AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization
This work addresses the need for efficient inverse design in photonic surfaces for energy harvesting and storage systems, presenting an incremental improvement over existing optimization methods.
The study tackled the problem of designing photonic surfaces with specific optical characteristics by developing a surrogate-based optimization approach using Random Forest and a greedy exploration strategy, demonstrating superior performance across benchmarks and introducing a warm-starting technique for enhanced efficiency.
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.