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Wilcox, Trevor

Publications and source records attributed to Wilcox, Trevor.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

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 hydrodynamic simulations wherein the parameters related to material properties and initial conditions are varied. The new method can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability (RMI) from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. A noise model is developed to characterize errors in extracting features from synthetic radiographs of the simulated density field. The key component of the network is a transformer encoder that acts on a sequence of features extracted from noisy radiographs. This encoder includes numerous self-attention layers that act to learn temporal dependencies in the input sequences and increase the expressiveness of the model. This approach is shown to exhibit an excellent ability to accurately recover the RMI growth rates, despite the gas-metal interface being greatly obscured by radiographic noise. Our approach can be applied in a broad array of fields involving shock physics and material science.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Pinwheel Experiment Tantalum Shot 1 Pre-Shot Report

The purpose of this experimental series is to validate the reactive burn model in the PAGOSA hydrodynamic code. It will also help validate the EOS and Strength models for the aluminum and tantalum cylinders. C4 high explosive (HE) is used to drive an aluminum cylinder into a tantalum sandwiched PBX‐9501. The experiment will provide time of arrival (TOA) data for the burn front in the HE through the metal cylinders using Fiber Bragg Gratings (FBG). Photon Doppler Velocimetry (PDV) will provide shock wave TOA and the velocity of the inward moving cylinder. The PAGOSA code will be used to predict TOA and cylinder wall velocity using PDV and tracers.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Pinwheel Experiment Confirmatory Shot 1 Pre-Shot Report

The purpose of this experimental series is to validate the reactive burn model in the PAGOSA hydrodynamic code. It will also help validate the EOS and Strength models for the aluminum and tantalum cylinders. C4 high explosive (HE) is used to drive an aluminum cylinder into an aluminum sandwiched PBX-9501. The follow-on experiment will drive the aluminum cylinder into a tantalum sandwiched PBX-9501. The experiment will provide time of arrival (TOA) data for the burn front in the HE through the metal cylinders using Fiber Bragg Gratings (FBG). Photon Doppler Velocimetry (PDV) will provide shock wave TOA and the velocity of the inward moving cylinder. The PAGOSA code will be used to predict TOA and cylinder wall velocity using PDV and tracers.

42 ENGINEERING↗
DOE OSTI2022

High-precision inversion of dynamic radiography using hydrodynamic features

While radiography is routinely used to probe complex, evolving density fields in research areas ranging from materials science to shock physics to inertial confinement fusion and other national security applications, complications resulting from noise, scatter, complex beam dynamics, etc. prevent current methods of reconstructing density from being accurate enough to identify the underlying physics with sufficient confidence. In this work, we show that using only features that are robustly identifiable in radiographs and combining them with the underlying hydrodynamic equations of motion using a machine learning approach of a conditional generative adversarial network (cGAN) provides a new and effective approach to determine density fields from a dynamic sequence of radiographs. In particular, we demonstrate the ability of this method to outperform a traditional, direct radiograph to density reconstruction in the presence of scatter, even when relatively small amounts of scatter are present. Our experiments on synthetic data show that the approach can produce high quality, robust reconstructions. We also show that the distance (in feature space) between a testing radiograph and the training set can serve as a diagnostic of the accuracy of the reconstruction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

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 reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate density reconstruction, particularly in dynamic imaging, where the time evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. Here, we use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time series) from simulations. A supervised loss is also included in the training, which leads to an improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during the network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