From the 1 of 10 linked papers with an AI index.
10 papers
GeoQ: Geometry-Aware Conditional Quantile Error Estimation for Scientific Surrogate Models
Khoa Nguyen, Daniel Serino, Aviral Prakash +1
Neural-network surrogate models are increasingly used to accelerate scientific simulations, but their deployment in extrapolative and autoregressive settings requires input-depende…
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…
Material Identification using Multi-Modal Intrinsic Radiation and Radiography
Khoa Nguyen, Brendt Wohlberg, Oleg Korobkin +1
We investigate multi-modal material identification for special nuclear material (SNM) configurations using a combination of X-ray radiography, high-resolution γ-ray spectroscopy,…
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…
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…