85 citations · 255 across the 18 of their papers we have counts for
4 papers · 1 filter
Force Training Neural Network Potential Energy Surface Models
Christian Devereux, Yoona Yang, Carles Martí +3
Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or…
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty
Joy N. Mueller, Khachik Sargsyan, Craig J. Daniels +1
Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and…
Bayesian calibration with summary statistics for the prediction of xenon diffusion in UO2 nuclear fuel
Pieterjan Robbe, David Andersson, Luc Bonnet +5
The evolution and release of fission gas impacts the performance of UO2 nuclear fuel. We have created a Bayesian framework to calibrate a novel model for fission gas transport that…
Global Sensitivity Analysis of a coupled multiphysics model to predict surface evolution in fusion plasma-surface interactions
Pieterjan Robbe, Sophie Blondel, Tiernan Casey +4
We construct a global sensitivity analysis framework for a coupled multiphysics model used to predict the changes in material properties and surface morphology of helium plasma-fac…