PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing

• URL: http://arxiv.org/abs/2412.19284v1
• Date: Thu, 26 Dec 2024 17:02:19 GMT
• Title: PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing
• Authors: Michael Bezick, Blake A. Wilson, Vaishnavi Iyer, Yuheng Chen, Vladimir M. Shalaev, Sabre Kais, Alexander V. Kildishev, Alexandra Boltasseva, Brad Lackey,
• Abstract summary: PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces. It uses a Pearson correlated surrogate model to predict the figure of merit of the true design metric. It achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods.
• Score: 66.27103948750306
• License:
• 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.

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