3 citations · 7 across the 11 of their papers we have counts for
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws
Aviral Prakash, Marc L. Klasky
Reduced-order models, such as latent dynamics models, are becoming mainstream for accelerating simulations for parameterized physical systems governed by nonlinear conservation law…
Causal Multi-fidelity Surrogate Forward and Inverse Models for ICF Implosions
Tyler E. Maltba, Ben S. Southworth, Jeffrey R. Haack +1
Continued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input parameters, followed by design opti…
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…
ECLEIRS: Exact conservation law embedded identification of reduced states for parameterized partial differential equations from sparse and noisy data
Aviral Prakash, Ben S. Southworth, Marc L. Klasky
Multi-query applications such as parameter estimation, uncertainty quantification and design optimization for parameterized PDE systems are expensive due to the high computational…
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…
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…