AI驱动的激光参数搜索：基于贪心代理优化的光子表面逆向设计
AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization
AI总结：
本文提出一种基于随机森林和贪心预测探索策略的代理优化方法，用于光子表面激光制造参数的逆向设计，并在多个基准上验证了其优于其他优化算法的性能。
摘要：
具有特定光学特性的光子表面在各类能量收集与存储系统中的应用日益重要。在本研究中，我们开发了一种基于代理的优化方法用于设计此类表面。该基于代理的优化框架采用随机森林（Random Forest）算法，并使用基于预测的贪心探索策略来识别激光制造参数，从而最小化与用户定义的目标光学特性之间的差异。我们在两个合成基准以及两个具体的光子表面逆向设计目标案例上验证了该方法。与所有基准上的其他优化算法相比，该方法表现出更优越的性能。此外，我们还展示了一种针对变化后的目标光学特性进行逆向设计热启动的技术，该技术进一步提升了所提出方法的性能。
英文摘要：
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.
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