folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update doc

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

30
docs/source/_rst/_code.rst
View File

@@ -11,11 +11,11 @@ The high-level structure of the package is depicted in our API.
The pipeline to solve differential equations with PINA follows just five steps: The pipeline to solve differential equations with PINA follows just five steps:
1. Define the `Problem`_ the user aim to solve 1. Define the `Problems`_ the user aim to solve
2. Generate data using built in `Domains`_, or load high level simulation results as :doc:`LabelTensor <label_tensor>` 2. Generate data using built in `Geometrical Domains`_, or load high level simulation results as :doc:`LabelTensor <label_tensor>`
3. Choose or build one or more `Models`_ to solve the problem 3. Choose or build one or more `Models`_ to solve the problem
4. Choose a solver across PINA available `Solvers`_, or build one using the :doc:`SolverInterface <solver/solver_interface>` 4. Choose a solver across PINA available `Solvers`_, or build one using the :doc:`SolverInterface <solver/solver_interface>`
5. Train the model with the PINA :doc:`Trainer <solver/solver_interface>`, enhance the train with `Callback`_ 5. Train the model with the PINA :doc:`Trainer <solver/solver_interface>`, enhance the train with `Callbacks`_
Trainer, Dataset and Datamodule Trainer, Dataset and Datamodule
@@ -34,6 +34,7 @@ Data Types
LabelTensor <label_tensor.rst> LabelTensor <label_tensor.rst>
Graph <graph/graph.rst> Graph <graph/graph.rst>
LabelBatch <graph/label_batch.rst>
Graphs Structures Graphs Structures
@@ -41,7 +42,6 @@ Graphs Structures
.. toctree:: .. toctree::
:titlesonly: :titlesonly:
Graph <graph/graph.rst>
GraphBuilder <graph/graph_builder.rst> GraphBuilder <graph/graph_builder.rst>
RadiusGraph <graph/radius_graph.rst> RadiusGraph <graph/radius_graph.rst>
KNNGraph <graph/knn_graph.rst> KNNGraph <graph/knn_graph.rst>
@@ -98,7 +98,8 @@ Models
FNO <model/fourier_neural_operator.rst> FNO <model/fourier_neural_operator.rst>
AveragingNeuralOperator <model/average_neural_operator.rst> AveragingNeuralOperator <model/average_neural_operator.rst>
LowRankNeuralOperator <model/low_rank_neural_operator.rst> LowRankNeuralOperator <model/low_rank_neural_operator.rst>
GraphNeuralOperator <model/> GraphNeuralOperator <model/graph_neural_operator.rst>
GraphNeuralKernel <model/graph_neural_operator_integral_kernel.rst>
Blocks Blocks
------------- -------------
@@ -112,7 +113,10 @@ Blocks
Fourier Block <model/block/fourier_block.rst> Fourier Block <model/block/fourier_block.rst>
Averaging Block <model/block/average_neural_operator_block.rst> Averaging Block <model/block/average_neural_operator_block.rst>
Low Rank Block <model/block/low_rank_block.rst> Low Rank Block <model/block/low_rank_block.rst>
Graph Neural Operator Block <model/block/gno_block.rst>
Continuous Convolution Interface <model/block/convolution_interface.rst>
Continuous Convolution Block <model/block/convolution.rst> Continuous Convolution Block <model/block/convolution.rst>
Orthogonal Block <model/block/orthogonal.rst>
Reduction and Embeddings Reduction and Embeddings
@@ -144,7 +148,7 @@ Adaptive Activation Functions
.. toctree:: .. toctree::
:titlesonly: :titlesonly:
Adaptive Function Interface <adaptive_function/AdaptiveFunctionInterface.rst> Adaptive Function Interface <adaptive_function/AdaptiveActivationFunctionInterface.rst>
Adaptive ReLU <adaptive_function/AdaptiveReLU.rst> Adaptive ReLU <adaptive_function/AdaptiveReLU.rst>
Adaptive Sigmoid <adaptive_function/AdaptiveSigmoid.rst> Adaptive Sigmoid <adaptive_function/AdaptiveSigmoid.rst>
Adaptive Tanh <adaptive_function/AdaptiveTanh.rst> Adaptive Tanh <adaptive_function/AdaptiveTanh.rst>
@@ -165,10 +169,10 @@ Equations and Differential Operators
.. toctree:: .. toctree::
:titlesonly: :titlesonly:
EquationInterface <equation.equation_interface.rst> EquationInterface <equation/equation_interface.rst>
Equation <equation.equation.rst> Equation <equation/equation.rst>
SystemEquation <equation.system_equation.rst> SystemEquation <equation/system_equation.rst>
Equation Factory <equation.equation_factory.rst> Equation Factory <equation/equation_factory.rst>
Differential Operators <operator.rst> Differential Operators <operator.rst>
@@ -200,7 +204,7 @@ Problems Zoo
Geometrical Domains Geometrical Domains
--------------------- --------------------
.. toctree:: .. toctree::
:titlesonly: :titlesonly:
@@ -222,8 +226,8 @@ Domain Operations
Difference <domain/difference_domain.rst> Difference <domain/difference_domain.rst>
Exclusion <domain/exclusion_domain.rst> Exclusion <domain/exclusion_domain.rst>
Callback Callbacks
-------------------- -----------
.. toctree:: .. toctree::
:titlesonly: :titlesonly:

