  We present a method for improving the efficiency and user experience of
freeform illumination design with machine learning. By utilizing orthogonal
polynomials to interface with artificial neural networks, we are able to
generalize relationships between freeform surface shapes and design parameters.
Then, by training the network to generalize the relationship between high-level
design goals and final performance, we were able to transform what is
traditionally a difficult and computationally intensive problem into a compact,
user friendly form. The potential of the proposed method is demonstrated
through the design of uniform square patterns from off-axis positions and
rectangular patterns of tuneable aspect ratios and distances from the target.
