This is the complete repo for three research works:
In this work, we introduce OptoGPT (Opto Generative Pretrained Transformer), a decoder-only transformer, to solve inverse design of multi-layer thin film structures.
Check our paper at: https://www.oejournal.org/article/doi/10.29026/oea.2024.240062
The code can be found in folder /optogpt
OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures
In this work, we propose the Opto-Layer (OL) Transformer to act as a universal surrogate simulator for fast and efficient simulation of multilayer thin film structures.
Check our paper at: https://arxiv.org/abs/2305.11984
The code can be found in folder /optogpt
Solving Out-of-Distribution Challenges in Optical Foundation Models using Self-Improving Data Augmentation
In this work, we propose a self-improving data augmentation technique by leveraging neural networks' extrapolation ability. Using this method, we show significant improvement in various real-applicative design tasks with minimum fine-tuning, which can also be potentially generalized to inverse scientific foundation models.
Check our paper at: https://openreview.net/forum?id=8jqhElTmNP
The code can be found in folder /self_improving
To cite this work:
@article{ma2024optogpt,
title={OptoGPT: a foundation model for inverse design in optical multilayer thin film structures},
author={Ma, Taigao and Wang, Haozhu and Guo, L Jay},
journal={Opto-Electronic Advances},
volume={7},
number={7},
year={2024},
publisher={Opto-Electronic Advance}
}
@article{ma2023ol,
title={OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures},
author={Ma, Taigao and Wang, Haozhu and Guo, L Jay},
journal={arXiv preprint arXiv:2305.11984},
year={2023}
}
@inproceedings{ma2024solving,
title={Solving Out-of-Distribution Challenges in Optical Foundation Models using Self-Improving Data Augmentation},
author={Ma, Mingqian and Ma, Taigao and Guo, L Jay},
booktitle={Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities, and Challenges}
}