Using machine learning to create high-efficiency freeform illumination design tools
This work addresses the difficulty in illumination design for engineers and designers, offering a more efficient and accessible approach, though it appears incremental as it builds on existing methods with machine learning enhancements.
The paper tackles the computationally intensive problem of freeform illumination design by using orthogonal polynomials and neural networks to generalize relationships between surface shapes and design parameters, transforming it into a user-friendly tool, as demonstrated through the design of uniform square and rectangular patterns with tuneable aspect ratios and distances.
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.