2
docs/source/_rst/adaptive_function/AdaptiveActivationFunctionInterface.rst
View File

@@ -3,6 +3,6 @@ AdaptiveActivationFunctionInterface
.. currentmodule:: pina.adaptive_function.adaptive_function_interface .. currentmodule:: pina.adaptive_function.adaptive_function_interface
.. automodule:: pina.adaptive_function.adaptive_functiontion_interface .. automodule:: pina.adaptive_function.adaptive_function_interface
:members: :members:
:show-inheritance: :show-inheritance:

0
docs/source/_rst/callback/adaptive_refinment_callbacks.rst → docs/source/_rst/callback/adaptive_refinment_callback.rst
View File

0
docs/source/_rst/callback/optimizer_callbacks.rst → docs/source/_rst/callback/optimizer_callback.rst
View File

2
docs/source/_rst/callback/processing_callbacks.rst → docs/source/_rst/callback/processing_callback.rst
View File

@@ -1,7 +1,7 @@
Processing callbacks Processing callbacks
======================= =======================
.. currentmodule:: pina.callbacks.processing_callback .. currentmodule:: pina.callback.processing_callback
.. autoclass:: MetricTracker .. autoclass:: MetricTracker
:members: :members:
:show-inheritance: :show-inheritance:

2
docs/source/_rst/domain/domain.rst
View File

@@ -4,6 +4,6 @@ Domain
.. automodule:: pina.domain.domain_interface .. automodule:: pina.domain.domain_interface
.. autoclass:: Domain .. autoclass:: DomainInterface
:members: :members:
:show-inheritance: :show-inheritance:

9
docs/source/_rst/graph/label_batch.rst Normal file
View File

@@ -0,0 +1,9 @@
LabelBatch
===========
.. currentmodule:: pina.graph
.. autoclass:: LabelBatch
:members:
:private-members:
:show-inheritance:

0
docs/source/_rst/model/block/average_neural_operator_block.py.rst → docs/source/_rst/model/block/average_neural_operator_block.rst
View File

8
docs/source/_rst/model/block/convolution_interface.rst Normal file
View File

@@ -0,0 +1,8 @@
Continuous Convolution Interface
==================================
.. currentmodule:: pina.model.block.convolution
.. autoclass:: BaseContinuousConv
:members:
:show-inheritance:
:noindex:

2
docs/source/_rst/model/fourier_integral_kernel.rst
View File

@@ -1,6 +1,6 @@
FourierIntegralKernel FourierIntegralKernel
========================= =========================
.. currentmodule:: pina.model.fno .. currentmodule:: pina.model.fourier_neural_operator
.. autoclass:: FourierIntegralKernel .. autoclass:: FourierIntegralKernel
:members: :members:

7
docs/source/_rst/model/graph_neural_operator_integral_kernel.rst Normal file
View File

@@ -0,0 +1,7 @@
GraphNeuralKernel
=======================
.. currentmodule:: pina.model.graph_neural_operator
.. autoclass:: GraphNeuralKernel
:members:
:show-inheritance:

2
docs/source/_rst/solver/physic_informed_solver/causal_pinn.rst
View File

@@ -1,6 +1,6 @@
CausalPINN CausalPINN
============== ==============
.. currentmodule:: pina.solver.physic_informed_solver.causalpinn .. currentmodule:: pina.solver.physic_informed_solver.causal_pinn
.. autoclass:: CausalPINN .. autoclass:: CausalPINN
:members: :members:

2
docs/source/_rst/solver/physic_informed_solver/rba_pinn.rst
View File

@@ -1,6 +1,6 @@
RBAPINN RBAPINN
======== ========
.. currentmodule:: pina.solver.physic_informed_solver.rbapinn .. currentmodule:: pina.solver.physic_informed_solver.rba_pinn
.. autoclass:: RBAPINN .. autoclass:: RBAPINN
:members: :members:

