Dario/0.3/doc

README.md

@@ -219,8 +219,8 @@ Want to dive deeper? Check out the official
 import torch
 from pina import Trainer
 from pina.model import FeedForward
-from pina.solver import SupervisedSolver
 from pina.problem.zoo import SupervisedProblem
+from pina.solver import SupervisedSingleModelSolver
 
 input_tensor  = torch.rand((10, 1))
 target_tensor = input_tensor.pow(3)
@@ -232,7 +232,7 @@ problem = SupervisedProblem(input_tensor, target_tensor)
 model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64])
 
 # Step 3. Define solver
-solver = SupervisedSolver(problem, model, use_lt=False)
+solver = SupervisedSingleModelSolver(problem, model, use_lt=False)
 
 # Step 4. Train
 trainer = Trainer(solver, max_epochs=1000, accelerator="gpu")
@@ -261,13 +261,14 @@ In PINA, this can be implemented as:
 </p>
 
 ```python
-from pina import Trainer, Condition
-from pina.problem import SpatialProblem
 from pina.operator import grad
-from pina.solver import PINN
 from pina.model import FeedForward
+from pina.equation import Equation
+from pina import Trainer, Condition
 from pina.domain import CartesianDomain
-from pina.equation import Equation, FixedValue
+from pina.problem import SpatialProblem
+from pina.equation.zoo import FixedValue
+from pina.solver import PhysicsInformedSingleModelSolver
 
 def ode_equation(input_, output_):
     u_x = grad(output_, input_, components=["u"], d=["x"])
@@ -294,7 +295,7 @@ problem.discretise_domain(n=100, mode="grid", domains=["D", "x0"])
 model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64])
 
 # Step 3. Define solver
-solver = PINN(problem, model)
+solver = PhysicsInformedSingleModelSolver(problem, model)
 
 # Step 4. Train
 trainer = Trainer(solver, max_epochs=1000, accelerator="gpu")

docs/source/_LICENSE.rst

@@ -1,3 +1,7 @@
+.. docmeta::
+   :last_reviewed: 2026-06-24
+
+
 License
 ==============
 

docs/source/_cite.rst

@@ -1,3 +1,7 @@
+.. docmeta::
+   :last_reviewed: 2026-06-24
+
+
 Cite PINA
 ==============
 

docs/source/_contributing.rst

@@ -1,32 +1,26 @@
-Contributing to PINA
-=====================
+.. docmeta::
+   :last_reviewed: 2026-06-24
 
-First off, thanks for taking the time to contribute to **PINA**! 🎉 Your help makes the project better for everyone. This document outlines the process for contributing, reporting issues, suggesting features, and submitting pull requests.
 
-Table of Contents
-------------------------
+Contributing to PINA
+=====================
 
-1. `How to Contribute`_
-2. `Reporting Bugs`_
-3. `Suggesting Enhancements`_
-4. `Pull Request Process`_
-5. `Code Style & Guidelines`_
-6. `Community Standards`_
+First off, thanks for taking the time to contribute to **PINA**! Your help makes the project better for everyone. This document outlines the process for contributing, reporting issues, suggesting features, and submitting pull requests.
 
 How to Contribute
-------------------------
+-----------------
 
 You can contribute in several ways:
 
 - Reporting bugs
-- Suggesting features/enhancements
+- Suggesting features or enhancements
 - Submitting fixes or improvements via Pull Requests (PRs)
 - Improving documentation
 
 We encourage all contributions, big or small!
 
 Reporting Bugs
-------------------------
+--------------
 
 If you find a bug, please open an `issue <https://github.com/mathLab/PINA/issues>`_ and include:
 
@@ -38,23 +32,23 @@ If you find a bug, please open an `issue <https://github.com/mathLab/PINA/issues
 - Environment info (OS, Python version, dependencies, etc.)
 
 Suggesting Enhancements
-------------------------
+-----------------------
 
 We welcome new ideas! If you have an idea to improve PINA:
 
 1. Check the `issue tracker <https://github.com/mathLab/PINA/issues>`_ or the `discussions <https://github.com/mathLab/PINA/discussions>`_ to see if someone has already suggested it.
