PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing
This addresses inverse design problems, such as thermophotovoltaic metasurface design, for researchers and engineers, but it is incremental as it builds on existing generative models and optimization techniques.
The paper tackles the problem of inverse design in large design spaces by introducing PearSAN, a machine learning-assisted optimization algorithm that uses a Pearson correlated surrogate model and generative model latent space. It achieves a state-of-the-art maximum design efficiency of 97% and is at least an order of magnitude faster than previous methods.
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.