3 citations · 7 across the 11 of their papers we have counts for
5 papers · 1 filter
Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions
Daniel A. Serino, Evan Bell, Marc Klasky +4
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of th…
Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction
Xiaojian Xu, Marc Klasky, Michael T. McCann +2
Reconstructing 3D cone beam computed tomography (CBCT) images from a limited set of projections is an important inverse problem in many imaging applications from medicine to inerti…
Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
Daniel A. Serino, Marc L. Klasky, Balasubramanya T. Nadiga +2
A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features deriv…
Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography
Siddhant Gautam, Marc L. Klasky, Balasubramanya T. Nadiga +3
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…
Neural Network Representations of Multiphase Equations of State
George A. Kevrekidis, Daniel A. Serino, Alexander Kaltenborn +3
Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Cli…