Abstract

Following the results of [1], which demonstrates a novel method to translate 2-dimensional measurements of HeI line radiation on turbulent scale into local plasma fluctuations via an integrated deep learning framework, this manuscript investigates the results when applying two separate techniques for optimization: Adam and L-BFGS. Fundamentally, the two approaches apply the same set of constraints and loss functions that combine neutral transport physics and collisional radiative theory for the 33D − 23P (587.6 nm line) transition in atomic helium whilst training the networks. The impact of these first- and second-order optimization techniques are investigated to examine their influence on numerical convergence and stability when seeking to analyze turbulent dynamics via gas puff imaging in experimental plasmas.

Details

Title
Impacts of first- and second-order optimization in deep learning of turbulent fluctuations from gas puff imaging
Author
Mathews, Abhilash 1 

 Plasma Science and Fusion Center, Massachusetts Institute of Technology , Cambridge, MA 02139 , USA; Present address: Swiss Plasma Center, École Polytechnique Fédérale de Lausanne , Lausanne, VD 1015 , CHE 
First page
012001
Publication year
2022
Publication date
Dec 2022
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2753732207
Copyright
Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.