@inproceedings{
sathujoda2023excitonpolariton,
title={Exciton-Polariton Condensates: A Fourier Neural Operator Approach},
author={Surya Sathujoda and Yuan Wang and Kanishk Gandhi},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=D1OcdXlFcL}
}
Advancements in semiconductor fabrication over the past decade have catalyzed extensive research into all-optical devices driven by exciton-polariton condensates. Preliminary validations of such devices, including transistors, have shown encouraging results even under ambient conditions. A significant challenge still remains for large scale application however: the lack of a robust solver that can be used to simulate complex nonlinear systems which require an extended period of time to stabilize. Addressing this need, we propose the application of a machine-learning-based Fourier Neural Operator approach to find the solution to the Gross-Pitaevskii equations coupled with extra exciton rate equations. This work marks the first direct application of Neural Operators to an exciton-polariton condensate system. Our findings show that the proposed method can predict final-state solutions to a high degree of accuracy almost 1000 times faster than CUDA-based GPU solvers. Moreover, this paves the way for potential all-optical chip design workflows by integrating experimental data.
Exciton-Polariton Condensates: A Fourier Neural Operator Approach
Surya Teja Sathujoda Yuan Wang Kanishk Gandhi