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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…
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…
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…