Machine learning and applications in ultrafast photonics

G Genty, L Salmela, JM Dudley, D Brunner… - Nature …, 2021 - nature.com
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 …

Deep learning for the design of photonic structures

W Ma, Z Liu, ZA Kudyshev, A Boltasseva, W Cai… - Nature Photonics, 2021 - nature.com
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 …

Controlling light propagation in multimode fibers for imaging, spectroscopy, and beyond

H Cao, T Čižmár, S Turtaev, T Tyc… - Advances in Optics and …, 2023 - opg.optica.org
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 …

Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi‐supervised learning strategy

W Ma, F Cheng, Y Xu, Q Wen, Y Liu - Advanced Materials, 2019 - Wiley Online Library
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 …

Fiber laser development enabled by machine learning: review and prospect

M Jiang, H Wu, Y An, T Hou, Q Chang, L Huang, J Li… - PhotoniX, 2022 - Springer
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 …

PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning

Y Rivenson, T Liu, Z Wei, Y Zhang, K de Haan… - Light: Science & …, 2019 - nature.com
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 …

Tackling photonic inverse design with machine learning

Z Liu, D Zhu, L Raju, W Cai - Advanced Science, 2021 - Wiley Online Library
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 …

Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network

L Salmela, N Tsipinakis, A Foi, C Billet… - Nature machine …, 2021 - nature.com
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 …

A framework for biosensors assisted by multiphoton effects and machine learning

JA Arano-Martinez, CL Martínez-González, MI Salazar… - Biosensors, 2022 - mdpi.com
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 …

Artificial neural networks for photonic applications—from algorithms to implementation: tutorial

P Freire, E Manuylovich, JE Prilepsky… - Advances in Optics and …, 2023 - opg.optica.org
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 …