  Neural network-based methods for solving differential equations have been
gaining traction. They work by improving the differential equation residuals of
a neural network on a sample of points in each iteration. However, most of them
employ standard sampling schemes like uniform or perturbing equally spaced
points. We present a novel sampling scheme which samples points adversarially
to maximize the loss of the current solution estimate. A sampler architecture
is described along with the loss terms used for training. Finally, we
demonstrate that this scheme outperforms pre-existing schemes by comparing both
on a number of problems.
