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sciai.architecture.FNO2D

class sciai.architecture.FNO2D(in_channels, out_channels, resolution, modes, channels=20, depths=4, mlp_ratio=4, dtype=ms.float32)

The 2-dimensional Fourier Neural Operator (FNO2D) contains a lifting layer, multiple Fourier layers and a decoder layer. The details can be found in Fourier neural operator for parametric partial differential equations.

Parameters

• in_channels (int) – The number of channels in the input space.

• out_channels (int) – The number of channels in the output space.

• resolution (int) – The spatial resolution of the input.

• modes (int) – The number of low-frequency components to keep.

• channels (int) – The number of channels after dimension lifting of the input. Default: 20.

• depths (int) – The number of FNO layers. Default: 4.

• mlp_ratio (int) – The number of channels lifting ratio of the decoder layer. Default: 4.

• dtype (dtype.Number) – The computation type of dense. It should be ms.float16 or ms.float32. ms.float32 is recommended for the GPU backend, and ms.float16 is recommended for the Ascend backend. Default: ms.float32.

Inputs:

• x (Tensor) - Tensor of shape $(batch\mathrm{\_}size,resolution,resolution,in\mathrm{\_}channels)$.

Outputs:

Tensor, the output of this FNO network.

• output (Tensor) -Tensor of shape $(batch\mathrm{\_}size,resolution,resolution,out\mathrm{\_}channels)$.

Raises

• TypeError – If in_channels is not an int.

• TypeError – If out_channels is not an int.

• TypeError – If resolution is neither an int nor a tuple of int.

• TypeError – If modes is not an int.

• ValueError – If modes is less than 1.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindspore.common.initializer import initializer, Normal
>>> from sciai.architecture.neural_operators import FNO2D
>>> B, H, W, C = 32, 64, 64, 1
>>> x = initializer(Normal(), [B, H, W, C])
>>> net = FNO2D(in_channels=1, out_channels=1, resolution=64, modes=12)
>>> output = net(x)
>>> print(output.shape)
(32, 64, 64, 1)