- A Unified Framework for Scientific Machine Learning
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-[](https://github.com/mathLab/PINA/actions/workflows/pages/pages-build-deployment)
-[](https://pypi.org/project/pina-mathlab/)
-[](https://pypi.org/project/pina-mathlab/)
-[](https://joss.theoj.org/papers/10.21105/joss.05352)
-[](https://github.com/mathLab/PINA/blob/main/LICENSE.rst)
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-
-[Getting Started](https://github.com/mathLab/PINA/tree/master/tutorials#pina-tutorials) |
-[Documentation](https://mathlab.github.io/PINA/) |
-[Contributing](https://github.com/mathLab/PINA/blob/master/CONTRIBUTING.md)
-
-**PINA** is an open-source Python library designed to simplify and accelerate the development of Scientific Machine Learning (SciML) solutions. Built on top of [PyTorch](https://pytorch.org/), [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), and [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/), PINA provides an intuitive framework for defining, experimenting with, and solving complex problems using Neural Networks, Physics-Informed Neural Networks (PINNs), Neural Operators, and more.
+
+ A Unified Framework for Scientific Machine Learning
+
+
+
+
+
+ PINA is an open-source Python library designed to simplify and accelerate the development of
+ Scientific Machine Learning (SciML) solutions, including PINNs, Neural Operators,
+ data-driven modeling, and more.
+
+
+
+
Built on top of
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+
News & Announcements
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+
+
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+ [YYYY-MM-DD] – Short announcement headline.
+ More
+
+
+ [YYYY-MM-DD] – Another update: new release / tutorial / paper / feature.
+ Details
+
+ Want the full history?
+ See the Releases page and the
+ Changelog (if present).
+
+
+
+
+
What's PINA
+
+PINA provides an intuitive framework for defining, experimenting with, and solving complex problems using Neural Networks, Physics-Informed Neural Networks (PINNs), Neural Operators, and more.
- **Modular Architecture**: Designed with modularity in mind and relying on powerful yet composable abstractions, PINA allows users to easily plug, replace, or extend components, making experimentation and customization straightforward.
@@ -45,51 +86,117 @@ SPDX-License-Identifier: Apache-2.0
-## Installation
+
-### Installing a stable PINA release
-**Install using pip:**
-```sh
-pip install "pina-mathlab"
-```
-**Install from source:**
-```sh
-git clone https://github.com/mathLab/PINA
+
+
+
+
+
+To install additional packages required for development, tests, docs, or tutorials:
+
+
+
pip install "pina-mathlab[extras]"
+
+
Available extras:
+
+
+
dev for development purposes
+
test for running tests locally
+
doc for building documentation locally
+
tutorial for running tutorials
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+
PINA for Dummies
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+
+
+
+Solving a differential problem in PINA follows a clean four-step pipeline:
+
+
+
+
+ Define the problem and constraints using the
+ Problem API.
+
+
+ Design your model using PyTorch, PyTorch Geometric, or import from the
+ Model API.
+
+
+ Select or build a Solver using the
+ Solver API.
+
+
+ Train with the
+ Trainer API,
+ powered by PyTorch Lightning.
+
+
-**Install with extra packages:**
+```mermaid
+flowchart LR
+ STEP1["
Problem and Data
Define the mathematical problem Identify constraints or import data"]
+ STEP2["
Model Design
Build a PyTorch module Choose or customize a model"]
+ STEP3["
Solver Selection
Use available solvers or define your own strategy"]
+ STEP4["
Training
Optimize the model with PyTorch Lightning"]
-To install extra dependencies required to run tests or tutorials directories, please use the following command:
-```sh
-pip install "pina-mathlab[extras]"
+ STEP1 e1@--> STEP2
+ STEP2 e2@--> STEP3
+ STEP3 e3@--> STEP4
+ e1@{ animate: true }
+ e2@{ animate: true }
+ e3@{ animate: true }
```
-Available extras include:
-* `dev` for development purpuses, use this if you want to [Contribute](https://github.com/mathLab/PINA/blob/master/CONTRIBUTING.md#contributing-to-pina).
-* `test` for running test locally.
-* `doc` for building documentation locally.
-* `tutorial` for running [Tutorials](https://github.com/mathLab/PINA/tree/master/tutorials#pina-tutorials).
-
-## Quick Tour for New Users
-Solving a differential problem in **PINA** follows the *four steps pipeline*:
-1. Define the problem to be solved with its constraints using the [Problem API](https://mathlab.github.io/PINA/_rst/_code.html#problems).
+
+Want to dive deeper? Check out the official
+Tutorials.
+
-2. Design your model using PyTorch, or for graph-based problems, leverage PyTorch Geometric to build Graph Neural Networks. You can also import models directly from the [Model API](https://mathlab.github.io/PINA/_rst/_code.html#models).
