`neuralop.models`.GINO

class neuralop.models.GINO(*args, **kwargs)[source]

GINO: Geometry-informed Neural Operator. Learns a mapping between functions presented over arbitrary coordinate meshes. The model carries global integration through spectral convolution layers in an intermediate latent space, as described in [1]_.

Parameters:

in_channelsint: feature dimension of input points
out_channelsint: feature dimension of output points
latent_feature_channelsint, optional: number of channels in optional latent feature map to concatenate onto latent embeddings before the FNO’s forward pass, default None
projection_channelsint, optional: number of channels in FNO pointwise projection
gno_coord_dimint, optional: geometric dimension of input/output queries, by default 3
gno_radiusfloat, optional: radius in input/output space for GNO neighbor search, by default 0.033
in_gno_transform_typestr, optional: transform type parameter for input GNO, by default ‘linear’ see neuralop.layers.gno_block for more details
out_gno_transform_typestr, optional: transform type parameter for output GNO, by default ‘linear’ see neuralop.layers.gno_block for more details
gno_pos_embed_typeliteral {‘transformer’, ‘nerf’} | None: type of optional sinusoidal positional embedding to use in GNOBlock, by default ‘transformer’
fno_in_channelsint, optional: number of input channels for FNO, by default 26
fno_n_modestuple, optional: number of modes along each dimension to use in FNO, by default (16, 16, 16)
fno_hidden_channelsint, optional: hidden channels for use in FNO, by default 64
lifting_channel_ratioint, optional: ratio of lifting channels to fno_hidden_channels, by default 2 The number of liting channels in the lifting block of the FNO is lifting_channel_ratio * hidden_channels (e.g. default 512)
fno_n_layersint, optional: number of layers in FNO, by default 4

Methods

`forward`(input_geom, latent_queries, ...[, ...])	The GINO's forward call: Input GNO --> FNOBlocks --> output GNO + projection to output queries
`latent_embedding`(in_p[, ada_in])

Other Parameters:

gno_embed_channels: int: dimension of optional per-channel embedding to use in GNOBlock, by default 32
gno_embed_max_positions: int: max positions of optional per-channel embedding to use in GNOBlock, by default 10000. If gno_pos_embed_type != ‘transformer’, value is unused.
in_gno_channel_mlp_hidden_layerslist, optional: widths of hidden layers in input GNO, by default [80, 80, 80]
out_gno_channel_mlp_hidden_layerslist, optional: widths of hidden layers in output GNO, by default [512, 256]
gno_channel_mlp_non_linearitynn.Module, optional: nonlinearity to use in gno ChannelMLP, by default F.gelu
gno_use_open3dbool, optional: whether to use open3d neighbor search, by default True if False, uses pure-PyTorch fallback neighbor search
gno_use_torch_scatterbool, optional: whether to use torch_scatter’s neighborhood reduction function or the native PyTorch implementation in IntegralTransform layers. If False, uses the fallback PyTorch version.
out_gno_tanhbool, optional: whether to use tanh to stabilize outputs of the output GNO, by default False
fno_resolution_scaling_factorfloat | None, optional: factor by which to scale output of FNO, by default None
fno_incremental_n_modeslist[int] | None, defaults to None
if passed, sets n_modes separately for each FNO layer.
fno_block_precisionstr, defaults to ‘full’: data precision to compute within fno block
fno_use_channel_mlpbool, defaults to True: Whether to use a ChannelMLP layer after each FNO block.
fno_channel_mlp_dropoutfloat, defaults to 0: dropout parameter of above ChannelMLP.
fno_channel_mlp_expansionfloat, defaults to 0.5: expansion parameter of above ChannelMLP.
fno_non_linearitynn.Module, defaults to F.gelu: nonlinear activation function between each FNO layer.
fno_stabilizernn.Module | None, defaults to None: By default None, otherwise tanh is used before FFT in the FNO block.
fno_normnn.Module | None, defaults to None: normalization layer to use in FNO.
fno_ada_in_featuresint | None, defaults to 4: if an adaptive mesh is used, number of channels of its positional embedding. If None, adaptive mesh embedding is not used.
fno_ada_in_dimint, defaults to 1: dimensions of above FNO adaptive mesh.
fno_preactivationbool, defaults to False: whether to use Resnet-style preactivation.
fno_skipstr, defaults to ‘linear’: type of skip connection to use.
fno_channel_mlp_skipstr, defaults to ‘soft-gating’: type of skip connection to use in the FNO ‘linear’: conv layer ‘soft-gating’: weights the channels of the input ‘identity’: nn.Identity
fno_separablebool, defaults to False: if True, use a depthwise separable spectral convolution.
fno_factorizationstr {‘tucker’, ‘tt’, ‘cp’} | None, defaults to None: Tensor factorization of the parameters weight to use
fno_rankfloat, defaults to 1.0: Rank of the tensor factorization of the Fourier weights.
fno_joint_factorizationbool, defaults to False: Whether all the Fourier layers should be parameterized by a single tensor (vs one per layer).
fno_fixed_rank_modesbool, defaults to False: Modes to not factorize.
fno_implementationstr {‘factorized’, ‘reconstructed’} | None, defaults to ‘factorized’: If factorization is not None, forward mode to use:: * reconstructed : the full weight tensor is reconstructed from the factorization and used for the forward pass * factorized : the input is directly contracted with the factors of the decomposition
fno_decomposition_kwargsdict, defaults to dict(): Optionaly additional parameters to pass to the tensor decomposition.
fno_conv_modulenn.Module, defaults to SpectralConv: Spectral Convolution module to use.

forward(input_geom, latent_queries, output_queries, x=None, latent_features=None, ada_in=None, **kwargs)[source]

The GINO’s forward call: Input GNO –> FNOBlocks –> output GNO + projection to output queries

Parameters:

input_geomtorch.Tensor: input domain coordinate mesh shape (1, n_in, gno_coord_dim)
latent_queriestorch.Tensor: latent geometry on which to compute FNO latent embeddings a grid on [0,1] x [0,1] x …. shape (1, n_gridpts_1, …. n_gridpts_n, gno_coord_dim)
output_queriestorch.Tensor: points at which to query the final GNO layer to get output shape (batch, n_out, gno_coord_dim)
xtorch.Tensor, optional: input function a defined on the input domain input_geom shape (batch, n_in, in_channels). Default None
latent_featurestorch.Tensor, optional: optional feature map to concatenate onto latent embedding before being passed into the latent FNO, default None if latent_feature_channels is set, must be passed
ada_intorch.Tensor, optional: adaptive scalar instance parameter, defaults to None