 64*64, in this case there is actually no much we can do. Since if the boundary/initial conditon of PDE is low-resolution, then the solution is under-determined at higher-resolution. You may use the code below to evaluate at a higher resolution, where the addition parameter 'size' will be 256.

    def forward(self, x, size=None):
        if size==None:
            size = x.size(-1)

        batchsize = x.shape[0]
        #Compute Fourier coeffcients up to factor of e^(- something constant)
        x_ft = torch.fft.rfftn(x, dim=[2,3])

        # Multiply relevant Fourier modes
        out_ft = torch.zeros(batchsize, self.out_channels, size, size//2 + 1, device=x.device, dtype=torch.cfloat)
        out_ft[:, :, :self.modes1, :self.modes2] = \
            compl_mul2d(x_ft[:, :, :self.modes1, :self.modes2], self.weights1)
        out_ft[:, :, -self.modes1:, :self.modes2] = \
            compl_mul2d(x_ft[:, :, -self.modes1:, :self.modes2], self.weights2)


        #Return to physical space
        x = torch.fft.irfftn(out_ft, s=(size, size), dim=[2,3])
        return x

But it's not much different to directly evaluate the solution at 64*64 and do an interpolation.

from neuraloperator.

Thanks that was useful I was getting error in eval.py script while testing for 64*64 mesh.
How do you change boundary conditions in psuedospectral method I tried changing stream function value by setting zero at points where I have to fix a wall but I think it does not work so how set boundary condition there and does setting boundary condition means also setting it in Fourier space do we have to make any changes there?
I have been comparing results of FNO with solver method right now I just took periodic boundary conditions and got results with openFoam solver. Now I have to change boundary condition so wanted to know it can be done.
I m also going try this super resolution and compare those with openFoam.

from neuraloperator.

Sorry for the delayed response! To address boundary we have two methods in mind:

1. Fourier continuation: the domain is non-perodic, but we can create a large periodic domain that contains the real domain. It is just a few line of code: for example, if my input and output is of the shape (batch, x, y, channels):
   x_in = F.pad(x, (0,0,0,5,0,5), "constant", 0) # create a large domain
   out = model(x_in).reshape(batch_size,S+5,S+5,channel)
   out = out[:,:5,:5,:] # take out the original domain

2. Multiply the output by a mask, for example to enforce a zero boundary
   x = torch.linspace(0,np.pi,64+1)[:-1]
   mask = torch.outer(torch.sin(x), torch.sin(x))
   out = model(x_in) * mask.reshape(1,64,64,1)

Thanks!

from neuraloperator.

For super resolution for 2-D mesh the script mention does not work as it is for 3-d. So what changes should be done in script for getting super resolution for 2-d mesh?

from neuraloperator.

Sorry I am not very sure I understand the issue. What is working and what is not?

from neuraloperator.

So trained a model on 6464 mesh using fourier_2D.py . Wanted to evaluate at 256256 mesh but got error. So what chnages are to be made in super resolution script. As I think the super resolution script is a 3D one.

from neuraloperator.

If the fourier_2d.py or fourier_2d_time.py model works on 64x64, I think you can directly apply it on 256x256. Just to make sure the size parameter s is updated, and your input (u0 / train_a / test_a) are 256x256.

from neuraloperator.