This tutorial will present the fundamentals of multi-fidelity fusion as well as Sequential and Batch Bayesian Optimization as possible optimization approaches that can be integrated with high accuracy computational models or experimental procedures to speed up the optimization or design of materials. The tutorial will also provide an overview of the BAREFOOT framework that combines these two approaches together to further improve the rate of the optimization. All these approaches are compared using the optimization of the workability of a dual-phase steel microstructure.

The nanoHUB tool "A Batch Reification/Fusion Optimization Framework for Bayesian-based Material Optimization" is used in this hands-on tutorial.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Richard Couperthwaite (2021), "Batch Reification Fusion Optimization (BAREFOOT) Framework," https://nanohub.org/resources/35143.

  BibTex | EndNote

1. BAREFOOT framework
2. Bayesian
3. Bayesian statistics
4. design of materials
5. machine learning
6. NCN Group - Machine Learning Hands-on
7. Optimization