About this Research Topic
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 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
The goal of this Research Topic is to gather both theoretical and experimental research which employs machine learning algorithms for the design of advanced optical devices. As a primary aspects, submitted contributions should target to expand the theoretical knowledge by using physics-driven machine learning algorithms providing deeper insights and innovative solutions to light-matter interactions at fundamental physics level, and 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 the optical devices and further enhance its applications.
Topics of interest include, but are not limited to, machine learning models and techniques applied to :
- Modelling and design 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
Merging machine learning techniques with the principles of quantum physics has sparked a revolutionary transformation in the design and optimization of novel 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
The goal of this Research Topic is to gather both theoretical and experimental research which employs machine learning algorithms for the design of advanced optical devices. As a primary aspects, submitted contributions should target to expand the theoretical knowledge by using physics-driven machine learning algorithms providing deeper insights and innovative solutions to light-matter interactions at fundamental physics level, and 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 the optical devices and further enhance its applications.
Topics of interest include, but are not limited to, machine learning models and techniques applied to :
- Modelling and design 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
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.
