.. DeePCK documentation master file, created by
   sphinx-quickstart on Wed Aug 17 21:17:38 2022.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.


DeePCK |release|
================

|Build Status| |GitHub release| |Documentation Status| |Last Commit| |Sphinx|

.. |Build Status| image:: https://img.shields.io/badge/build-passing-brightgreen.svg
   :target: https://github.com/Seauagain/DeePCK-Documentation

.. |GitHub release| image:: https://img.shields.io/github/release/Seauagain/DeePCK-Documentation.svg
   :target: https://github.com/Seauagain/DeePCK-Documentation/releases/

.. |Documentation Status| image:: https://readthedocs.org/projects/deepck/badge/?version=latest
   :target: https://deepck.readthedocs.io/en/latest/

.. |Last Commit| image:: https://img.shields.io/github/last-commit/Seauagain/DeePCK-Documentation?color=orange
   :target: https://github.com/Seauagain/DeePCK-Documentation/commit/main

.. |Sphinx| image:: https://img.shields.io/badge/Made%20with-Sphinx-1f425f.svg



.. only:: html or man

   :Author:   Yuxiao Yi
   :Contact:  yxyee@foxmail.com
   :Date:     |today|


**DeePCK** is a Python package and aims at building alternative DNN models for 
Chemical Kinetics stiff Ordinary Differential Equations in numerical combustion simulation.
DeePCK consists of three fundamental modules: *Dataset*, *Model* and *Visualization*.
The source code of DeePCK will be publicly released on GitHub in the future.
Further theoretical and technical details could be viewed in the paper: CombustFlame2022_

.. _CombustFlame2022: https://doi.org/10.1016/j.combustflame.2022.112319

The *Dataset Module* currently provide several sampling  methods, such as adaptive zero dimensional
manifold (**zero D**), one dimensional flame (**one D**), multi-scale sampling (**MS**) and modified multi-scale sampling methods (**MMS**). 
Besides, the label generating tool and data processing procedure are also included. All of them support massive parallelization via ``multiprocessing`` or ``MPI``.

The *Model Module* deals with the framework design and training procedure of deep neural networks (DNNs), and the calling of DNNs for downstream tasks  e.g. single-step prediction 
or temporal evolution. For instance, fig. :num:`zero` demostrates the zero-dimensional homogeous ignition process of CH4/air mixture simulated by Cantera and a well-trained DNN, respectively.

.. _zero:
.. figure:: _pic/Methane_GRI3.0_134wAdapManCH4constP_2022-07-31_Phi=1_T=1600_P=1_epoch=5000_all1.png
   :width: 90%
   :class: no-scaled-link
   :align: center

   .. 

   Fig :num:`zero` : Auto-ignition of CH4/air mixture.

Then the `Visualization Module` offers the interface to draw distribution and phase diagrams w.r.t typical chemical/thermodynamic datasets organized as :math:`T,P,Y`.

The model provided by DeePCK has coupled with stardand CFD codes to replace classical ODE solvers and shows promosing robustness and accuracy in high-dimensional flame simulation. 
We will continue to improve the corresponding source code package and documentation. If you have any issue or suggestion, feel free to contact the author via the email.


User Guide
==========

.. toctree::
   :maxdepth: 2

   Setup/setup.rst
   Quick Start/quickstart.rst

.. set caption by  :caption: Contents:


Modules
========

.. toctree::
   :maxdepth: 2
   :caption: Code API

   API/ModelDefine
   API/ModelTrain
   API/DataLoad
   API/ModelUse
   API/VisualArt
   API/util
   API/config

   API/utils_data
   API/MechConvert
   API/yaml2ck

   
    

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

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