.. DeterministicParticleFlowControl documentation master file, created by sphinx-quickstart on Tue Dec 14 10:29:06 2021. This should at least contain the root `toctree` directive.

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 Deterministic Particle Flow Control documentation
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Welcome to DeterministicParticleFlowControl's documentation!
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`Deterministic Particle Flow Control` 
is a deterministic particle-based stochastic optimal control framework implemented in Python. 


.. image:: _figs/waterfall_plot.png
  :width: 500
  :align: center
  :alt: Schematic depicting the proposed framework. A probability flow ρ is propagated forward in time until time T. Then employing the logarithmic gradients of the forward probability flow, a time reversed flow is propagated from T to 0. The time- and space dependent optimal controls are extracted at every time step as the difference of the logarithmic gradients of the two sampled flows rescaled by the noise variance of the process.
  

For a fast introduction on how to use it, please refer to the `README file 
<https://github.com/dimitra-maoutsa/DeterministicParticleFlowControl/blob/main/README.md>`_ in the Github repository. Here you will find more detailed descriptions of the components. 

(this documentation is getting updated.)




Contents:

.. toctree::
   :maxdepth: 3
   
   theory
   auto_examples/index
   api
   lost
   
   
   
   
   
   This is a documentation of the software using sphinx_. 

.. _sphinx: http://sphinx-doc.org/