Affiliation:
1. Los Alamos National Laboratory , Los Alamos, New Mexico 87545, USA
Abstract
Deep machine learning is used to analyze a proton radiograph from a tin pulsed power experiment and determine density values for each pixel in the image. Two promising convolutional neural network architectures that have proven to be effective for image analysis in other applications are applied to analyze a proton radiograph and find density values. The process of creating a suitable training dataset is described, involving the Lagrangian hydrodynamic model used for simulations of the experiment, the proton radiography forward model to make synthetic images for training, and the manner in which data augmentation is used to expand the resulting image dataset. It is shown that machine learning not only produces a reasonable density field but is also able to predict features in the density field that are suggested by the proton radiograph but not captured by simulations.
Funder
U.S. Department of Energy
Subject
General Physics and Astronomy
Reference29 articles.
1. Improving face recognition with domain adaptation;Neurocomputing,2018
2. Deep learning algorithm predicts diabetic retinopathy progression in individual patients;npj Digital Med.,2019
3. Fully convolutional networks for semantic segmentation,2015
4. A machine learning enabled image-data-driven end-to-end mechanical field predictor for dual-phase steel;Proc. Appl. Math. Mech.,2023
5. Surrogate modeling of stress fields in periodic polycrystalline microstructures using U-Net and Fourier neural operators