Bulletin of the American Physical Society
64th Annual Meeting of the APS Division of Plasma Physics
Volume 67, Number 15
Monday–Friday, October 17–21, 2022; Spokane, Washington
Session JP11: Poster Session IV: In-Person, Hall A (2:00-3:30pm) and Virtual Poster Presentations (3:45-5:00pm)
BEAMS: Laser Accelerators
Education, Public Engagement, and DEI
High School and Undergraduate Research
2:00 PM - 5:00 PM
Tuesday, October 18, 2022
Room: Exhibit Hall A and Online
Abstract: JP11.00068 : Machine Learing-Boosted Diagnostics Using Autoencoders*
Presenter:
Noah A Borthwick
(University of Illinois at Urbana-Champaign)
Authors:
Noah A Borthwick
(University of Illinois at Urbana-Champaign)
Noah R Mandell
(MIT)
Ammar Hakim
(Princeton Plasma Physics Laboratory)
Ralph Kube
(Princeton Plasma Physics Laboratory)
This work aims to explore how autoencoders, a type of generative machine learning model, can be used to map synthetic diagnostics signals to underlying simulation data. Autoencoders map their inputs to a low-dimensional latent space. They then reconstruct the original data from the latent space representation. During training, the latent space adapts to represent the most important properties of the training data set. Our autoencoder is trained on reconstructing pairs of simulation and synthetic diagnostic data. The simulation data was produced using the Gkeyll code and a simplified gas-puff imaging diagnostic is used as the forward model.
We explore how the dimensions of the latent space relate to various features of the training data set. Additionally, we explore to what degree it is possible to reconstruct both simulation data and synthetic diagnostic data when parts of the input are missing.
*This work was made possible by funding from the Department of Energy for the Summer Undergraduate Laboratory Internship (SULI) programĀ
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