Abstract Scope |
Gaussian process (GP), as well as Bayesian optimization (BO), has been a cornerstone of Bayesian machine learning methods with naturally enabled uncertainty quantification. In the first half of the talk, we will discuss a generic and versatile BO approach to tackle a handful of general optimization problems, including known and unknown constraints, multi-objective, multi-fidelity, mixed-integer, parallelization on high-performance computers, Big Data, and high-dimensional problems. In the second half of the talk, we will discuss the applications of GP/BO to several ICME models, in the materials design under uncertainty context and in the spirit of the Material Genome Initiative (2011). In particular, using ICME applications as forward models in the process-structure-property relationship, we will discuss how GP/BO fits in as an enabler to the data-driven fourth paradigm for materials design using multiple ICME models, including density functional theory, molecular dynamics, kinetic Monte Carlo, and crystal plasticity finite element. |