  Many scientific and industrial applications require solving Partial
Differential Equations (PDEs) to describe the physical phenomena of interest.
Some examples can be found in the fields of aerodynamics, astrodynamics,
combustion and many others. In some exceptional cases an analytical solution to
the PDEs exists, but in the vast majority of the applications some kind of
numerical approximation has to be computed. In this work, an alternative
approach is proposed using neural networks (NNs) as the approximation function
for the PDEs. Unlike traditional numerical methods, NNs have the property to be
able to approximate any function given enough parameters. Moreover, these
solutions are continuous and derivable over the entire domain removing the need
for discretization. Another advantage that NNs as function approximations
provide is the ability to include the free-parameters in the process of finding
the solution. As a result, the solution can generalize to a range of situations
instead of a particular case, avoiding the need of performing new calculations
every time a parameter is changed dramatically decreasing the optimization
time. We believe that the presented method has the potential to disrupt the
physics simulation field enabling real-time physics simulation and geometry
optimization without the need of big supercomputers to perform expensive and
time consuming simulations