41
docs/source/_tutorial.rst
View File

@@ -1,46 +1,35 @@
PINA Tutorials PINA Tutorials
============== ======================
In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**. In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**.
Getting started with PINA Getting started with PINA
------------------------- -------------------------
.. toctree::
:maxdepth: 3
:titlesonly:
`Introduction to PINA for Physics Informed Neural Networks training <tutorials/tutorial1/tutorial.html>`_ - `Introduction to PINA for Physics Informed Neural Networks training <tutorial1/tutorial.html>`_
`Introduction to PINA Equation class <tutorials/tutorial12/tutorial.html>`_ - `Introduction to PINA Equation class <tutorial12/tutorial.html>`_
`PINA and PyTorch Lightning, training tips and visualizations <tutorials/tutorial11/tutorial.html>`_ - `PINA and PyTorch Lightning, training tips and visualizations <tutorial11/tutorial.html>`_
`Building custom geometries with PINA Location class <tutorials/tutorial6/tutorial.html>`_ - `Building custom geometries with PINA Location class <tutorial6/tutorial.html>`_
Physics Informed Neural Networks Physics Informed Neural Networks
-------------------------------- --------------------------------
.. toctree::
:maxdepth: 3
:titlesonly:
`Two dimensional Poisson problem using Extra Features Learning <tutorials/tutorial2/tutorial.html>`_ - `Two dimensional Poisson problem using Extra Features Learning <tutorial2/tutorial.html>`_
`Two dimensional Wave problem with hard constraint <tutorials/tutorial3/tutorial.html>`_ - `Two dimensional Wave problem with hard constraint <tutorial3/tutorial.html>`_
`Resolution of a 2D Poisson inverse problem <tutorials/tutorial7/tutorial.html>`_ - `Resolution of a 2D Poisson inverse problem <tutorial7/tutorial.html>`_
`Periodic Boundary Conditions for Helmotz Equation <tutorials/tutorial9/tutorial.html>`_ - `Periodic Boundary Conditions for Helmotz Equation <tutorial9/tutorial.html>`_
`Multiscale PDE learning with Fourier Feature Network <tutorials/tutorial13/tutorial.html>`_ - `Multiscale PDE learning with Fourier Feature Network <tutorial13/tutorial.html>`_
Neural Operator Learning Neural Operator Learning
------------------------ ------------------------
.. toctree::
:maxdepth: 3
:titlesonly:
`Two dimensional Darcy flow using the Fourier Neural Operator <tutorials/tutorial5/tutorial.html>`_ - `Two dimensional Darcy flow using the Fourier Neural Operator <tutorial5/tutorial.html>`_
`Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator <tutorials/tutorial10/tutorial.html>`_ - `Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator <tutorial10/tutorial.html>`_
Supervised Learning Supervised Learning
------------------- -------------------
.. toctree::
:maxdepth: 3
:titlesonly:
`Unstructured convolutional autoencoder via continuous convolution <tutorials/tutorial4/tutorial.html>`_ - `Unstructured convolutional autoencoder via continuous convolution <tutorial4/tutorial.html>`_
`POD-RBF and POD-NN for reduced order modeling <tutorials/tutorial8/tutorial.html>`_ - `POD-RBF and POD-NN for reduced order modeling <tutorial8/tutorial.html>`_

