59 citations · 157 across the 16 of their papers we have counts for
16 papers
Building Population-Informed Priors for Bayesian Inference Using Data-Consistent Stochastic Inversion
Rebekah D. White, John D. Jakeman, Tim Wildey +1
Bayesian inference provides a powerful tool for leveraging observational data to inform model predictions and uncertainties. However, when such data is limited, Bayesian inference…
A Distributions-based Approach for Data-Consistent Inversion
Kirana Bergstrom, Troy Butler, Tim Wildey
We formulate a novel approach to solve a class of stochastic problems, referred to as data-consistent inverse (DCI) problems, which involve the characterization of a probability me…
Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation methods
Anh Tran, Tim Wildey, Hojun Lim
Uncertainty quantification (UQ) plays a major role in verification and validation of computational engineering models and simulations, and establishes trust in the predictive capab…
Solving stochastic inverse problems for property-structure linkages using data-consistent inversion and machine learning
Anh Tran, Tim Wildey
Determining process-structure-property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process…
Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys
Anh Tran, Julien Tranchida, Tim Wildey +1
We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predict…
An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics
Anh Tran, John A. Mitchell, Laura P. Swiler +1
Determining a process-structure-property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in t…
A stochastic reduced-order model for statistical microstructure descriptors evolution
Anh Tran, Jing Sun, Dehao Liu +2
Integrated Computational Materials Engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and signif…
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture
Anh Tran, Mike Eldred, Tim Wildey +3
High-fidelity complex engineering simulations are highly predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of compu…
Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification: Extensions to
Troy Butler, Tim Wildey, Wenjuan Zhang
A previous study analyzed the convergence of probability densities for forward and inverse problems when a sequence of approximate maps between model inputs and outputs converges i…
Unified geometric multigrid algorithm for hybridized high-order finite element methods
Tim Wildey, Sriramkrishnan Muralikrishnan, Tan Bui-Thanh
We consider a standard elliptic partial differential equation and propose a geometric multigrid algorithm based on Dirichlet-to-Neumann (DtN) maps for hybridized high-order finite…
Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification
T. Butler, J. D. Jakeman, T. Wildey
We analyze the convergence of probability density functions utilizing approximate models for both forward and inverse problems. We consider the standard forward uncertainty quantif…
Optimal Experimental Design Using A Consistent Bayesian Approach
Scott N. Walsh, Tim M. Wildey, John D. Jakeman
We consider the utilization of a computational model to guide the optimal acquisition of experimental data to inform the stochastic description of model input parameters. Our formu…
A Consistent Bayesian Formulation for Stochastic Inverse Problems Based on Push-forward Measures
T. Butler, J. D. Jakeman, T. Wildey
We formulate, and present a numerical method for solving, an inverse problem for inferring parameters of a deterministic model from stochastic observational data (quantities of int…
Solving Stochastic Inverse Problems using Sigma-Algebras on Contour Maps
Troy Butler, Don Estep, Simon Tavener +3
We compute approximate solutions to inverse problems for determining parameters in differential equation models with stochastic data on output quantities. The formulation of the pr…
Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates
John D. Jakeman, Timothy Wildey
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-bas…
Efficient uncertainty propagation for network multiphysics systems
Paul G. Constantine, Eric T. Phipps, Timothy M. Wildey
We consider a multiphysics system with multiple component models coupled together through network coupling interfaces, i.e., a handful of scalars. If each component model contains…