+
+
-3. Select or build a Solver for the Problem, e.g., supervised solvers, or physics-informed (e.g., PINN) solvers. [PINA Solvers](https://mathlab.github.io/PINA/_rst/_code.html#solvers) are modular and can be used as-is or customized.
+
+
+
PINA by Examples
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+
+
-4. Train the model using the [Trainer API](https://mathlab.github.io/PINA/_rst/trainer.html) class, built on PyTorch Lightning, which supports efficient, scalable training with advanced features.
+
+
Data-Driven Modeling Example
-Do you want to learn more about it? Look at our [Tutorials](https://github.com/mathLab/PINA/tree/master/tutorials#pina-tutorials).
-
-### Solve Data Driven Problems
-Data driven modelling aims to learn a function that given some input data gives an output (e.g. regression, classification, ...). In PINA you can easily do this by:
-```python
+```python
import torch
from pina import Trainer
from pina.model import FeedForward
@@ -101,25 +208,37 @@ target_tensor = input_tensor.pow(3)
# Step 1. Define problem
problem = SupervisedProblem(input_tensor, target_tensor)
-# Step 2. Design model (you can use your favourite torch.nn.Module in here)
-model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64])
-# Step 3. Define Solver
-solver = SupervisedSolver(problem, model, use_lt=False)
+
+# Step 2. Define model
+model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64])
+
+# Step 3. Define solver
+solver = SupervisedSolver(problem, model, use_lt=False)
+
# Step 4. Train
-trainer = Trainer(solver, max_epochs=1000, accelerator='gpu')
+trainer = Trainer(solver, max_epochs=1000, accelerator="gpu")
trainer.train()
```
-### Solve Physics Informed Problems
-Physics-informed modeling aims to learn functions that not only fit data, but also satisfy known physical laws, such as differential equations or boundary conditions. For example, the following differential problem:
+
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+
+If PINA has been significant in your research and you would like to acknowledge it, please cite:
+
+
+
Coscia, D., Ivagnes, A., Demo, N., & Rozza, G. (2023).
+Physics-Informed Neural networks for Advanced modeling.
+Journal of Open Source Software, 8(87), 5352.
+
+
Or in BibTeX format:
+
+
@article{coscia2023physics,
+ title={Physics-Informed Neural networks for Advanced modeling},
+ author={Coscia, Dario and Ivagnes, Anna and Demo, Nicola and Rozza, Gianluigi},
+ journal={Journal of Open Source Software},
+ volume={8},
+ number={87},
+ pages={5352},
+ year={2023}
+}
+
+
+
+
API(NA)
+
+
+Here is a high-level overview of PINA’s main modules. For full details, refer to the
+documentation.
+
```mermaid
flowchart TB
PINA["
pina
The basic module including `Condition`, LabelTensor, `Graph` and `Trainer` API"]
@@ -205,54 +395,4 @@ flowchart TB
BLOCK --> ADAPT
OPTIM --> LOSS
DATA --> CONDITION
-
-```
-### Steps to Follow
-
-```mermaid
-flowchart LR
- STEP1["
Problem and Data
Define the mathematical problem Identify constraints or import data"]
- STEP2["
Model Design
Build a PyTorch module Choose or customize a model"]
- STEP3["
Solver Selection
Use available solvers or define your own strategy"]
- STEP4["
Training
Optimize the model with PyTorch Lightning"]
-
- STEP1 e1@--> STEP2
- STEP2 e2@--> STEP3
- STEP3 e3@--> STEP4
- e1@{ animate: true }
- e2@{ animate: true }
- e3@{ animate: true }
-```
-## Contributing and Community
-
-We would love to develop PINA together with our community! Best way to get started is to select any issue from the [`good-first-issue` label](https://github.com/mathLab/PINA/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22). If you would like to contribute, please review our [Contributing Guide](CONTRIBUTING.md) for all relevant details.
-
-We warmly thank all the contributors that have supported PINA so far:
-
-
-
-
-
-Made with [contrib.rocks](https://contrib.rocks).
-
-## Citation
-If **PINA** has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper:
-
-```
-Coscia, D., Ivagnes, A., Demo, N., & Rozza, G. (2023). Physics-Informed Neural networks for Advanced modeling. Journal of Open Source Software, 8(87), 5352.
-```
-
-Or in BibTex format
-```
-@article{coscia2023physics,
- title={Physics-Informed Neural networks for Advanced modeling},
- author={Coscia, Dario and Ivagnes, Anna and Demo, Nicola and Rozza, Gianluigi},
- journal={Journal of Open Source Software},
- volume={8},
- number={87},
- pages={5352},
- year={2023}
- }
```
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+.gif)
-### Installing a stable PINA release
-**Install using pip:**
-```sh
-pip install "pina-mathlab"
-```
-**Install from source:**
-```sh
-git clone https://github.com/mathLab/PINA
+
+
+
+
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+