PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces, where traditional optimizers struggle. The algorithm leverages the latent space of a generative model for rapid sampling and employs a Pearson correlated surrogate model to predict the figure of merit of the true design metric. As a showcase example, PearSAN is applied to thermophotovoltaic (TPV) metasurface design by matching the working bands between a thermal radiator and a photovoltaic cell. PearSAN can work with any pretrained generative model with a discretized latent space, making it easy to integrate with VQ-VAEs and binary autoencoders. Its novel Pearson correlational loss can be used as both a latent regularization method, similar to batch and layer normalization, and as a surrogate training loss. We compare both to previous energy matching losses, which are shown to enforce poor regularization and performance, even with upgraded affine parameters. PearSAN achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods, with an improved maximum figure-of-merit gain.

翻译：PearSAN是一种适用于大设计空间逆向设计问题的机器学习辅助优化算法，传统优化器在此类问题上表现不佳。该算法利用生成模型的潜在空间进行快速采样，并采用皮尔逊相关代理模型来预测真实设计指标的性能指数。作为示例展示，PearSAN通过匹配热辐射体与光伏电池的工作波段，应用于热光伏（TPV）超表面设计。PearSAN可与任何具有离散化潜在空间的预训练生成模型协同工作，使其易于与VQ-VAE及二进制自编码器集成。其新颖的皮尔逊相关损失函数既可作为潜在正则化方法（类似于批归一化与层归一化），也可作为代理训练损失。我们将其与先前的能量匹配损失函数进行比较，结果表明即使采用升级的仿射参数，后者仍会导致较差的正则化效果与性能表现。PearSAN实现了97%的顶尖最大设计效率，相比先前方法提速至少一个数量级，并获得了更高的最大性能指数增益。