.. _api_ref:
#############
API reference
#############
:mod:`neuralop`: Neural Operators in Python
.. automodule:: neuralop.models
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.. _neuralop_models_ref:
======
Models
======
In :mod:`neuralop.models`, we provide neural operator models you can directly use on your applications.
.. _fno_api:
FNO
---
We provide a general Fourier Neural Operator (FNO) that supports most usecases.
It works for any dimension, which is inferred based on `n_modes`
(a tuple with the number of modes to keep in the Fourier domain for each dimension.)
.. autosummary::
:toctree: generated
:template: class.rst
FNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _tfno_api:
Tensorized FNO (TFNO)
---------------------
.. autosummary::
:toctree: generated
:template: class.rst
TFNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _sfno_api:
Spherical Fourier Neural Operators (SFNO)
-----------------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
SFNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _gino_api:
Geometry-Informed Neural Operators (GINO)
-----------------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
GINO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _local_no_api:
Local Neural Operators (LocalNO)
--------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
LocalNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _uno_api:
U-shaped Neural Operators (U-NO)
--------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
UNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _rno_api:
Recurrent Neural Operators (RNO)
--------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
RNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _otno_api:
Optimal Transport Neural Operators (OTNO)
-----------------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
OTNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _uqno_api:
Uncertainty Quantification Neural Operators (UQNO)
--------------------------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
UQNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _fnogno_api:
Fourier/Geometry Neural Operators (FNOGNO)
------------------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
FNOGNO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _codano_api:
Codomain Attention Neural Operators (CODANO)
--------------------------------------------
.. autosummary::
:toctree: generated
:template: class.rst
CODANO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. raw:: html
.. _neuralop_layers_ref:
======
Layers
======
.. automodule:: neuralop.layers
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In addition to the full architectures, we also provide
in :mod:`neuralop.layers` building blocks,
in the form of PyTorch layers, that you can use to build your own models:
.. _fno_blocks_api:
FNO Blocks
----------
.. automodule:: neuralop.layers.fno_block
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.. autosummary::
:toctree: generated
:template: class.rst
FNOBlocks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _rno_block_api:
RNO Blocks
----------
.. automodule:: neuralop.layers.rno_block
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.. autosummary::
:toctree: generated
:template: class.rst
RNOBlock
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _spectral_conv_api:
Fourier Convolutions
--------------------
.. automodule:: neuralop.layers.spectral_convolution
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.. autosummary::
:toctree: generated
:template: class.rst
SpectralConv
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _spherical_conv_api:
Spherical Convolutions
----------------------
.. automodule:: neuralop.layers.spherical_convolution
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.. autosummary::
:toctree: generated
:template: class.rst
SphericalConv
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _gno_api:
Graph convolutions and kernel integration
-----------------------------------------
.. automodule:: neuralop.layers.gno_block
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.. autosummary::
:toctree: generated
:template: class.rst
GNOBlock
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. automodule:: neuralop.layers.integral_transform
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.. autosummary::
:toctree: generated
:template: class.rst
IntegralTransform
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _local_no_blocks_api:
Local NO Blocks
---------------
.. automodule:: neuralop.layers.local_no_block
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.. autosummary::
:toctree: generated
:template: class.rst
LocalNOBlocks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _diff_conv_api:
Local Integral/Differential Convolutions
----------------------------------------
.. automodule:: neuralop.layers.differential_conv
:no-members:
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.. autosummary::
:toctree: generated
:template: class.rst
FiniteDifferenceConvolution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _disco_conv_api:
Discrete-Continuous (DISCO) Convolutions
----------------------------------------
.. automodule:: neuralop.layers.discrete_continuous_convolution
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.. autosummary::
:toctree: generated
:template: class.rst
DiscreteContinuousConv2d
DiscreteContinuousConvTranspose2d
EquidistantDiscreteContinuousConv2d
EquidistantDiscreteContinuousConvTranspose2d
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Codomain Attention (Transformer) Blocks
---------------------------------------
.. automodule:: neuralop.layers.coda_layer
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.. autosummary::
:toctree: generated
:template: class.rst
CODALayer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Channel MLP
-----------
.. automodule:: neuralop.layers.channel_mlp
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.. autosummary::
:toctree: generated
:template: class.rst
ChannelMLP
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Embeddings
----------
Apply positional embeddings as additional channels on a function:
.. automodule:: neuralop.layers.embeddings
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.. autosummary::
:toctree: generated
:template: class.rst
GridEmbeddingND
GridEmbedding2D
SinusoidalEmbedding
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Neighbor search
---------------
Find neighborhoods on arbitrary coordinate meshes:
.. automodule:: neuralop.layers.neighbor_search
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.. autosummary::
:toctree: generated
:template: class.rst
NeighborSearch
.. autosummary::
:toctree: generated
:template: function.rst
native_neighbor_search
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Domain Padding
--------------
.. automodule:: neuralop.layers.padding
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.. autosummary::
:toctree: generated
:template: class.rst
DomainPadding
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Skip Connections
----------------
.. automodule:: neuralop.layers.skip_connections
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.. autosummary::
:toctree: generated
:template: function.rst
skip_connection
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Normalization Layers
--------------------
.. automodule:: neuralop.layers.normalization_layers
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.. autosummary::
:toctree: generated
:template: class.rst
AdaIN
InstanceNorm
BatchNorm
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Complex-value Support
---------------------
.. automodule:: neuralop.layers.complex
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.. autosummary::
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:template: class.rst
ComplexValued
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. raw:: html
=================
Model Dispatching
=================
We provide a utility function to create model instances from a configuration.
