Machine learning and applications in ultrafast photonics
By Goëry Genty, L. Salmela, John M. Dudley, Daniel Brunner, A. Kokhanovskiy, S. Kobtsev, Sergei K. Turitsyn. Nature Photonics, 2020
341 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/e8aa24d9c64f1215a97e8118905fad0189a53b97
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yijie Shen
- K. Bliokh
- A. Safavi-Naeini
- Shanhui Fan
- Demetrios N. Christodoulides
- J. Chiaverini
- Carsten Schuck
- Eleni Diamanti
In Atomic and Molecular Physics, and Optics
- Yijie Shen
- Shanhui Fan
- Cheng Guo
- Kartik Srinivasan
- Frank Wise
- K. Bliokh
- Logan G. Wright
- A. Safavi-Naeini
In Electrical and Electronic Engineering
- Cheng Guo
- John E. Bowers
- A. Safavi-Naeini
- K. Bliokh
- Logan G. Wright
- Shanhui Fan
- Demetrios N. Christodoulides
- Frank Wise
Citations in the corpus
- 2022 Roadmap on integrated quantum photonics (2021, background): “An ever-increasing range of photonic devices and systems have now be successfully modelled using machine learning, ranging from integrated photonic devices [268] to ultrafast photonic systems [270] and many more.”
- Roadmap on spatiotemporal light fields (2022, background): “control techniques, such as deep learning [17], inverse design [18], and reinforcement learning.”