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Plasma Reconstruction Summary

Code used for predicting continuous high-density laser ray-tracing results using deep learning techniques. To be used in VR modules for Clean energy research and STEM outreach.

Collaborators:

Isaac Whittaker, Jacob Braswell (@jocobtt), Tyki Wada (@tykiww)

Data Preparation

  • Full Factorial Design including noise (Cartesian Product with Gaussian blur in SAS/python)
  • Latin Hypercube Design (pyDOE)
  • Image Storage in hdf5 as numpy arrays (24000 images, 448MB)
  • Labels as X input in CSV

Computation

  • BYU Supercomputer access

    • Pyspark executed on Slurm batches
  • Google Colab

    • Python3 on tensorflow GPU (TPU)

Models

  • Deep Convolutional Generator (DCG)
  • Regular Keras sequential model (forward fully-connected model)

(Proprietary information (ie. packages, models) excluded)

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LLNL Plasma Deep Learning Comparison

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