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.

推动了全光器件研究的半导体制造技术进展已经十年，而初步验证显示出即使在环境条件下也能获得令人鼓舞的结果。然而，一个重大挑战依然存在：缺乏能够模拟需要长时间稳定的复杂非线性系统的强大求解器。为了解决这一需求，我们提出了应用基于机器学习的傅立叶神经运算器方法来寻找与额外激子速率方程耦合的 Gross-Pitaevskii 方程的解决方案。我们的研究结果显示，所提出的方法几乎比基于CUDA的GPU求解器快1000倍，并且为全光芯片设计工作流程集成实验数据铺平了道路。

激子-极化子凝聚：傅里叶神经算子方法