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2024

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07

Deep Learning Topics in Optical Design

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The application of deep learning in the field of optical design provides new opportunities and challenges for the innovation of photonic structures. In the future, with the continuous development and improvement of deep learning technology, it is believed that it will play a more important role in the field of optical design.

Introduction to Optical Design Lecturer in Deep Learning

The lecturer team comes from the national key university and the national "985 Project" physics and information interdisciplinary major, with many years of machine learning and research experience! His research direction involves the intersection of optical design and physics, deep learning, machine learning and so on. Has a wealth of knowledge and practical experience. Participated in a number of projects of the National Natural Science Foundation, including the publication of more than ten SCI papers and a national invention patent! He has served as a reviewer for several journals such as MDPI.

Lecturer Introduction to Deep Learning Computational Optical Imaging

The lecturer is from a top foreign university in optical imaging, and is good at computer vision and deep learning imaging research. In recent years, 15 SCI papers have been published and three invention patents have been granted. His research interests include image processing and computer vision, deep learning methods, physics-driven optical imaging, and cross-modal imaging.

Deep Learning Optical Design Goals

1. The training goal of optical network based on deep learning is mainly to cultivate senior professionals with modern optical theoretical foundation and deep learning technology knowledge. They not only need to be familiar with the principles of modern optics, but also need to master the principles and applications of deep learning algorithms, and be able to combine deep learning and modern optical principles to design devices with optical acceleration functions.

2. Grasp the tools needed to build a deep learning model and learn to build a deep learning development environment. Let beginners use the deep learning framework to build common neural network models, understand the problems in the model training process and master common solutions.

3. Be familiar with the development status of metamaterials, basically master the multi-physics simulation software, and be able to use the software to calculate the energy band of photonic crystals and post-process the simulation results. Understand the application of super surface in optical and quantum fields, learn to use simulation software to simulate the structure of super surface and analyze the results.

4. Know how MATLAB interacts with COMSOL and Python, learn how to use Python to process COMSOL exported data, and how to use MATLAB to export COMSOL data and process it into data that Python can read.

5. Understand the development status of silicon-based optical networks, know the principles of matrix decomposition, learn to use the deep learning framework to build an MZI-based model framework and apply it to deep learning examples.

6. In the future, optical chips with acceleration function can be designed based on on-chip optical networks using the acceleration function of light. Based on the diffraction network, a fast imaging system can be designed in free space to accelerate the image recognition of automatic driving.

7. Using the deep learning model, the disadvantages of the traditional design method based on full-wave simulation can be overcome, the target response of a given structure can be quickly given, and the process of optical design can be accelerated.

Deep learning to compute optical imaging targets

1. Master the typical optical imaging mechanism, understand its corresponding mathematical model and the problem of demand solution.

2. Master the typical optimization theory and method, and be able to solve the typical computational imaging inverse problem by designing the objective function.

3. Master the principles and applications of deep learning algorithms, be able to deploy and modify typical deep network models through python programming, and be used to solve typical computational optical imaging problems.

 

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