From the 1 of 10 linked papers with an AI index.
5 papers · 1 filter
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws
Aviral Prakash, Marc L. Klasky
The paper introduces a variational latent neural field framework that provides both uncertainty estimates and exact preservation of conservation laws for reduced-order models of no…
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…
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…
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…