Deep Learning in Optical Engineering: Applications in Design, Simulation, and Performance
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: 1 October 2026
Special Issue Editor
Interests: optical measurement; 3D reconstruction; pattern recognition; machine vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning has injected intelligent impetus into the entire chain of optical engineering, forging close connections among the core links of optical material research and development, system design, processing and manufacturing, and testing. On the material front, it enables precise prediction of key parameters such as refractive index, thereby expediting the screening of novel functional optical materials. In the design phase, it achieves inverse intelligent design of optical devices, surmounting the limitations of traditional forward design methodologies. During the processing stage, it dynamically adjusts process parameters based on visual monitoring, effectively minimizing surface form errors. In the field of optical inspection, it facilitates rapid identification of micro-defects in components and accurate analysis of imaging performance indicators, significantly boosting testing efficiency and precision. This addresses the inherent drawbacks of traditional testing methods, such as strong subjectivity and high missed detection rates, and provides a robust guarantee for the quality control of optical products.
Dr. Dongliang Zheng
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- R&D of optical materials
- optical design
- optical inspection
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