AI 驱动的激光参数搜索：基于贪心代理优化的光子表面反演设计
摘要
具有特定光学特征的光子表面正在 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。
引用
@article{arxiv.2407.03356,
title = {AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization},
author = {Luka Grbcic and Minok Park and Juliane Müller and Vassilia Zorba and Wibe Albert de Jong},
journal= {arXiv preprint arXiv:2407.03356},
year = {2024}
}