.. nnodely documentation master file, created by
   sphinx-quickstart on Wed Oct 13 2021.
   You can adapt this file completely to your liking, but it should at least
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Welcome to nnodely's documentation!
====================================

.. image:: https://raw.githubusercontent.com/tonegas/nnodely/main/imgs/logo_white_info.png
     :target: https://github.com/tonegas/nnodely
     :alt: Open 


*nnodely* is a framework designed to facilitate the creation and deployment of **Model-Structured Neural Networks** (**MSNNs**). 
Modeling, control, and estimation of physical systems impose constraints that differ fundamentally from typical 
deep-learning tasks (e.g., images or text). In engineering applications, models often need to respect 
known physical laws or constraints, operate in real time, remain interpretable, and generalize reliably 
even when only limited experimental data are available. MS-NNs combine the learning capabilities of neural 
networks with structural priors grounded in physics, control and estimation theory, enabling: 


- **Data Efficiency**: By embedding structural priors, MS-NNs can learn effectively from limited data, reducing the need for extensive datasets.
- **Generalization**: The incorporation of domain knowledge allows MS-NNs to generalize better to unseen scenarios.
- **Interpretability**: The structured nature of MS-NNs enhances interpretability, allowing practitioners to understand and trust the model's predictions.
- **Real-time**: MS-NNs can be designed for real-time applications, making them suitable for control and estimation tasks in dynamic environments.

The main objective of the *nnodely* framework is to allow fast prototyping of MS-NNs for modeling, estimation and control of physical systems by embedding structural priors knowledge into the networks' architecture.

In this documentation you will find a comprehensive guide for getting started with *nnodely*, illustrating the main blocks that constitute the framework.



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       Repository
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       Getting Started
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       Applications
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Overview
-----------------------

.. This needs to be revised in order to explain at high level the phases of the workflow.
.. - :ref:`Modely <nnodely-modely>`: Main entry point of nnodely. It manages the composition of the MS-NNs, the connection between structural blocks and the training of the networks.
.. - :ref:`Model structured NN Inputs Outputs and Parameters <nnodely-msnn_ins_out_param>`: Description of the Input, Output and Parameter modules that can be used to build MS-NNs.
.. - :ref:`Model structured NN building blocks <nnodely-modules-layers>`: Overview of the different structural layers available in nnodely to build MS-NNs.
.. - :ref:`Training <nnodely-training>`: Explanation of the training procedures implemented in nnodely to train MS-NNs.




.. sidebar:: Overview
   
  Overview of the *nnodely* development pipeline. It spans model design (:ref:`PH1 <nnodely-modely>`), dataset construction aligned with the network architecture (:ref:`PH2 <nnodely-dataset-creation>`), training (:ref:`PH3 <nnodely-training>`), domain-specific validation (:ref:`PH4 <nnodely-validation>`), model export (:ref:`PH5 <nnodely-export>`), and composition of complex models (:ref:`PH6 <nnodely-model-composition>`). Ellipses indicate the pipeline phases, while rectangles denote the artifacts produced at each phase.


.. image:: https://raw.githubusercontent.com/tonegas/nnodely/docs/update/imgs/framework_p.png
   :width: 50%
   :alt: Framework




Table of Contents
==========================
.. toctree::
   :maxdepth: 2

   _autodoc/getting_started/index
   _autodoc/modely_class
   _autodoc/model_definition/index
   _autodoc/dataset_creation/index
   _autodoc/model_composition/index
   _autodoc/training/index
   _autodoc/validation/index
   _autodoc/inference/index
   _autodoc/export/index
   _autodoc/tutorials/index

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`