A5047579177- Nuclear Physics and Applications
- Nuclear reactor physics and engineering
- Graphite, nuclear technology, radiation studies
- Magnetic confinement fusion research
- Nuclear Materials and Properties
- Medical Imaging Techniques and Applications
- Nuclear and radioactivity studies
- Seismic Imaging and Inversion Techniques
- Particle accelerators and beam dynamics
- Geophysical Methods and Applications
- Particle Detector Development and Performance
- Lightning and Electromagnetic Phenomena
- Radioactive contamination and transfer
- Laser-Plasma Interactions and Diagnostics
- Heat transfer and supercritical fluids
- Rocket and propulsion systems research
- Radiation Detection and Scintillator Technologies
- Advanced X-ray and CT Imaging
- Pulsars and Gravitational Waves Research
- Transportation Safety and Impact Analysis
- Generative Adversarial Networks and Image Synthesis
- Combustion and Detonation Processes
- Advanced Radiotherapy Techniques
- Machine Learning and ELM
- Particle Accelerators and Free-Electron Lasers
Los Alamos National Laboratory
2012-2023
University of Nevada, Las Vegas
2009
Pancreatic Cancer UK
1991
Lawrence Livermore National Laboratory
1973-1981
MCNP6 is simply and accurately described as the merger of MCNP5 MCNPX capabilities, but it much more than sum those two computer codes. result five years effort by code development teams. These groups people, residing in Los Alamos National Laboratory's (LANL) X Computational Physics Division, Monte Carlo Codes Group (XCP-3), Decision Applications Radiation Transport Team (D-5), respectively, have combined their efforts to produce next evolution MCNP. While maintenance bug fixes will...
While radiography is routinely used to probe complex, evolving density fields in research areas ranging from materials science shock physics inertial confinement fusion and other national security applications, complications resulting noise, scatter, complex beam dynamics, etc. prevent current methods of reconstructing being accurate enough identify the underlying with sufficient confidence. In this work, we show that using only features are robustly identifiable radiographs combining them...
MCNP6 is simply and accurately described as the merger of MCNP5 MCNPX capabilities, but it much more than sum these two computer codes. result six years effort by code development teams. These groups people, residing in Los Alamos National Laboratory’s X Computational Physics Division, Monte Carlo Codes Group (XCP-3) Nuclear Engineering Nonproliferation Radiation Transport Modeling Team (NEN-5) respectively, have combined their efforts to produce next evolution MCNP. While maintenance major...
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction methods may not provide the high accuracy needed applications can break down presence unmodeled or anomalous other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate reconstruction, particularly dynamic imaging, where time evolution fields be captured by partial differential equations...
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation state (EOS), opacities, initial conditions. Typically, however, these are not directly observable. What observed instead a time sequence radiographic projections using X-rays. this work, we define set sparse hydrodynamic features derived...
A trained attention-based transformer network can robustly recover the complex topologies given by Richtmyer-Meshkoff instability from a sequence of hydrodynamic features derived radiographic images corrupted with blur, scatter, and noise. This approach is demonstrated on ICF-like double shell simulations. The key component this encoder that acts extracted noisy radiographs. includes numerous self-attention layers act to learn temporal dependencies in input sequences increase expressiveness...
Density reconstruction from X-ray projections is an important problem in radiography with key applications scientific and industrial computed tomography (CT). Often, such are corrupted by unknown sources of noise scatter, which when not properly accounted for, can lead to significant errors density reconstruction. In the setting this problem, recent deep learning-based methods have shown promise improving accuracy article, we propose a encoder-decoder framework wherein encoder extracts...
We develop an ML-based approach for density reconstruction based on transformer neural networks. This is demonstrated in the setting of ICF-like double shell hydrodynamic simulations wherein parameters related to material properties and initial conditions are varied. The new method can robustly recover complex topologies given by Richtmyer-Meshkoff instability (RMI) from a sequence features derived radiographic images corrupted with blur, scatter, noise. A noise model developed characterize...