Artificial intelligence inspired freeform optics design: a review
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 70 references that are in the map. First posted on arXiv 18 Sep 2024.
Builds on
3 of 70 references.
References · 3
- 2021Dive into Deep Learningcited by 111
- 2023On the use of deep learning for phase recoverycited by 20
- 2023Gradient descent-based freeform optics design using algorithmic differentiable non-sequential ray tracingcited by 2
Built on by
Not cited yet.