Artificial intelligence inspired freeform optics design: a review
<|reference_start|>Artificial intelligence inspired freeform optics design: a review: Integrating artificial intelligence (AI) techniques such as machine learning and deep learning into freeform optics design has significantly enhanced design efficiency, expanded the design space, and led to innovative solutions. This article reviews the latest developments in AI applications within this field, highlighting their roles in initial design generation, optimization, and performance prediction. It also addresses the benefits of AI, such as improved accuracy and performance, alongside challenges like data requirements, model interpretability, and computational complexity. Despite these challenges, the future of AI in freeform optics design looks promising, with potential advancements in hybrid design methods, interpretable AI, AI-driven manufacturing, and targeted research for specific applications. Collaboration among researchers, engineers, and designers is essential to fully harness AI's potential and drive innovation in optics.<|reference_end|>
@article{feng2024artificial,
    title={Artificial intelligence inspired freeform optics design: a review},
    author={Lei Feng, Jingxing Liao, Jingna Yang},
    journal={arXiv preprint arXiv:2410.03554},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03554},
    primaryClass={cs.LG physics.optics}
}