{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/large-area-optimization-of-meta-lens-via-data","title":"Large area optimization of meta-lens via data-free machine learning","arxiv_id":"2212.10703","date":"2022-12-21","proceeding":null,"authors":["Maksym V. Zhelyeznyakov","Johannes E. Froch","Anna Wirth-Singh","Jaebum Noh","Junsuk Rho","Steven L. Brunton","Arka Majumdar"],"abstract":"Sub-wavelength diffractive optics meta-optics present a multi-scale optical system, where the behavior of constituent sub-wavelength scatterers, or meta-atoms, need to be modelled by full-wave electromagnetic simulations, whereas the whole meta-optical system can be modelled using ray/ wave optics. Current simulation techniques for large-scale meta-optics rely on the local phase approximation (LPA), where the coupling between dissimilar meta-atoms are completely neglected. Here we introduce a physics-informed neural network, which can efficiently model the meta-optics while still incorporating all of the coupling between meta-atoms. Unlike existing deep learning techniques which generally predict the mean transmission and reflection coefficients of meta-atoms, we predict the full electro-magnetic field distribution. We demonstrate the efficacy of our technique by designing 1mm aperture cylindrical meta-lenses exhibiting higher efficiency than the ones designed under LPA. We experimentally validated the maximum intensity improvement (up to $53\\%$) of the inverse-designed meta-lens. Our reported method can design large aperture $(\\sim 10^4-10^5\\lambda)$ meta-optics in a reasonable time (approximately 15 minutes on a graphics processing unit) without relying on any approximation.","url_abs":"https://arxiv.org/abs/2212.10703v1","url_pdf":"https://arxiv.org/pdf/2212.10703v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"large-area-optimization-of-meta-lens-via-data","repo_url":"https://github.com/demroz/pinn-ms","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}