165
docs/source/conf.py
View File

@@ -19,84 +19,84 @@ import importlib.metadata
# -- Project information ----------------------------------------------------- # -- Project information -----------------------------------------------------
_DISTRIBUTION_METADATA = importlib.metadata.metadata('pina-mathlab') _DISTRIBUTION_METADATA = importlib.metadata.metadata("pina-mathlab")
project = _DISTRIBUTION_METADATA['Name'] project = _DISTRIBUTION_METADATA["Name"]
copyright = _DISTRIBUTION_METADATA['License-File'] copyright = _DISTRIBUTION_METADATA["License-File"]
author = "PINA contributors" author = "PINA Contributors"
version = _DISTRIBUTION_METADATA['Version'] version = _DISTRIBUTION_METADATA["Version"]
sys.path.insert(0, os.path.abspath('../sphinx_extensions')) # extension to remove paramref link from lightinig sys.path.insert(
0, os.path.abspath("../sphinx_extensions")
)
# -- General configuration ------------------------------------------------ # -- General configuration ------------------------------------------------
extensions = [ extensions = [
'sphinx.ext.autodoc', "sphinx.ext.autodoc",
'sphinx.ext.autosummary', "sphinx.ext.autosummary",
'sphinx.ext.doctest', "sphinx.ext.doctest",
'sphinx.ext.napoleon', "sphinx.ext.napoleon",
'sphinx.ext.intersphinx', "sphinx.ext.intersphinx",
'sphinx.ext.todo', "sphinx.ext.todo",
'sphinx.ext.coverage', "sphinx.ext.coverage",
'sphinx.ext.viewcode', "sphinx.ext.viewcode",
'sphinx.ext.mathjax', "sphinx.ext.mathjax",
'sphinx.ext.intersphinx', "sphinx.ext.intersphinx",
'paramref_extension', # this extension is made to remove paramref links from lightining doc "paramref_extension", # this extension is made to remove paramref links from lightining doc
'sphinx_copybutton', "sphinx_copybutton",
'sphinx_design' "sphinx_design",
] ]
# The root document.
root_doc = 'index'
# List of patterns, relative to source directory, that match files and # List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files. # directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path. # This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ['_build', 'docstrings', 'nextgen', 'Thumbs.db', '.DS_Store'] exclude_patterns = ["build", "docstrings", "nextgen", "Thumbs.db", ".DS_Store"]
# The reST default role (used for this markup: `text`) to use for all documents. # The reST default role (used for this markup: `text`) to use for all documents.
#default_role = 'literal' # default_role = 'literal'
# Generate the API documentation when building # Generate the API documentation when building
autosummary_generate = True autosummary_generate = True
numpydoc_show_class_members = False numpydoc_show_class_members = False
intersphinx_mapping = { intersphinx_mapping = {
'python': ('http://docs.python.org/3', None), "python": ("http://docs.python.org/3", None),
'matplotlib': ('https://matplotlib.org/stable', None), "matplotlib": ("https://matplotlib.org/stable", None),
'torch': ('https://pytorch.org/docs/stable/', None), "torch": ("https://pytorch.org/docs/stable/", None),
'lightning.pytorch': ("https://lightning.ai/docs/pytorch/stable/", None), "lightning.pytorch": ("https://lightning.ai/docs/pytorch/stable/", None),
} "torch_geometric": (
"https://pytorch-geometric.readthedocs.io/en/latest/",
None,
),
}
nitpicky = True # nitpicky = True
nitpick_ignore = [ # nitpick_ignore = [
# ('py:meth', 'lightning.pytorch.core.module.LightningModule.log'), # ("py:meth", "lightning.pytorch.core.module.LightningModule.log"),
# ('py:meth', 'lightning.pytorch.core.module.LightningModule.log_dict'), # ("py:meth", "lightning.pytorch.core.module.LightningModule.log_dict"),
# ('py:exc', 'MisconfigurationException'), # ("py:exc", "MisconfigurationException"),
# ('py:func', 'torch.inference_mode'), # ("py:func", "torch.inference_mode"),
# ('py:func', 'torch.no_grad'), # ("py:func", "torch.no_grad"),
# ('py:class', 'torch.utils.data.DistributedSampler'), # ("py:class", "torch.utils.data.DistributedSampler"),
# ('py:class', 'pina.model.layers.convolution.BaseContinuousConv'), # ("py:class", "pina.model.layers.convolution.BaseContinuousConv"),
# ('py:class', 'Module'), # ("py:class", "Module"),
# ('py:class', 'torch.nn.modules.loss._Loss'), # TO FIX # ("py:class", "torch.nn.modules.loss._Loss"), # TO FIX
# ('py:class', 'torch.optim.LRScheduler'), # TO FIX # ("py:class", "torch.optim.LRScheduler"), # TO FIX
# ]
]
# Add any paths that contain templates here, relative to this directory. # Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates'] templates_path = ["_templates"]
# The suffix(es) of source filenames. # The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string: source_suffix = ".rst"
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The master toctree document. # The master toctree document.
master_doc = 'index' master_doc = "index"
# autoclass # autoclass
autoclass_content = 'both' autoclass_content = "both"
# The version info for the project you're documenting, acts as replacement for # The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the # |version| and |release|, also used in various other places throughout the
@@ -108,7 +108,7 @@ release = version
# #
# This is also used if you do content translation via gettext catalogs. # This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases. # Usually you set "language" from the command line for these cases.
language = 'en' language = "en"
# List of patterns, relative to source directory, that match files and # List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files. # directories to ignore when looking for source files.
@@ -122,7 +122,7 @@ add_function_parentheses = True
add_module_names = False add_module_names = False
# The name of the Pygments (syntax highlighting) style to use. # The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx' pygments_style = "sphinx"
# A list of ignored prefixes for module index sortins as "systems = False # A list of ignored prefixes for module index sortins as "systems = False
@@ -143,7 +143,7 @@ viewcode_import = True
# The theme to use for HTML and HTML Help pages. See the documentation for # The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes. # a list of builtin themes.
html_theme = 'pydata_sphinx_theme' html_theme = "pydata_sphinx_theme"
# Theme options are theme-specific and customize the look and feel of a theme # Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the # further. For a list of options available for each theme, see the
@@ -151,7 +151,7 @@ html_theme = 'pydata_sphinx_theme'
# html_theme_options = {} # html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory. # Add any paths that contain custom themes here, relative to this directory.
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# The name of an image file (relative to this directory) to place at the top # The name of an image file (relative to this directory) to place at the top
# of the sidebar. # of the sidebar.
@@ -185,7 +185,7 @@ html_theme_options = {
# If not ''i, a 'Last updated on:' timestamp is inserted at every page bottom, # If not ''i, a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format. # using the given strftime format.
html_last_updated_fmt = '%b %d, %Y' html_last_updated_fmt = "%b %d, %Y"
# If false, no index is generated. # If false, no index is generated.
html_use_index = True html_use_index = True
@@ -197,40 +197,52 @@ html_show_sourcelink = True
html_show_copyright = True html_show_copyright = True
# Output file base name for HTML help builder. # Output file base name for HTML help builder.
htmlhelp_basename = 'pinadoc' htmlhelp_basename = "pinadoc"
# Link to external html files
html_extra_path = ["tutorials"]
# Avoid side bar for html files
html_sidebars = {
"_tutorial": [],
"_team": [],
"_cite": [],
"_contributing": [],
"_installation": [],
"_LICENSE": [],
}
# -- Options for LaTeX output --------------------------------------------- # -- Options for LaTeX output ---------------------------------------------
latex_elements = { latex_elements = {
# The paper size ('letterpaper' or 'a4paper'). # The paper size ('letterpaper' or 'a4paper').
'papersize': 'a4paper', "papersize": "a4paper",
# The font size ('10pt', '11pt' or '12pt'). # The font size ('10pt', '11pt' or '12pt').
'pointsize': '20pt', "pointsize": "20pt",
# Additional stuff for the LaTeX preamble. # Additional stuff for the LaTeX preamble.
'preamble': '', "preamble": "",
# Latex figure (float) alignment # Latex figure (float) alignment
'figure_align': 'htbp', "figure_align": "htbp",
} }
# Grouping the document tree into LaTeX files. List of tuples # Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, # (source start file, target name, title,
# author, documentclass [howto, manual, or own class]). # author, documentclass [howto, manual, or own class]).
latex_documents = [ latex_documents = [
(master_doc, 'pina.tex', u'PINA Documentation', (
u'PINA contributors', 'manual'), master_doc,
"pina.tex",
"PINA Documentation",
"PINA contributors",
"manual",
),
] ]
# -- Options for manual page output --------------------------------------- # -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples # One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section). # (source start file, name, description, authors, manual section).
man_pages = [ man_pages = [(master_doc, "pina", "PINA Documentation", [author], 1)]
(master_doc, 'pina', u'PINA Documentation',
[author], 1)
]
# -- Options for Texinfo output ------------------------------------------- # -- Options for Texinfo output -------------------------------------------
@@ -238,11 +250,20 @@ man_pages = [
# (source start file, target name, title, author, # (source start file, target name, title, author,
# dir menu entry, description, category) # dir menu entry, description, category)
texinfo_documents = [ texinfo_documents = [
(master_doc, 'pina', u'PINA Documentation', (
author, 'pina', 'One line description of project.', master_doc,
'Miscellaneous'), "pina",
"PINA Documentation",
author,
"pina",
"One line description of project.",
"Miscellaneous",
),
] ]
# If true, do not generate a @detailmenu in the "Top" node's menu. # If true, do not generate a @detailmenu in the "Top" node's menu.
# texinfo_no_detailmenu = False # texinfo_no_detailmenu = False
autodoc_member_order = 'bysource' autodoc_member_order = "bysource"
# Do consider meth ending with _ (needed for in-place methods of torch)
strip_signature_backslash = True

