Intelligent metaphotonics empowered by machine learning

@article{Krasikov2022IntelligentME,
  title={Intelligent metaphotonics empowered by machine learning},
  author={Sergey Krasikov and Aaron D. Tranter and Andrey Bogdanov and Yuri S. Kivshar},
  journal={Opto-Electronic Advances},
  year={2022}
}
In the recent years, a dramatic boost of the research is observed at the junction of photonics, machine learning and artificial intelligence . A new methodology can be applied to the description of a variety of photonic systems including optical waveguides, nanoantennas, and metasurfaces. These novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices. Artificial intelligence and machine learning penetrate… 

Figures from this paper

Experimental realization of the active convolved illumination imaging technique for enhanced signal-to-noise ratio
Wyatt Adams, Anindya Ghoshroy, and Durdu Ö. Güney3∗ Ansys, Inc., 2600 Ansys Dr, Canonsburg, PA 15317, USA Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering
Vector optical field manipulation via structural functional materials: Tutorial
Vector optical field (VOF) manipulation greatly extended the boundaries of traditional scalar optics over the past decades. Meanwhile, the newly emerging techniques enabled by structural functional

References

SHOWING 1-10 OF 259 REFERENCES
Approaching the adiabatic timescale with machine learning
TLDR
This work experimentally demonstrates a general approach using a machine-learning algorithm that develops a model of the system, based on previous performance, to create further educated guesses on how to improve, and reaches a speed faster than that of previous approaches, dealing well with the complex dynamics and experimental imperfections present with its empirical approach.
Multiparameter optimisation of a magneto-optical trap using deep learning
TLDR
A deep artificial neural network is implemented to optimise the magneto-optic cooling and trapping of neutral atomic ensembles, showing an improvement in the resulting resonant optical depth compared to more traditional solutions.
Explainable Machine Learning for Scientific Insights and Discoveries
TLDR
This article provides a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas and discusses three core elements that were identified as relevant in this context: transparency, interpretability, and explainability.
Machine Prediction of Topological Transitions in Photonic Crystals
TLDR
It is found that the trained network yields remarkably accurate predictions of the topological phases for 1D photonic crystals, even for the geometric and material parameters that are outside of the range of the trained dataset.
Fast machine-learning online optimization of ultra-cold-atom experiments
TLDR
It is demonstrated that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique.
A high-bias, low-variance introduction to Machine Learning for physicists
Inverse design of photonic topological state via machine learning
TLDR
This work focuses on Zak phases, which are the topological properties of one-dimensional photonics crystals, and proposes an approach to achieve the design of optical structures with the target topological states by exploiting machine learning technologies.
Machine learning at the energy and intensity frontiers of particle physics
TLDR
The application and development of machine-learning methods used in experiments at the frontiers of particle physics (such as the Large Hadron Collider) are reviewed, including recent advances based on deep learning.
Optimisation of colour generation from dielectric nanostructures using reinforcement learning.
TLDR
The colour generation by dielectric nanostructures is investigated and it is shown that this model can find geometrical properties that can generate much purer red, green and blue colours compared to previously reported results.
Interpretable inverse design of particle spectral emissivity using machine learning
TLDR
This work demonstrates the possibility for approachable and interpretable machine learning models to be used for rapid forward and inverse design of devices that span a broad and diverse parameter space.
...
1
2
3
4
5
...