[2604.02184] Neural network methods for two-dimensional finite-source reflector design

Computer Science > Machine Learning arXiv:2604.02184 (cs) [Submitted on 2 Apr 2026] Title:Neural network methods for two-dimensional finite-source reflector design Authors:Roel Hacking, Lisa Kusch, Koondanibha Mitra, Martijn Anthonissen, Wilbert IJzerman View a PDF of the paper titled Neural network methods for two-dimensional finite-source reflector design, by Roel Hacking and 4 other authors View PDF HTML (experimental) Abstract:We address the inverse problem of designing two-dimensional reflectors that transform light from a finite, extended source into a prescribed far-field distribution. We propose a neural network parameterization of the reflector height and develop two differentiable objective functions: (i) a direct change-of-variables loss that pushes the source distribution through the learned inverse mapping, and (ii) a mesh-based loss that maps a target-space grid back to the source, integrates over intersections, and remains continuous even when the source is discontinuous. Gradients are obtained via automatic differentiation and optimized with a robust quasi-Newton method. As a comparison, we formulate a deconvolution baseline built on a simplified finite-source approximation: a 1D monotone mapping is recovered from flux balance, yielding an ordinary differential equation solved in integrating-factor form; this solver is embedded in a modified Van Cittert iteration with nonnegativity clipping and a ray-traced forward operator. Across four benchmarks -- contin...