18
docs/source/index.rst
View File

@@ -9,32 +9,32 @@ Welcome to PINA’s documentation!
.. grid-item:: .. grid-item::
.. image:: index_files/tutorial_13_3.png .. image:: index_files/tutorial_13_3.png
:target: tutorials/tutorial2/tutorial.html :target: tutorial2/tutorial.html
.. grid-item:: .. grid-item::
.. image:: index_files/tutorial_32_0.png .. image:: index_files/tutorial_32_0.png
:target: tutorials/tutorial4/tutorial.html :target: tutorial4/tutorial.html
.. grid-item:: .. grid-item::
.. image:: index_files/tutorial_13_01.png .. image:: index_files/tutorial_13_01.png
:target: tutorials/tutorial9/tutorial.html :target: tutorial9/tutorial.html
.. grid-item:: .. grid-item::
.. image:: index_files/tutorial_36_0.png .. image:: index_files/tutorial_36_0.png
:target: tutorials/tutorial6/tutorial.html :target: tutorial6/tutorial.html
.. grid-item:: .. grid-item::
.. image:: index_files/tutorial_15_0.png .. image:: index_files/tutorial_15_0.png
:target: tutorials/tutorial13/tutorial.html :target: tutorial13/tutorial.html
.. grid-item:: .. grid-item::
.. image:: index_files/tutorial_5_0.png .. image:: index_files/tutorial_5_0.png
:target: tutorials/tutorial10/tutorial.html :target: tutorial10/tutorial.html
.. grid:: 1 1 3 3 .. grid:: 1 1 3 3
@@ -45,7 +45,7 @@ Welcome to PINA’s documentation!
an open-source Python library providing an intuitive interface for an open-source Python library providing an intuitive interface for
solving differential equations using PINNs, NOs or both together. solving differential equations using PINNs, NOs or both together.
Based on `PyTorch <https://pytorch.org/>`_ and `PyTorchLightning <https://lightning.ai/docs/pytorch/stable/>`_, **PINA** offers a simple and intuitive way to formalize a specific (differential) problem Based on `PyTorch <https://pytorch.org/>`_, `PyTorchLightning <https://lightning.ai/docs/pytorch/stable/>`_, and `PyG <https://pytorch-geometric.readthedocs.io/en/latest/>`_, **PINA** offers a simple and intuitive way to formalize a specific (differential) problem
and solve it using neural networks . The approximated solution of a differential equation and solve it using neural networks . The approximated solution of a differential equation
can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface. can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface.
@@ -63,9 +63,9 @@ Welcome to PINA’s documentation!
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1
Installing <_installation>
Tutorial <_tutorial>
API <_rst/_code> API <_rst/_code>
Tutorial <_tutorial>
Installing <_installation>
Team & Foundings <_team.rst> Team & Foundings <_team.rst>
Contributing <_contributing> Contributing <_contributing>
License <_LICENSE.rst> License <_LICENSE.rst>

5
docs/sphinx_extensions/paramref_extension.py
View File

@@ -1,11 +1,12 @@
from docutils import nodes from docutils import nodes
from docutils.parsers.rst.roles import register_local_role from docutils.parsers.rst.roles import register_local_role
def paramref_role(name, rawtext, text, lineno, inliner, options={}, content=[]): def paramref_role(name, rawtext, text, lineno, inliner, options={}, content=[]):
# Simply replace :paramref: with :param: # Simply replace :paramref: with :param:
new_role = nodes.literal(text=text[1:]) new_role = nodes.literal(text=text[1:])
return [new_role], [] return [new_role], []
def setup(app):
register_local_role('paramref', paramref_role)
def setup(app):
register_local_role("paramref", paramref_role)

4
pina/adaptive_function/adaptive_function_interface.py
View File

@@ -7,9 +7,7 @@ from ..utils import check_consistency, is_function
class AdaptiveActivationFunctionInterface(torch.nn.Module, metaclass=ABCMeta): class AdaptiveActivationFunctionInterface(torch.nn.Module, metaclass=ABCMeta):
r""" r"""
The The :class:`AdaptiveActivationFunctionInterface`
:class:`~pina.adaptive_function.adaptive_func_interface.\
AdaptiveActivationFunctionInterface`
class makes a :class:`torch.nn.Module` activation function into an adaptive class makes a :class:`torch.nn.Module` activation function into an adaptive
trainable activation function. If one wants to create an adpative activation trainable activation function. If one wants to create an adpative activation
function, this class must be use as base class. function, this class must be use as base class.