It has the advantage of doing some checks on the parameters it receives.
.. automodule:: neuralop.models.base_model
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.. autosummary::
:toctree: generated
:template: function.rst
get_model
available_models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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========
Training
========
We provide functionality that automates the boilerplate code associated with
training a machine learning model to minimize a loss function on a dataset:
.. automodule:: neuralop.training
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.. autosummary::
:toctree: generated
:template: class.rst
Trainer
IncrementalFNOTrainer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Optimizers
----------
.. automodule:: neuralop.training.tensorgrad
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.. autosummary::
:toctree: generated
:template: class.rst
TensorGRaD
TensorGRaDProjector
UnstructuredSparseProjector
.. autosummary::
:toctree: generated
:template: function.rst
fno_tensorgrad_param_groups
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Training Utilities
------------------
.. automodule:: neuralop.training.torch_setup
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.. autosummary::
:toctree: generated
:template: function.rst
setup
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Multi-Grid Patching
-------------------
.. automodule:: neuralop.training.patching
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.. autosummary::
:toctree: generated
:template: class.rst
MultigridPatching2D
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. raw:: html
==============
Loss Functions
==============
Data Losses
-----------
.. automodule:: neuralop.losses.data_losses
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.. autosummary::
:toctree: generated
:template: class.rst
LpLoss
H1Loss
HdivLoss
PointwiseQuantileLoss
MSELoss
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Equation Losses
---------------
Physics-informed loss functions:
.. automodule:: neuralop.losses.equation_losses
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.. autosummary::
:toctree: generated
:template: class.rst
BurgersEqnLoss
ICLoss
PoissonInteriorLoss
PoissonBoundaryLoss
PoissonEqnLoss
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Meta Losses
-----------
Meta-losses for weighting composite loss functions.
.. automodule:: neuralop.losses.meta_losses
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.. autosummary::
:toctree: generated
:template: class.rst
WeightedSumLoss
Relobralo
SoftAdapt
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Differentiation
---------------
Numerical differentiation utilities:
.. automodule:: neuralop.losses.differentiation
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.. autosummary::
:toctree: generated
:template: class.rst
FourierDiff
FiniteDiff
.. autosummary::
:toctree: generated
:template: function.rst
non_uniform_fd
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Spectral Projection
-------------------
Spectral projection utilities for enforcing physical constraints:
.. automodule:: neuralop.layers.spectral_projection
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.. autosummary::
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:template: function.rst
spectral_projection_divergence_free
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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====
Data
====
In `neuralop.data`, we provide APIs for standardizing PDE datasets (`.datasets`) and transforming raw data into model inputs (`.transforms`).
Datasets
--------
We ship a small dataset for testing:
.. automodule:: neuralop.data.datasets
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.. autosummary::
:toctree: generated
:template: function.rst
load_darcy_flow_small
We provide downloadable datasets for Darcy-Flow, Navier-Stokes, and Car-CFD.
.. automodule:: neuralop.data.datasets.darcy
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.. autosummary::
:toctree: generated
:template: class.rst
DarcyDataset
.. automodule:: neuralop.data.datasets.navier_stokes
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.. autosummary::
:toctree: generated
:template: class.rst
NavierStokesDataset
.. automodule:: neuralop.data.datasets.car_cfd_dataset
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.. autosummary::
:toctree: generated
:template: class.rst
CarCFDDataset
.. _burgers_dataset_api:
.. automodule:: neuralop.data.datasets.burgers
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.. autosummary::
:toctree: generated
:template: class.rst
Burgers1dTimeDataset
.. autosummary::
:toctree: generated
:template: function.rst
load_mini_burgers_1dtime
.. note::
Additional datasets are available with optional dependencies:
* **The Well Datasets**: Large-scale collection of diverse physics simulations
(requires `the_well` package)
* **Spherical Shallow Water Equations**: For spherical coordinate systems
(requires `torch_harmonics` package)
These datasets are conditionally imported and may not be available
depending on your installation.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
DataProcessors
--------------
Much like PyTorch's `Torchvision.Datasets` module, our `data` module also includes
utilities to transform data from its raw form into the form expected by models and
loss functions:
.. automodule:: neuralop.data.transforms.data_processors
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.. autosummary::
:toctree: generated
:template: class.rst
DefaultDataProcessor
MGPatchingDataProcessor
IncrementalDataProcessor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Normalizers
-----------
Data normalization utilities:
.. automodule:: neuralop.data.transforms.normalizers
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.. autosummary::
:toctree: generated
:template: class.rst
UnitGaussianNormalizer
DictUnitGaussianNormalizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. raw:: html
=================
Utility Functions
=================
.. automodule:: neuralop.utils
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.. autosummary::
:toctree: generated
:template: function.rst
count_model_params
count_tensor_params
spectrum_2d
compute_rank
compute_stable_rank
compute_explained_variance