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.

翻译：具有特定光学特性的光子表面在各种能量收集与存储系统中正变得日益重要。本研究开发了一种基于代理的优化方法，用于设计此类表面。该代理优化框架采用随机森林算法，并运用基于预测的贪婪探索策略，以识别能够最小化与用户定义目标光学特性之间差异的激光制造参数。我们在两个合成基准测试和两个光子表面逆向设计目标的具体案例上验证了该方法。与所有基准测试中的其他优化算法相比，该方法展现出优越的性能。此外，我们还展示了一种针对变化目标光学特性的逆向设计热启动技术，该技术进一步提升了所提出方法的性能。