4 papers
Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers
Anh Tran
Bayesian optimization (BO) is a flexible and powerful framework that is suitable for computationally expensive simulation-based applications and guarantees statistical convergence…
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…
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…