光学中的人工智能和机器学习:教程

Ksenia Yadav, Serge Bidnyk, Ashok Balakrishnan
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摘要

在科学探索和实际应用领域,人工智能(AI)和机器学习(ML)的出现全面革新了解决问题的方法。本教程探讨人工智能/机器学习的关键方面,以及它们在增强光学和光子学技术能力方面的显著作用。教程从基本定义和范例开始,以支持向量机和随机森林为例,介绍了经典的机器学习算法。对深度学习的广泛讨论包括反向传播算法和人工神经网络,并举例说明密集神经网络和卷积神经网络的应用。接下来研究了数据增强和迁移学习,它们是处理数据集有限情况的有效策略。最后,讨论了减轻数据收集和标注负担的必要性,从而激发了对无监督和半监督学习策略的研究以及对强化学习的利用。通过对人工智能/ML 技术的结构化探索,本教程为研究人员提供了在光学和光子学的广阔领域开始利用人工智能变革潜力的基本工具。
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Artificial intelligence and machine learning in optics: tutorial
Across the spectrum of scientific inquiry and practical applications, the emergence of artificial intelligence (AI) and machine learning (ML) has comprehensively revolutionized problem-solving methodologies. This tutorial explores key aspects of AI/ML and their remarkable role in augmenting the capabilities of optics and photonics technologies. Beginning with fundamental definitions and paradigms, the tutorial progresses to classical machine learning algorithms, with examples employing support vector machines and random forests. Extensive discussion of deep learning encompasses the backpropagation algorithm and artificial neural networks, with examples demonstrating the applications of dense and convolutional neural networks. Data augmentation and transfer learning are examined next as effective strategies for handling scenarios with limited datasets. Finally, the necessity of alleviating the burden of data collection and labeling is discussed, motivating the investigation of unsupervised and semi-supervised learning strategies as well as the utilization of reinforcement learning. By providing a structured exploration of AI/ML techniques, this tutorial equips researchers with the essential tools to begin leveraging AI’s transformative potential within the expansive realm of optics and photonics.
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