Determining HEDP Foams' Quality with Multi-View Deep Learning Classification

Nadav Schneider, Matan Rusanovsky, Raz Gvishi, Gal Oren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

High energy density physics (HEDP) experiments commonly involve a dynamic wave-front propagating inside a lowdensity foam. This effect affects its density and hence, its transparency. A common problem in foam production is the creation of defective foams. Accurate information on their dimension and homogeneity is required to classify the foams' quality. Therefore, those parameters are being characterized using a 3D-measuring laser confocal microscope. For each foam, five images are taken: two 2D images representing the top and bottom surface foam planes and three images of side cross-sections from 3D scannings. An expert has to do the complicated, harsh, and exhausting work of manually classifying the foam's quality through the image set and only then determine whether the foam can be used in experiments or not. Currently, quality has two binary levels of normal vs. defective. At the same time, experts are commonly required to classify a sub-class of normal-defective, i.e., defective foams but might be sufficient for the needed experiment. This sub-class is problematic due to inconclusive judgment that is primarily intuitive. In this work, we present a novel state-of-the-art multi-view deep learning classification model that mimics the physicist's perspective by automatically determining the foams' quality classification and thus aids the expert. Our model achieved 86% accuracy on upper and lower surface foam planes and 82% on the entire set, suggesting interesting heuristics to the problem. A significant added value in this work is the ability to regress the foam quality instead of binary deduction and even explain the decision visually. The source code used in this work, as well as other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCNIMulti-View-Foams.git.

Original languageEnglish
Title of host publicationProceedings of AI4S 2022
Subtitle of host publicationArtificial Intelligence and Machine Learning for Scientific Applications, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-25
Number of pages7
ISBN (Electronic)9781665462075
DOIs
StatePublished - 2022
Externally publishedYes
Event3rd IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, AI4S 2022 - Dallas, United States
Duration: 13 Nov 202218 Nov 2022

Publication series

NameProceedings of AI4S 2022: Artificial Intelligence and Machine Learning for Scientific Applications, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis

Conference

Conference3rd IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, AI4S 2022
Country/TerritoryUnited States
CityDallas
Period13/11/2218/11/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Aerogel
  • Deep Learning
  • HEDP
  • LIME
  • Low-Density Foams
  • Multi-View Classification

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