Reconstructing Richtmyer–Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
We develop an ML-based approach for density reconstruction based on transformer neural
networks. This approach is demonstrated in the setting of ICF-like double shell …
networks. This approach is demonstrated in the setting of ICF-like double shell …
Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data
Reconstruction of CT images from a limited set of projections through an object is important
in several applications ranging from medical imaging to industrial settings. As the number of …
in several applications ranging from medical imaging to industrial settings. As the number of …
Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography
Density reconstruction from X-ray projections is an important problem in radiography with
key applications in scientific and industrial X-ray computed tomography (CT). Often, such …
key applications in scientific and industrial X-ray computed tomography (CT). Often, such …
Learning physical unknowns from hydrodynamic shock and material interface features in ICF capsule implosions
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive
modeling is complicated by uncertainty in parameters that characterize various aspects of …
modeling is complicated by uncertainty in parameters that characterize various aspects of …
Physics-driven learning of Wasserstein GAN for density reconstruction in dynamic tomography
Object density reconstruction from projections containing scattered radiation and noise is of
critical importance in many applications. Existing scatter correction and density …
critical importance in many applications. Existing scatter correction and density …
Improving Radiography Machine Learning Workflows via Metadata Management for Training Data Selection
Most machine learning models require many iterations of hyper-parameter tuning, feature
engineering, and debugging to produce effective results. As machine learning models …
engineering, and debugging to produce effective results. As machine learning models …
Stochastic parameterization of column physics using generative adversarial networks
We demonstrate the use of a probabilistic machine-learning technique to develop stochastic
parameterizations of atmospheric column physics. After suitable preprocessing of NASA's …
parameterizations of atmospheric column physics. After suitable preprocessing of NASA's …
[HTML][HTML] Using deep machine learning to interpret proton radiography data from a pulsed power experiment
VP Chiravalle - AIP Advances, 2023 - pubs.aip.org
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 …
experiment and determine density values for each pixel in the image. Two promising …
An End-to-End Learning Approach for Subpixel Feature Extraction
An End-to-End Learning Approach for Subpixel Feature Extraction Page 1 An End-to-End
Learning Approach for Subpixel Feature Extraction **aojian Xu1, *, Jeffrey A. Fessler1, Marc …
Learning Approach for Subpixel Feature Extraction **aojian Xu1, *, Jeffrey A. Fessler1, Marc …
Scatter Removal in Dynamic X-Ray Tomography using Learned Robust Features
A challenging problem in industrial radiography is accurate density reconstructions from X-
ray projections corrupted by noise, scatter, etc. We propose a deep learning-based …
ray projections corrupted by noise, scatter, etc. We propose a deep learning-based …