34
pina/data/data_module.py
View File

@@ -16,29 +16,23 @@ from ..collector import Collector
class DummyDataloader: class DummyDataloader:
"""
Dataloader used when batch size is ``None``. It returns the entire dataset
in a single batch.
"""
def __init__(self, dataset): def __init__(self, dataset):
""" """
Preprare a dataloader object which will return the entire dataset Prepare a dataloader object that returns the entire dataset in a single
in a single batch. Depending on the number of GPUs, the dataset is batch. Depending on the number of GPUs, the dataset is managed
managed as follows: as follows:
- **Distributed Environment** (multiple GPUs): - **Distributed Environment** (multiple GPUs): Divides dataset across
- Divides the dataset across processes using the rank and world processes using the rank and world size. Fetches only portion of
size. data corresponding to the current process.
- Fetches only the portion of data corresponding to the current - **Non-Distributed Environment** (single GPU): Fetches the entire
process. dataset.
- **Non-Distributed Environment** (single GPU):
- Fetches the entire dataset.
:param dataset: The dataset object to be processed. :param PinaDataset dataset: The dataset object to be processed.
:type dataset: PinaDataset
.. note:: This data loader is used when the batch size is ``None``. .. note::
This dataloader is used when the batch size is ``None``.
""" """
if ( if (
@@ -84,8 +78,10 @@ class Collator:
Initialize the object, setting the collate function based on whether Initialize the object, setting the collate function based on whether
automatic batching is enabled or not. automatic batching is enabled or not.
:param dict max_conditions_lengths: dict containing the maximum number :param dict max_conditions_lengths: ``dict`` containing the maximum
of data points to consider in a single batch for each condition. number of data points to consider in a single batch for
each condition.
:param bool automatic_batching: Whether to enable automatic batching.
:param PinaDataset dataset: The dataset where the data is stored. :param PinaDataset dataset: The dataset where the data is stored.
""" """

3
pina/data/dataset.py
View File

@@ -276,7 +276,8 @@ class PinaGraphDataset(PinaDataset):
:param data: List of items to collate in a single batch. :param data: List of items to collate in a single batch.
:type data: list[Data] | list[Graph] :type data: list[Data] | list[Graph]
:return: Batch object. :return: Batch object.
:rtype: Batch | LabelBatch :rtype: :class:`~torch_geometric.data.Batch`
| :class:`~pina.graph.LabelBatch`
""" """
if isinstance(data[0], Data): if isinstance(data[0], Data):

5
pina/graph.py
View File

@@ -399,8 +399,9 @@ class LabelBatch(Batch):
:param data_list: List of :class:`~torch_geometric.data.Data` or :param data_list: List of :class:`~torch_geometric.data.Data` or
:class:`~pina.graph.Graph` objects. :class:`~pina.graph.Graph` objects.
:type data_list: list[Data] | list[Graph] :type data_list: list[Data] | list[Graph]
:return: A :class:`Batch` object containing the input data. :return: A :class:`~torch_geometric.data.Batch` object containing
:rtype: Batch the input data.
:rtype: :class:`~torch_geometric.data.Batch`
""" """
# Store the labels of Data/Graph objects (all data have the same labels) # Store the labels of Data/Graph objects (all data have the same labels)
# If the data do not contain labels, labels is an empty dictionary, # If the data do not contain labels, labels is an empty dictionary,

7
pina/label_tensor.py
View File

@@ -389,14 +389,15 @@ class LabelTensor(torch.Tensor):
def requires_grad_(self, mode=True): def requires_grad_(self, mode=True):
""" """
Override the requires_grad_ method to handle the labels in the new Override the :meth:`~torch.Tensor.requires_grad_` method to handle
tensor. For more details, see :meth:`torch.Tensor.requires_grad_`. the labels in the new tensor.
For more details, see :meth:`~torch.Tensor.requires_grad_`.
:param bool mode: A boolean value indicating whether the tensor should :param bool mode: A boolean value indicating whether the tensor should
track gradients.If `True`, the tensor will track gradients; track gradients.If `True`, the tensor will track gradients;
if `False`, it will not. if `False`, it will not.
:return: The :class:`~pina.label_tensor.LabelTensor` itself with the :return: The :class:`~pina.label_tensor.LabelTensor` itself with the
updated `requires_grad` state and retained labels. updated ``requires_grad`` state and retained labels.
:rtype: LabelTensor :rtype: LabelTensor
""" """

1
pina/model/block/convolution.py
View File

@@ -15,6 +15,7 @@ class BaseContinuousConv(torch.nn.Module, metaclass=ABCMeta):
batch_size, :math:`N_{in}` is the number of input fields, :math:`N` 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. the number of points in the mesh, :math:`D` the dimension of the problem.
In particular: In particular:
* :math:`D` is the number of spatial variables + 1. The last column must * :math:`D` is the number of spatial variables + 1. The last column must
contain the field value. contain the field value.
* :math:`N_{in}` represents the number of function components. * :math:`N_{in}` represents the number of function components.

