About this Research Topic

Background

The integration of machine learning with photonic design represents a cutting-edge intersection of technology and fundamental physics. In recent decades, the rapid development of machine learning algorithms has revolutionized many areas of technology and research, enabling the tackling of complex problems in novel ways. Simultaneously, the latest achievements in the design and technology of advanced optical devices have opened new horizons in many different application fields, such as data communications, computing, imaging, and (bio)sensing. Merging machine learning techniques with the principles of quantum physics has sparked a revolutionary transformation in the design and optimization of novel optical materials and devices, including metamaterials and metadevices. Physics-driven machine learning techniques are opening new avenues in the investigation of light propagation in artificial materials and meta-structures, unlocking a deeper understanding of optical phenomena. In addition, they are triggering pioneering breakthroughs in the synthesis of new materials and in the design of photonic devices with unprecedented functionalities. Despite these advancements, there remains a significant gap in fully understanding and exploiting the potential of machine learning in photonic design, necessitating further research and exploration.

This Research Topic aims to gather both theoretical and experimental research that employs machine learning algorithms for the design of advanced optical devices. As primary aspects, submitted contributions should target expanding theoretical knowledge by using physics-driven machine learning algorithms to provide deeper insights and innovative solutions to light-matter interactions at the fundamental physics level. Additionally, the goal is to merge artificial intelligence with experimental approaches for the design of radically novel device concepts. This Research Topic aims to serve as a platform for experts in the field, leveraging state-of-the-art machine learning algorithms to provide valuable insights into the realm of optical devices and further enhance their applications.

To gather further insights into the integration of machine learning with photonic design, we welcome articles addressing, but not limited to, the following themes:

• Modelling and design of photonic devices and circuits.
• Light propagation and nonlinear interactions in complex materials, metastructures, and metadevices.
• Experimental data analysis, classification, and performance assessment of photonic devices.
• Photonic devices for optical communication, sensing, imaging, and biophotonics.
• Photonics for quantum communications and computing.
• Optical implementations of machine learning models and algorithms.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Machine Learning, Deep Neural Networks, Photonics, Optical Devices, Quantum Photonics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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