A “super-mirror” having ultrahigh infrared reflectance is achieved by an optimized photonic contrast grating metasurface. Finding ways to achieve this exceptional performance can be enabled by implementing global optimization and machine learning elements, such as Bayesian optimization and genetic algorithm. Here, we acquired an optimized grating design made of high-index germanium, which excites resonances that result in ultralow emittance at certain wavelengths. Our optimizations assisted in the discovery of hybridized coupling of Fabry-Pérot modes and guided modes in a monolithic microscale multilayered coating. We demonstrate constraints in the given geometric variable ranges improves the overall performance of algorithms. We also show the enhanced performance of a deep learning Feedforward Neural Network, which is implemented as the inverse design using the network trained with dataset obtained from Bayesian optimization and Genetic Algorithm approaches. The performance of the Feedforward Neural Network-assisted design produced normal emissivity difference by only +3.5 %, with lower sensitivity to grating dimensional parameter variations. The improvement is achieved by predicting and better understanding of the optical physics of resonant gratings. The proposed few-layer grating coating can be applied to space components, enclosures, and vessels to suppress thermal radiative heat loss.

中文翻译：

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基于深度学习辅助优化算法的谐振模式超表面热超级反射镜


具有超高红外反射率的“超级镜子”是通过优化的光子对比光栅超表面实现的。通过实施全局优化和机器学习元素（例如贝叶斯优化和遗传算法），可以找到实现这种卓越性能的方法。在这里，我们获得了由高折射率锗制成的优化光栅设计，它激发共振，导致某些波长的超低发射率。我们的优化有助于在整体微尺度多层涂层中发现 Fabry-Pérot 模式和导向模式的杂化耦合。我们证明了给定几何变量范围内的约束可以提高算法的整体性能。我们还展示了深度学习前馈神经网络的增强性能，该网络使用从贝叶斯优化和遗传算法方法获得的数据集进行训练的网络实现为逆向设计。前馈神经网络辅助设计的性能仅产生 +3.5% 的正常发射率差异，对光栅尺寸参数变化的敏感度较低。这种改进是通过预测和更好地理解谐振光栅的光学物理学来实现的。所提出的几层格栅涂层可以应用于空间部件、外壳和容器，以抑制热辐射热损失。