85 citations · 255 across the 18 of their papers we have counts for
19 papers
Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures
Christophe Bonneville, Nathan Bieberdorf, Pieterjan Robbe +4
Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a condition…
Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone
Christophe Bonneville, Nathan Bieberdorf, Arun Hegde +4
Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For such liquid-metal dealloying (LMD) process, phase field models have been develop…
Bayesian optimization for stable properties amid processing fluctuations in sputter deposition
Ankit Shrivastava, Matias Kalaswad, Joyce O. Custer +2
We introduce a Bayesian optimization approach to guide the sputter deposition of molybdenum thin films, aiming to achieve desired residual stress and sheet resistance while minimiz…
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…
Bayesian Nonlocal Operator Regression (BNOR): A Data-Driven Learning Framework of Nonlocal Models with Uncertainty Quantification
Yiming Fan, Marta D'Elia, Yue Yu +2
We consider the problem of modeling heterogeneous materials where micro-scale dynamics and interactions affect global behavior. In the presence of heterogeneities in material micro…
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…
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
Scalable approximation of Green's function for estimation of anharmonic energy corrections
Prashant Rai, Khachik Sargsyan, Habib Najm +1
A method based on separated integration to estimate anharmonic corrections to energy and vibration of molecules in a second-order diagrammatic vibrational many-body Green's functio…
Sparse Low Rank Approximation of Potential Energy Surfaces with Applications in Estimation of Anharmonic Zero Point Energies and Frequencies
Prashant Rai, Khachik Sargsyan, Habib Najm +1
We propose a method that exploits sparse representation of potential energy surfaces (PES) on a polynomial basis set selected by compressed sensing. The method is useful for studie…
Entropy-based closure for probabilistic learning on manifolds
C. Soizea, R. Ghanem, C. Safta +7
In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically cons…
Embedded Model Error Representation for Bayesian Model Calibration
Khachik Sargsyan, Xun Huan, Habib N. Najm
Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, al…
Compressive sensing adaptation for polynomial chaos expansions
Panagiotis Tsilifis, Xun Huan, Cosmin Safta +5
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adapta…
Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations
Xun Huan, Cosmin Safta, Khachik Sargsyan +6
The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simula…
Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions
Xun Huan, Cosmin Safta, Khachik Sargsyan +4
Compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quantification analysis of…
Efficient Representation of Uncertainty for Stochastic Economic Dispatch
Cosmin Safta, Richard L. -Y. Chen, Habib N. Najm +2
Stochastic economic dispatch models address uncertainties in forecasts of renewable generation output by considering a finite number of realizations drawn from a stochastic process…
Toward Using Surrogates to Accelerate Solution of Stochastic Electricity Grid Operations Problems
Cosmin Safta, Richard L. Chen, Habib N. Najm +2
Stochastic unit commitment models typically handle uncertainties in forecast demand by considering a finite number of realizations from a stochastic process model for loads. Accura…