用相关性引导的生成模型加速逆向设计，效率提升超10倍。
PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing
- 基于生成模型潜空间快速采样，用皮尔逊相关损失构建代理模型。
- 在热光伏超表面设计中实现97%最高效率，比之前快至少10倍。
- 适用于VQ-VAE等模型，可作正则化或训练损失，适合高效逆向设计场景。
PearSAN是一种机器学习辅助优化算法，适用于传统优化器难以处理的大设计空间逆向设计问题。该方法利用生成模型的潜空间进行快速采样，并采用皮尔逊相关代理模型预测真实设计指标的性能。以热光伏（TPV）超表面设计为例，通过匹配热辐射源与光伏电池的工作频带实现优化。PearSAN可兼容任何具有离散潜空间的预训练生成模型，如VQ-VAE和二值自编码器。其新颖的皮尔逊相关损失既可用作潜空间正则化（类似批归一化、层归一化），也可作为代理模型训练损失。相比以往的能量匹配损失，该方法表现出更优的正则化效果和性能，即使使用升级的仿射参数亦然。实验显示，PearSAN达到97%的最大设计效率，较此前方法提速至少一个数量级，且最大性能增益更高。
原文摘要 · Abstract (English)
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.
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