4 papers
Revealing Low-Dimensional Structure in 2D Richtmyer-Meshkov Instabilities via Parametric Reduced-Order Modeling
Daniel Messenger, Daniel Serino, Balu Nadiga +1
Efficient modeling of the Richtmyer-Meshkov instability (RMI) is essential to many engineering tasks, including high-speed combustion and drive and capsule geometry optimization in…
Learning robust parameter inference and density reconstruction in flyer plate impact experiments
Evan Bell, Daniel A. Serino, Ben S. Southworth +2
Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, espec…
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…
Supervised Reconstruction for Silhouette Tomography
Evan Bell, Michael T. McCann, Marc Klasky
In this paper, we introduce silhouette tomography, a novel formulation of X-ray computed tomography that relies only on the geometry of the imaging system. We formulate silhouette…