 2. If not, open a new issue describing:
-   - The enhancement you'd like
+   - The enhancement you would like
    - Why it would be useful
    - Any ideas on how to implement it (optional but helpful)
-3. If you are not sure about (something of) the enhancement, we suggest opening a discussion to collaborate on it with the PINA community.
+3. If you are not sure about the enhancement, open a discussion to collaborate with the PINA community.
 
 Pull Request Process
-------------------------
+--------------------
 
 Before submitting a PR:
 
-1. Ensure there’s an open issue related to your contribution (or create one).
+1. Ensure there is an open issue related to your contribution (or create one).
 2. `Fork <https://help.github.com/articles/fork-a-repo>`_ the repository and create a new branch from ``master``:
 
    .. code-block:: bash
@@ -71,30 +65,36 @@ Before submitting a PR:
 
       pytest
 
-5. Properly format your code. If you want to save time, simply run:
+5. Format your code:
 
    .. code-block:: bash
 
       bash code_formatter.sh
 
-7. Submit a `pull request <https://help.github.com/articles/creating-a-pull-request>`_ with a clear explanation of your changes and reference the related issue if applicable.
+6. Submit a `pull request <https://help.github.com/articles/creating-a-pull-request>`_ with a clear explanation of your changes and reference the related issue if applicable.
 
-Pull Request Checklist
+Pull Request Checklist:
 
-1. Code follows the project’s style guidelines
+1. Code follows the project's style guidelines
 2. Tests have been added or updated
 3. Documentation has been updated if necessary
 4. Pull request is linked to an open issue (if applicable)
 
 Code Style & Guidelines
-------------------------
+-----------------------
 
 - Follow PEP8 for Python code.
 - Use descriptive commit messages (e.g. ``Fix parser crash on empty input``).
 - Write clear docstrings for public classes, methods, and functions.
 - Keep functions small and focused; do one thing and do it well.
 
 Community Standards
-------------------------
+-------------------
 
 By participating in this project, you agree to abide by our Code of Conduct. We are committed to maintaining a welcoming and inclusive community.
+
+See Also
+--------
+
+* :doc:`Installation guide <_installation>`
+* :doc:`API Reference <_rst/_code>`

docs/source/_installation.rst

@@ -1,52 +1,70 @@
+.. docmeta::
+   :last_reviewed: 2026-06-24
+
+
 Installation
 ============
 
-**PINA** requires requires `torch`, `lightning`, `torch_geometric` and `matplotlib`. 
+**PINA** requires `torch`, `lightning`, `torch_geometric` and `matplotlib`.
 
 Installing via PIP
 __________________
 
-Mac and Linux users can install pre-built binary packages using pip.
-To install the package just type:
+Mac and Linux users can install pre-built binary packages using pip:
 
 .. code-block:: bash
 
-    $ pip install pina-mathlab
+    pip install pina-mathlab
 
 To uninstall the package:
 
 .. code-block:: bash
 
-    $ pip uninstall pina-mathlab
+    pip uninstall pina-mathlab
 
 Installing from source
 ______________________
-The official distribution is on GitHub, and you can clone the repository using
 
-.. code-block:: bash
-
-    $ git clone https://github.com/mathLab/PINA
+The official distribution is on GitHub. Clone the repository:
 
-To install the package just type:
-
 .. code-block:: bash
 
-    $ pip install -e .
+    git clone https://github.com/mathLab/PINA
+
+Then install in editable mode:
+
+.. code-block:: bash
 
+    pip install -e .
 
 Install with extra packages
 ____________________________
 
-To install extra dependencies required to run tests or tutorials directories, please use the following command:
+To install extra dependencies required to run tests or tutorials, use:
 
 .. code-block:: bash
 
-    $ pip install "pina-mathlab[extras]" 
