Machine learning and applications in ultrafast photonics
Recent years have seen the rapid growth and development of the field of smart photonics,
where machine-learning algorithms are being matched to optical systems to add new …
where machine-learning algorithms are being matched to optical systems to add new …
Deep learning for the design of photonic structures
Innovative approaches and tools play an important role in shaping design, characterization
and optimization for the field of photonics. As a subset of machine learning that learns …
and optimization for the field of photonics. As a subset of machine learning that learns …
Controlling light propagation in multimode fibers for imaging, spectroscopy, and beyond
Light transport in a highly multimode fiber exhibits complex behavior in space, time,
frequency, and polarization, especially in the presence of mode coupling. The newly …
frequency, and polarization, especially in the presence of mode coupling. The newly …
Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi‐supervised learning strategy
The research of metamaterials has achieved enormous success in the manipulation of light
in a prescribed manner using delicately designed subwavelength structures, so‐called meta …
in a prescribed manner using delicately designed subwavelength structures, so‐called meta …
Fiber laser development enabled by machine learning: review and prospect
In recent years, machine learning, especially various deep neural networks, as an emerging
technique for data analysis and processing, has brought novel insights into the development …
technique for data analysis and processing, has brought novel insights into the development …
PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
Using a deep neural network, we demonstrate a digital staining technique, which we term
PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections …
PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections …
Tackling photonic inverse design with machine learning
Abstract 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 study of …
making based on complex data, has become one of the most effective tools in the study of …
Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network
The propagation of ultrashort pulses in optical fibre plays a central role in the development
of light sources and photonic technologies, with applications from fundamental studies of …
of light sources and photonic technologies, with applications from fundamental studies of …
A framework for biosensors assisted by multiphoton effects and machine learning
The ability to interpret information through automatic sensors is one of the most important
pillars of modern technology. In particular, the potential of biosensors has been used to …
pillars of modern technology. In particular, the potential of biosensors has been used to …
Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …
audience, ranging from optical research and engineering communities to computer science …