2024/06/20 by Luka Grbčić, Minok Park, Grbcic, Luka +7
Earth and Planetary Sciences · Environmental Science · #3D Surveying and Cultural Heritage #Applied Physics (physics.app-ph) #Computational Engineering #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Finance #Machine Learning (cs.LG) #Optics (physics.optics) #Optimization and Control (math.OC) #Remote Sensing and LiDAR Applications #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2407.03356
openalex publication_date 2024/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, we develop a surrogate-based optimization approach for designing such surfaces. The surrogate-based optimization framework employs the Random Forest algorithm and uses a greedy, prediction-based exploration strategy to identify the laser fabrication parameters that minimize the discrepancy relative to a user-defined target optical characteristics. We demonstrate the approach on two synthetic benchmarks and two specific cases of photonic surface inverse design targets. It exhibits superior performance when compared to other optimization algorithms across all benchmarks. Additionally, we demonstrate a technique of inverse design warm starting for changed target optical characteristics which enhances the performance of the introduced approach.