We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

翻译：我们展示了一种用于光子表面逆向设计的多保真度机器学习集成框架，该框架基于我们通过高通量飞秒激光加工制备的11,759个样本数据集进行训练。该多保真度集成框架结合了用于生成设计方案的初始低保真度模型，以及通过局部优化改进这些方案的高保真度模型。该组合式多保真度集成框架能够生成多组不同的激光加工参数集，每组参数均能以高精度（均方根误差<2%）实现相同的目标输入光谱发射率。SHapley可加性解释分析揭示了激光参数与光谱发射率之间复杂关系的透明模型可解释性。最后，通过制备并评估该集成框架为提升能量收集器件效率所生成的光子表面设计，我们在实验上验证了该多保真度集成框架的有效性。我们的方法为推进能量收集应用中光子表面的逆向设计提供了强有力的工具。