Using machine learning to create high-efficiency freeform illumination design tools

TL;DR

This paper introduces a machine learning-based method that leverages orthogonal polynomials and neural networks to streamline and enhance the design process of freeform illumination systems, making it more efficient and user-friendly.

Contribution

The paper presents a novel approach combining orthogonal polynomials and neural networks to model and optimize freeform illumination design, reducing computational complexity.

Findings

Successfully designed uniform square illumination patterns from off-axis positions

Generated rectangular patterns with tunable aspect ratios and distances

Demonstrated improved efficiency and user experience in illumination design

Abstract

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.