A beta-variational autoencoder (beta-VAE) to retrieve relevant degrees of freedom in a physical system

Joeri Lenaerts; Vincent Ginis

doi:10.1117/12.2632749

3 October 2022 A beta-variational autoencoder (beta-VAE) to retrieve relevant degrees of freedom in a physical system

Joeri Lenaerts, Vincent Ginis

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270A (2022) https://doi.org/10.1117/12.2632749
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

A popular framework to design optical components is inverse design. Here, one starts from a desired optical response and tries to find an optical design with exactly this response. In recent years, Machine Learning and in particular Deep Learning have been used in this regard. Many successes have been reported in the literature. What often arises in practice is the non-uniqueness problem. Multiple designs can lead to similar or even exactly the same optical responses. This is a complication, because most methods can only propose one design. The non-uniqueness problem can be mitigated by considering the degrees of freedom of the system. While multiple geometric parameters can correspond to the same response, the degrees of freedom define them uniquely. When working with these parameters, there is a one-to-one mapping between design and response. In order to find the degrees of freedom in an optical system, we propose a neural network architecture called a beta-VAE. We study its behaviour for analytical datasets of sine waves and damped harmonic oscillators. This gives us insight into the working of the neural network. We find that degrees of freedom can be retrieved for physical systems relevant to Optics.

Conference Presentation

Citation Download Citation

Joeri Lenaerts and Vincent Ginis "A beta-variational autoencoder (beta-VAE) to retrieve relevant degrees of freedom in a physical system", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270A (3 October 2022); https://doi.org/10.1117/12.2632749

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