  Recently, there has been a lot of interest in using neural networks for
solving partial differential equations. A number of neural network-based
partial differential equation solvers have been formulated which provide
performances equivalent, and in some cases even superior, to classical solvers.
However, these neural solvers, in general, need to be retrained each time the
initial conditions or the domain of the partial differential equation changes.
In this work, we posit the problem of approximating the solution of a fixed
partial differential equation for any arbitrary initial conditions as learning
a conditional probability distribution. We demonstrate the utility of our
method on Burger's Equation.
