NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
Authors: Jiaqi Gu, Zhengqi Gao, Chenghao Feng, Hanqing Zhu, Ray Chen, Duane Boning, David Pan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4.1 Experiment setup... In Table 1, we compare four models... Datasets. We focus on widely applied multi-mode interference (MMI) photonic devices... Table 1: Comparison of parameter count, train error, and test error on two benchmarks among four different models. |
| Researcher Affiliation | Academia | Jiaqi Gu1, Zhengqi Gao2, Chenghao Feng1, Hanqing Zhu1, Ray T. Chen1, Duane S. Boning2, David Z. Pan1 1The University of Texas at Austin, 2Massachusetts Institute of Technology |
| Pseudocode | No | The paper describes its model architecture and components in text and diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at link. |
| Open Datasets | No | We generate our customized MMI device simulation dataset using an open-source FDFD simulator angler [16]. The tunable MMI dataset has 5.5 K single-source training data, 614 validation data, and 1.5 K multi-source test data. The etched MMI dataset has 12.4 K single-source training data, 1.4 K validation data, and 1.5 K multi-source test data. The paper describes a custom-generated dataset but does not provide concrete access information (link, DOI, repository, or formal citation with author/year for the dataset itself). |
| Dataset Splits | Yes | For the tunable MMI dataset, we split all 7,680 examples into 72% training data, 8% validation data, and 20% test data. For the etched MMI dataset, we split all 15,360 examples into 81% training data, 9% validation data, and 10% test data. |
| Hardware Specification | Yes | All experiments are conducted on a machine with Intel Core i7-9700 CPUs and an NVIDIA Quadro RTX 6000 GPU. |
| Software Dependencies | Yes | We implement all models and training logic in Py Torch 1.10.2. |
| Experiment Setup | Yes | For training from scratch, we set the number of epochs to 200 with an initial learning rate of 0.002, cosine learning rate decay, and a mini-batch size of 12. |