PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing
Abstract
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.
Cite
@article{arxiv.2412.19284,
title = {PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing},
author = {Michael Bezick and Blake A. Wilson and Vaishnavi Iyer and Yuheng Chen and Vladimir M. Shalaev and Sabre Kais and Alexander V. Kildishev and Alexandra Boltasseva and Brad Lackey},
journal= {arXiv preprint arXiv:2412.19284},
year = {2025}
}