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update doc

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This commit is contained in:
Dario Coscia
2025-03-17 12:23:26 +01:00
committed by FilippoOlivo
parent ae1fd2680f
commit 480140dd31
33 changed files with 265 additions and 196 deletions
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1
pina/model/block/convolution.py
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@@ -15,6 +15,7 @@ class BaseContinuousConv(torch.nn.Module, metaclass=ABCMeta):
batch_size, :math:`N_{in}` is the number of input fields, :math:`N`
the number of points in the mesh, :math:`D` the dimension of the problem.
In particular:
* :math:`D` is the number of spatial variables + 1. The last column must
contain the field value.
* :math:`N_{in}` represents the number of function components.

9
pina/model/block/convolution_2d.py
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@@ -15,10 +15,13 @@ class ContinuousConvBlock(BaseContinuousConv):
batch_size, :math:`N_{in}` is the number of input fields, :math:`N`
the number of points in the mesh, :math:`D` the dimension of the problem.
In particular:
* :math:`D` is the number of spatial variables + 1. The last column must
contain the field value.
* :math:`N_{in}` represents the number of function components.
For instance, a vectorial function :math:`f = [f_1, f_2]` has
contain the field value. For example for 2D problems :math:`D=3` and
the tensor will be something like ``[first coordinate, second
coordinate, field value]``.
* :math:`N_{in}` represents the number of vectorial function presented.
For example a vectorial function :math:`f = [f_1, f_2]` will have
:math:`N_{in}=2`.
.. seealso::

3
pina/model/deeponet.py
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@@ -412,7 +412,8 @@ class DeepONet(MIONet):
Differently, for a :class:`torch.Tensor` only a list of integers can
be passed for ``input_indeces_branch_net`` and
``input_indeces_trunk_net``.
.. warning::
.. warning::
No checks are performed in the forward pass to verify if the input
is instance of either :class:`~pina.label_tensor.LabelTensor` or
:class:`torch.Tensor`. In general, in case of a

4
pina/model/feed_forward.py
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@@ -36,7 +36,7 @@ class FeedForward(torch.nn.Module):
:param int inner_size: The number of neurons for each hidden layer.
Default is ``20``.
:param int n_layers: The number of hidden layers. Default is ``2``.
::param func: The activation function. If a list is passed, it must have
:param func: The activation function. If a list is passed, it must have
the same length as ``n_layers``. If a single function is passed, it
is used for all layers, except for the last one.
Default is :class:`torch.nn.Tanh`.
@@ -144,7 +144,7 @@ class ResidualFeedForward(torch.nn.Module):
:param int inner_size: The number of neurons for each hidden layer.
Default is ``20``.
:param int n_layers: The number of hidden layers. Default is ``2``.
::param func: The activation function. If a list is passed, it must have
:param func: The activation function. If a list is passed, it must have
the same length as ``n_layers``. If a single function is passed, it
is used for all layers, except for the last one.
Default is :class:`torch.nn.Tanh`.

32
pina/model/fourier_neural_operator.py
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@@ -274,7 +274,7 @@ class FNO(KernelNeuralOperator):
layers=None,
):
"""
param torch.nn.Module lifting_net: The lifting neural network mapping
:param torch.nn.Module lifting_net: The lifting neural network mapping
the input to its hidden dimension.
:param torch.nn.Module projecting_net: The projection neural network
mapping the hidden representation to the output function.
@@ -318,22 +318,24 @@ class FNO(KernelNeuralOperator):
def forward(self, x):
"""
Forward pass for the :class:`FourierNeuralOperator` model.
Forward pass for the :class:`FourierNeuralOperator` model.
The ``lifting_net`` maps the input to the hidden dimension.
Then, several layers of Fourier blocks are applied. Finally, the
``projection_net`` maps the hidden representation to the output
function.
The ``lifting_net`` maps the input to the hidden dimension.
Then, several layers of Fourier blocks are applied. Finally, the
``projection_net`` maps the hidden representation to the output
function.
: param x: The input tensor for performing the computation. Depending
on the ``dimensions`` in the initialization, it expects a tensor
with the following shapes:
* 1D tensors: ``[batch, X, channels]``
* 2D tensors: ``[batch, X, Y, channels]``
* 3D tensors: ``[batch, X, Y, Z, channels]``
:type x: torch.Tensor | LabelTensor
:return: The output tensor.
:rtype: torch.Tensor
:param x: The input tensor for performing the computation. Depending
on the ``dimensions`` in the initialization, it expects a tensor
with the following shapes:
* 1D tensors: ``[batch, X, channels]``
* 2D tensors: ``[batch, X, Y, channels]``
* 3D tensors: ``[batch, X, Y, Z, channels]``
:type x: torch.Tensor | LabelTensor
:return: The output tensor.
:rtype: torch.Tensor
"""
if isinstance(x, LabelTensor):

9
pina/model/graph_neural_operator.py
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@@ -8,9 +8,9 @@ from .kernel_neural_operator import KernelNeuralOperator
class GraphNeuralKernel(torch.nn.Module):
"""
Graph Neural Kernel model class.
Graph Neural Operator kernel model class.
This class implements the Graph Neural Kernel network.
This class implements the Graph Neural Operator kernel network.
.. seealso::
@@ -18,8 +18,7 @@ class GraphNeuralKernel(torch.nn.Module):
Liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A. (2020).
*Neural Operator: Graph Kernel Network for Partial Differential
Equations*.
DOI: `arXiv preprint arXiv:2003.03485.
<https://arxiv.org/abs/2003.03485>`_
DOI: `arXiv preprint arXiv:2003.03485 <https://arxiv.org/abs/2003.03485>`_
"""
def __init__(
@@ -171,7 +170,7 @@ class GraphNeuralOperator(KernelNeuralOperator):
"""
Initialization of the :class:`GraphNeuralOperator` class.
param torch.nn.Module lifting_operator: The lifting neural network
:param torch.nn.Module lifting_operator: The lifting neural network
mapping the input to its hidden dimension.
:param torch.nn.Module projection_operator: The projection neural
network mapping the hidden representation to the output function.