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Frontiers in Data Analytics and Monitoring Tools for Extreme Materials Proceedings of a Workshop (2023) / Chapter Skim
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Pages 63-64

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From page 63...
... A clear lesson from the work, he said, is "as you include more and more physics in the model, you're going to get much better results." Agar's last subject was the use of machine learning tools to perform real-time data reduction as a way of dealing with the vast amounts of data being generated by model physics experiments. As an example of why data reduction is needed, he pointed to the Large Hadron Collider, which collects data at a rate that is on the order of petabytes per second.
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From page 64...
... In the discussion period following the presentation, Agar responded to a question about how easy it is to integrate the electron microscope with the data reduction tool. That is perhaps the biggest pain point, he said, because instruments generally have proprietary data acquisition systems, and it can be difficult to tap into the data streams.
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Key Terms

  • machine learning
  • discussion period
  • data reduction
  • materials scientists
  • period following

  • data acquisition
  • pulse laser
  • piezoresponse force
  • tremendous amounts
  • hadron collider

  • plume dynamics
  • neural network
  • learning approach
  • electron microscope
  • discover data

  • proprietary data
  • fit data
  • modeling data
  • produce data
  • collects data

  • raw data


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