-
+    pip install "pina-mathlab[extras]"
 
 Available extras include:
 
-* `dev` for development purpuses, use this if you want to Contribute.
-* `test` for running test locally.
-* `doc` for building documentation locally.
-* `tutorial` for running tutorials
+* ``dev`` — development tools (use this if you want to contribute).
+* ``test`` — for running tests locally.
+* ``doc`` — for building the documentation locally.
+* ``tutorial`` — for running tutorials.
+
+Requirements
+____________
+
+PINA is built on:
+
+* `PyTorch <https://pytorch.org/>`_ — deep learning framework.
+* `PyTorch Lightning <https://lightning.ai/docs/pytorch/stable/>`_ — training loop orchestration.
+* `PyTorch Geometric <https://pytorch-geometric.readthedocs.io/en/latest/>`_ — graph neural network support.
+* `Matplotlib <https://matplotlib.org/>`_ — plotting and visualisation.
+
+See Also
+--------
+
+* :doc:`Quickstart guide <_quickstart>`
+* :doc:`API Reference <_rst/_code>`

docs/source/_quickstart.rst

@@ -0,0 +1,100 @@
+.. docmeta::
+   :last_reviewed: 2026-06-24
+
+
+Quickstart
+==========
+
+This guide gets you up and running with PINA in 5 minutes. By the end, you will have trained a Physics-Informed Neural Network (PINN) to solve the Poisson equation on a unit square.
+
+Install
+-------
+
+.. code-block:: bash
+
+   pip install pina-mathlab
+
+Define a problem
+----------------
+
+Every PINA workflow starts by defining a :class:`~pina.problem.spatial_problem.SpatialProblem`.
+A problem specifies the output variables, the computational domain, and the conditions
+(PDE residual, boundary conditions, initial conditions, data) that the solver must satisfy.
+
+.. code-block:: python
+
+   from pina import Condition
+   from pina.problem import SpatialProblem
+   from pina.domain import CartesianDomain
+   from pina.equation import Equation, FixedValue
+
+   class PoissonProblem(SpatialProblem):
+       output_variables = ["u"]
+
+       domains = {
+           "domain": CartesianDomain({"x": [0, 1], "y": [0, 1]}),
+           "boundary": CartesianDomain({"x": [0, 1], "y": [0, 1]}),
+       }
+
+       conditions = {
+           "domain": Condition(
+               domain="domain",
+               equation=Equation("d2(u,x) + d2(u,y) + sin(pi*x)*sin(pi*y) = 0"),
+           ),
+           "boundary": Condition(
+               domain="boundary",
+               equation=FixedValue(0.0),
+           ),
+       }
+
+   problem = PoissonProblem()
+
+Create a model
+--------------
+
+Choose a neural network architecture. For standard PINNs, a :class:`~pina.model.feed_forward.FeedForward` (MLP) is a solid starting point.
+
+.. code-block:: python
+
+   from pina.model import FeedForward
+
+   model = FeedForward(
+       input_dimensions=2,
+       output_dimensions=1,
+       inner_size=20,
+       n_layers=3,
+   )
+
+Train with a solver
+-------------------
+
+The :class:`~pina.solver.physics_informed_solver.pinn.PINN` solver wraps the problem and model, and the :class:`~pina._src.core.trainer.Trainer` orchestrates the training loop.
+
+.. code-block:: python
+
+   from pina.solver import PINN
+   from pina import Trainer
+
+   pinn = PINN(problem=problem, model=model)
+   trainer = Trainer(solver=pinn, max_epochs=1000)
+   trainer.train()
+
+Inspect results
+---------------
+
+After training, the model stores its solution in the solver. Evaluate at any point:
+
+.. code-block:: python
+
+   import torch
+
+   x = torch.tensor([[0.5, 0.5]], requires_grad=True)
+   u_pred = pinn(x)
+   print(u_pred)
+
+What's next?
+------------
+
+* Walk through the `Introductory Tutorial <tutorial17/tutorial.html>`_ for a deeper introduction.
+* Explore the :doc:`API reference </_rst/_code>` for all available components.
+* Read the :doc:`tutorials </_tutorial>` for domain-specific guides (Neural Operators, Supervised Learning, etc.).