High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features

TL;DR

This paper introduces a novel machine learning approach using cGANs and hydrodynamic features to achieve high-precision density reconstructions from dynamic radiography, outperforming traditional methods especially with noise and scatter.

Contribution

The work develops a new method combining hydrodynamic features with cGANs for improved density reconstruction from radiographs, incorporating hydrodynamic constraints and parameter estimation.

Findings

Outperforms traditional radiographic reconstruction methods.

Effectively captures hydrodynamic paths with minimal scatter.

Enhances robustness and accuracy of density estimates.

Abstract

Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics. This technique has been used in numerous fields including materials science, shock physics, inertial confinement fusion, and other national security applications. In many of these applications, however, complications resulting from noise, scatter, complex beam dynamics, etc. prevent the reconstruction of density from being accurate enough to identify the underlying physics with sufficient confidence. As such, density reconstruction from static/dynamic radiography has typically been limited to identifying discontinuous features such as cracks and voids in a number of these applications. In this work, we propose a fundamentally new approach to reconstructing density from a temporal sequence of radiographic images. Using only the robust features identifiable in radiographs, we combine them with the underlying hydrodynamic equations of motion using a machine learning approach, namely, conditional generative adversarial networks (cGAN), to determine the density fields from a dynamic sequence of radiographs. Next, we seek to further enhance the hydrodynamic consistency of the ML-based density reconstruction through a process of parameter estimation and projection onto a hydrodynamic manifold. In this context, we note that the distance from the hydrodynamic manifold given by the training data to the test data in the parameter space considered both serves as a diagnostic of the robustness of the predictions and serves to augment the training database, with the expectation that the latter will further reduce future density reconstruction errors. Finally, we demonstrate the ability of this method to outperform a traditional radiographic reconstruction in capturing allowable hydrodynamic paths even when relatively small amounts of scatter are present.