AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing
A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Data-Science Based Techniques in Photonics".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 766
Special Issue Editors
Interests: AI for photonics; lensless fiber endomicroscopy; digital holography; optical trapping; optical tomography; wavefront shaping
Special Issues, Collections and Topics in MDPI journals
Interests: high power laser technology; nonlinear optics
Special Issues, Collections and Topics in MDPI journals
Interests: adaptive optics; wavefront sensing; laser communication; flow measurements
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent photonics; physics-informed deep learning; multimode fiber; optical computing; wavefront shaping; optical communication
Special Issue Information
Dear Colleagues,
AI for photonics replaces handcrafted optics with learning-driven design and closed-loop control, delivering faster development and robust performance in complex environments. It enables inverse-designed devices, adaptive wavefront shaping, intelligent imaging, and reliable control of nonlinear/high-power lasers—shrinking the gap from lab prototypes to real-world systems.
This Special Issue on “AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing” welcomes basic, methodological, and applied contributions, as regular and review papers, covering (but not limited to):
- Development and validation of AI-enhanced microscopic or imaging instruments;
- Modelling of light–matter interactions and learning-based methods;
- Processing of multidimensional optical data using machine-learning methods;
- Development of multimodal, multispectral, and multiscale approaches;
- Advancement of adaptive optics and wavefront shaping in complex media and links;
- Optical computing methods or optical neural networks.
We look forward to contributions that connect rigorous physical modelling with data-driven intelligence, and that demonstrate compelling progress from fundamental concepts to robust, real-world photonic systems.
Dr. Jiawei Sun
Dr. Zhaohong Liu
Dr. Zeyu Gao
Dr. Qian Zhang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI for photonics and learning-driven optics
- inverse design and differentiable photonics
- adaptive optics and wavefront sensing or shaping
- digital holography and computational imaging
- lensless and fiber endomicroscopy
- optical tomography and quantitative phase imaging
- optical neural networks and photonic computing
- nonlinear optics and high-power or solid-state lasers
- stimulated Brillouin scattering and Raman sensing
- physics-informed machine learning and digital twins
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