59 citations · 157 across the 16 of their papers we have counts for
6 papers · 1 filter
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…