9
pina/model/block/convolution_2d.py
View File

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

1
pina/model/deeponet.py
View File

@@ -412,6 +412,7 @@ class DeepONet(MIONet):
Differently, for a :class:`torch.Tensor` only a list of integers can Differently, for a :class:`torch.Tensor` only a list of integers can
be passed for ``input_indeces_branch_net`` and be passed for ``input_indeces_branch_net`` and
``input_indeces_trunk_net``. ``input_indeces_trunk_net``.
.. warning:: .. warning::
No checks are performed in the forward pass to verify if the input No checks are performed in the forward pass to verify if the input
is instance of either :class:`~pina.label_tensor.LabelTensor` or is instance of either :class:`~pina.label_tensor.LabelTensor` or

4
pina/model/feed_forward.py
View File

@@ -36,7 +36,7 @@ class FeedForward(torch.nn.Module):
:param int inner_size: The number of neurons for each hidden layer. :param int inner_size: The number of neurons for each hidden layer.
Default is ``20``. Default is ``20``.
:param int n_layers: The number of hidden layers. Default is ``2``. :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 the same length as ``n_layers``. If a single function is passed, it
is used for all layers, except for the last one. is used for all layers, except for the last one.
Default is :class:`torch.nn.Tanh`. 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. :param int inner_size: The number of neurons for each hidden layer.
Default is ``20``. Default is ``20``.
:param int n_layers: The number of hidden layers. Default is ``2``. :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 the same length as ``n_layers``. If a single function is passed, it
is used for all layers, except for the last one. is used for all layers, except for the last one.
Default is :class:`torch.nn.Tanh`. Default is :class:`torch.nn.Tanh`.

6
pina/model/fourier_neural_operator.py
View File

@@ -274,7 +274,7 @@ class FNO(KernelNeuralOperator):
layers=None, 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. the input to its hidden dimension.
:param torch.nn.Module projecting_net: The projection neural network :param torch.nn.Module projecting_net: The projection neural network
mapping the hidden representation to the output function. mapping the hidden representation to the output function.
@@ -325,12 +325,14 @@ class FNO(KernelNeuralOperator):
``projection_net`` maps the hidden representation to the output ``projection_net`` maps the hidden representation to the output
function. function.
: param x: The input tensor for performing the computation. Depending :param x: The input tensor for performing the computation. Depending
on the ``dimensions`` in the initialization, it expects a tensor on the ``dimensions`` in the initialization, it expects a tensor
with the following shapes: with the following shapes:
* 1D tensors: ``[batch, X, channels]`` * 1D tensors: ``[batch, X, channels]``
* 2D tensors: ``[batch, X, Y, channels]`` * 2D tensors: ``[batch, X, Y, channels]``
* 3D tensors: ``[batch, X, Y, Z, channels]`` * 3D tensors: ``[batch, X, Y, Z, channels]``
:type x: torch.Tensor | LabelTensor :type x: torch.Tensor | LabelTensor
:return: The output tensor. :return: The output tensor.
:rtype: torch.Tensor :rtype: torch.Tensor

9
pina/model/graph_neural_operator.py
View File

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

5
pina/optim/torch_optimizer.py
View File

@@ -17,8 +17,9 @@ class TorchOptimizer(Optimizer):
:param torch.optim.Optimizer optimizer_class: A :param torch.optim.Optimizer optimizer_class: A
:class:`torch.optim.Optimizer` class. :class:`torch.optim.Optimizer` class.
:param dict kwargs: Additional parameters passed to `optimizer_class`, :param dict kwargs: Additional parameters passed to ``optimizer_class``,
see more: <https://pytorch.org/docs/stable/optim.html#algorithms>_. see more
`here <https://pytorch.org/docs/stable/optim.html#algorithms>`_.
""" """
check_consistency(optimizer_class, torch.optim.Optimizer, subclass=True) check_consistency(optimizer_class, torch.optim.Optimizer, subclass=True)

5
pina/optim/torch_scheduler.py
View File

@@ -23,8 +23,9 @@ class TorchScheduler(Scheduler):
:param torch.optim.LRScheduler scheduler_class: A :param torch.optim.LRScheduler scheduler_class: A
:class:`torch.optim.LRScheduler` class. :class:`torch.optim.LRScheduler` class.
:param dict kwargs: Additional parameters passed to `scheduler_class`, :param dict kwargs: Additional parameters passed to ``scheduler_class``,
see more: <https://pytorch.org/docs/stable/optim.html#algorithms>_. see more
`here <https://pytorch.org/docs/stable/optim.html#algorithms>_`.
""" """
check_consistency(scheduler_class, LRScheduler, subclass=True) check_consistency(scheduler_class, LRScheduler, subclass=True)

