Beyond optimization—supervised learning applications in relativistic laser-plasma experiments-Reference-Cited by-同舟云学术

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Beyond optimization—supervised learning applications in relativistic laser-plasma experiments

Published:2021-08 Issue:8 Volume:28 Page:083102
ISSN:1070-664X
Container-title:Physics of Plasmas
language:en
Short-container-title:Physics of Plasmas

Author:

Lin Jinpu¹^ORCID,Qian Qian¹,Murphy Jon¹,Hsu Abigail²^ORCID,Hero Alfred³^ORCID,Ma Yong¹^ORCID,Thomas Alexander G. R.¹^ORCID,Krushelnick Karl¹^ORCID

Affiliation:

1. Gerard Mourou Center for Ultrafast Optical Science, University of Michigan, Ann Arbor, Michigan 48109, USA

2. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA

3. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, USA

Funder

Air Force Office of Scientific Research

U.S. Department of Energy

Publisher

AIP Publishing

Subject

Condensed Matter Physics

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0047940

Reference68 articles.

1. Coherent control of plasma dynamics

2. Laser wakefield acceleration with active feedback at 5 Hz

3. Adaptive control of laser-wakefield accelerators driven by mid-IR laser pulses

4. Optimizing laser–plasma interactions for ion acceleration using particle-in-cell simulations and evolutionary algorithms

5. Controlling laser driven protons acceleration using a deformable mirror at a high repetition rate

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Neural network-based control of an ultrafast laser;Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment;2023-08

2. 2022 Review of Data-Driven Plasma Science;IEEE Transactions on Plasma Science;2023-07

3. Laser Wakefield Accelerator modelling with Variational Neural Networks;High Power Laser Science and Engineering;2023-01-06

4. Applications of object detection networks in high-power laser systems and experiments;High Power Laser Science and Engineering;2023

5. Data-driven science and machine learning methods in laser–plasma physics;High Power Laser Science and Engineering;2023

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