Please forgive my ignorance and lack of experience: I am asking this question seeking answer from the experts/experienced persons in the field.

I have a training dataset where each sample is a 3D cube (x,y,t) with grid points of (Nx = 256, Ny = 256, Nt = 200). The input consists of spatio-temporal evolution of a specific wave propagation. However, the input is also corrupted by other unwanted wave modes and noise components. I need to build a neural network (possibly convolutional auto-encoder or something similar) that can provide 3D output cube where the target wave propagation is mapped in space and time, effectively denoised of the unwanted wave modes and noises.

My question is: should I consider a 3D convolutional neural network for this task? Or, since each (x,y) frames are stored in a collection of a time series from t(1) to t(n) in the 3D cube, should I try a Recurrent 2D convolutional neural network for this job? Is there any advantage of trying one over the other?

Eventually I have to do this for 4D cube (x,y,z,t) with grid points of (Nx = 256, Ny = 256, Nz = 100, Nt = 200). In that case, would a Recurrent 3D convolutional neural network be better than 4D convolutional neural network?

If you have any literature in mind regarding this, please share. Thanks in advance.


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