26
pina/problem/abstract_problem.py
View File

@@ -47,7 +47,7 @@ class AbstractProblem(metaclass=ABCMeta):
Get batching dimension. Get batching dimension.
:return: The batching dimension. :return: The batching dimension.
:rtype int :rtype: int
""" """
return self._batching_dimension return self._batching_dimension
@@ -85,7 +85,7 @@ class AbstractProblem(metaclass=ABCMeta):
points. points.
:return: The discretised domains. :return: The discretised domains.
:rtype dict :rtype: dict
""" """
return self._discretised_domains return self._discretised_domains
@@ -178,13 +178,28 @@ class AbstractProblem(metaclass=ABCMeta):
chebyshev sampling, ``chebyshev``; grid sampling ``grid``. chebyshev sampling, ``chebyshev``; grid sampling ``grid``.
:param domains: The domains from which to sample. Default is ``all``. :param domains: The domains from which to sample. Default is ``all``.
:type domains: str | list[str] :type domains: str | list[str]
:param dict sample_rules: A dictionary of custom sampling rules. :param dict sample_rules: A dictionary defining custom sampling rules
for input variables. If provided, it must contain a dictionary
specifying the sampling rule for each variable, overriding the
``n`` and ``mode`` arguments. Each key must correspond to the
input variables from
:meth:~pina.problem.AbstractProblem.input_variables, and its value
should be another dictionary with
two keys: ``n`` (number of points to sample) and ``mode``
(sampling method). Defaults to None.
:raises RuntimeError: If both ``n`` and ``sample_rules`` are specified. :raises RuntimeError: If both ``n`` and ``sample_rules`` are specified.
:raises RuntimeError: If neither ``n`` nor ``sample_rules`` are set. :raises RuntimeError: If neither ``n`` nor ``sample_rules`` are set.
:Example: :Example:
>>> problem.discretise_domain(n=10, mode='grid') >>> problem.discretise_domain(n=10, mode='grid')
>>> problem.discretise_domain(n=10, mode='grid', domains=['gamma1']) >>> problem.discretise_domain(n=10, mode='grid', domains=['gamma1'])
>>> problem.discretise_domain(
... sample_rules={
... 'x': {'n': 10, 'mode': 'grid'},
... 'y': {'n': 100, 'mode': 'grid'}
... },
... domains=['D']
... )
.. warning:: .. warning::
``random`` is currently the only implemented ``mode`` for all ``random`` is currently the only implemented ``mode`` for all
@@ -197,6 +212,11 @@ class AbstractProblem(metaclass=ABCMeta):
:class:`~pina.domain.intersection_domain.Intersection`. :class:`~pina.domain.intersection_domain.Intersection`.
The modes ``latin`` or ``lh``, ``chebyshev``, ``grid`` are only The modes ``latin`` or ``lh``, ``chebyshev``, ``grid`` are only
implemented for :class:`~pina.domain.cartesian.CartesianDomain`. implemented for :class:`~pina.domain.cartesian.CartesianDomain`.
.. warning::
If custom discretisation is applied by setting ``sample_rules`` not
to ``None``, then the discretised domain must be of class
:class:`~pina.domain.cartesian.CartesianDomain`
""" """
# check consistecy n, mode, variables, locations # check consistecy n, mode, variables, locations

2
pina/solver/physic_informed_solver/rba_pinn.py
View File

@@ -82,7 +82,7 @@ class RBAPINN(PINN):
:param AbstractProblem problem: The problem to be solved. :param AbstractProblem problem: The problem to be solved.
:param torch.nn.Module model: The neural network model to be used. :param torch.nn.Module model: The neural network model to be used.
param Optimizer optimizer: The optimizer to be used. :param Optimizer optimizer: The optimizer to be used.
If `None`, the :class:`torch.optim.Adam` optimizer is used. If `None`, the :class:`torch.optim.Adam` optimizer is used.
Default is ``None``. Default is ``None``.
:param Scheduler scheduler: Learning rate scheduler. :param Scheduler scheduler: Learning rate scheduler.

13
pina/trainer.py
View File

@@ -13,8 +13,9 @@ class Trainer(lightning.pytorch.Trainer):
PINA custom Trainer class to extend the standard Lightning functionality. PINA custom Trainer class to extend the standard Lightning functionality.
This class enables specific features or behaviors required by the PINA This class enables specific features or behaviors required by the PINA
framework. It modifies the standard :class:`lightning.pytorch.Trainer` class framework. It modifies the standard
to better support the training process in PINA. :class:`lightning.pytorch.Trainer <lightning.pytorch.trainer.trainer.Trainer>`
class to better support the training process in PINA.
""" """
def __init__( def __init__(
@@ -207,7 +208,9 @@ class Trainer(lightning.pytorch.Trainer):
""" """
Manage the training process of the solver. Manage the training process of the solver.
:param dict kwargs: Additional keyword arguments. :param dict kwargs: Additional keyword arguments. See `pytorch-lightning
Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_
for details.
""" """
return super().fit(self.solver, datamodule=self.data_module, **kwargs) return super().fit(self.solver, datamodule=self.data_module, **kwargs)
@@ -215,7 +218,9 @@ class Trainer(lightning.pytorch.Trainer):
""" """
Manage the test process of the solver. Manage the test process of the solver.
:param dict kwargs: Additional keyword arguments. :param dict kwargs: Additional keyword arguments. See `pytorch-lightning
Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_
for details.
""" """
return super().test(self.solver, datamodule=self.data_module, **kwargs) return super().test(self.solver, datamodule=self.data_module, **kwargs)