nickhnelsen / fourier-neural-mappings Goto Github PK

FNF (Fourier neural functional) could also be named Fourier Neural Encoder (FNE) for consistency with FND. Then Fourier Neural Network (FNN) function could be renamed Fourier Neural Function (FNF).

Can decide later.

Does the truncation of information in Fourier Neural Functionals cause issues? It takes a function and integrates it against another function with k_max Fourier modes (so only the first k_maxd Fourier modes are used). But unlike FNO layers, there is no notion of skip connection to retain the truncated modes i.e. use all the information in the input.
An alternative could be to take function $u$, map to the right number of output channels using $MLP(u(x))$, then take the mean of the resulting function to map it to a vector. Then concatenate this with an FNF layer. MLP being nonlinear is crucial here, else only the zero-th Fourier mode would be used.

So we would have
torch.cat(Lu, \int MLP(u(x)) dx),
where L is the FNF map.

na

Add argument to choose whether to use 1D or 2D spatial layers in the latent function space.

After clean PR #7 , scripts have not been tested so there are perhaps some errors in the function import paths. Need to check all.

7abbf99#r128768027

Change repo name and classes to "Functionals" instead of "Functional"?

fourier-neural-mappings

change readme to reflect this

also add conda yml environment requirement following the readme template here

acknowledgements in repo (original fno code, pyapprox, etc)

FNO2d
FNO2dto3d
FNF2d

Cleanup repo before making public: models directory, shared.py for shared layers and fucntions, etc.

https://github.com/nickhnelsen/FourierNeuralMappings/blob/95cd6260f1b89b087bbdadfc70abf468257c3c4d/models/vec_to_vec2d.py#L87

^^ The nonlinear processing at the beginning of all the architectures in vec_to_*.py files may not be necessary and hurt accuracy. We should try removing this nonlinearity to see if that helps.