Generative Machine Learning for Photonic Design

Machine learning, as a study of algorithms that automate prediction and decision-making based on complex data, has become one of the most effective tools in the exploitation of artificial intelligence. In recent years, scientific communities have been gradually merging data-driven approaches with research endeavors, enabling dramatic progress in revealing underlying mechanisms, predicting essential properties, and discovering unconventional phenomena. Very recently, generative machine learning models have attracted growing attention in the design and optimization of complex photonic structures. In this chapter, we overview the advantages of the generative design strategy for photonic components and devices over traditional optimization schemes with both theoretical analysis and practical design examples.