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    "# Steady State Flow with Neural Networks\n",
    "\n",
    "*In aerodynamics related design, analysis and optimization problems, flow fields are simulated using computational fluid\n",
    "dynamics (CFD) solvers. However, CFD simulation is usually a computationally expensive, memory demanding and\n",
    "time consuming iterative process. These drawbacks of CFD limit opportunities for design space exploration and forbid\n",
    "interactive design. We will use a general and flexible approximation model for real-time prediction of non-uniform\n",
    "steady laminar flow in a 2D domain based on neural networks.*\n",
    "\n",
    "\n",
    "*This notebook contains a implementation of the paper [Convolutional Neural Networks for Steady Flow Approximation](https://www.autodeskresearch.com/publications/convolutional-neural-networks-steady-flow-approximation). The premise is to learn a mapping from boundary conditions to steady state fluid flow.  It is based on the implementation of [O. Hennigh](https://github.com/loliverhennigh/Steady-State-Flow-With-Neural-Nets)*\n",
    "\n",
    "## Problem description\n",
    "Our aim is to predict 2D flow around objects. The input is the boundary around which we want to calculate the flow. \n",
    "Here is an example of input data and the corresponding flow that was calculated using the Lattice Boltzmann method ([Mechsys](http://mechsys.nongnu.org/)).\n",
    "\n",
    "<img src=\"images/flow_example.png\">\n",
    "\n",
    "We will implement neural networks to predict the steady state flow. We will start with a simple fully connected model, then implement the convolutional model. Finally, we will implement an U-network model based on [this](https://arxiv.org/abs/1710.10352) paper.\n",
    "\n",
    "## Licensing\n",
    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)\n"
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