1 citations · 2 across the 5 of their papers we have counts for
5 papers
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators
Yiming Fan, Habib Najm, Yue Yu +2
Nonlocal, integral operators have become an efficient surrogate for bottom-up homogenization, due to their ability to represent long-range dependence and multiscale effects. Howeve…
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…
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…
Bayesian calibration of interatomic potentials for binary alloys
Arun Hegde, Elan Weiss, Wolfgang Windl +2
Developing reliable interatomic potential models with quantified predictive accuracy is crucial for atomistic simulations. Commonly used potentials, such as those constructed throu…