Efficient use of multiple information sources in material design

Abstract We present a general framework for the design/optimization of materials that is capable of accounting for multiple information sources available to the materials designer. We demonstrate the framework through the microstructure-based design of multi-phase microstructures. Specifically, we seek to maximize the strength normalized strain-hardening rate of a dual-phase ferritic/martensitic steel through a multi-information source Bayesian optimal design strategy. We assume that we have multiple sources of information with varying degrees of fidelity as well as cost. The available information from all sources is fused through a reification approach and then a sequential experimental design is carried out. The experimental design seeks not only to identify the most promising region in the materials design space relative to the objective at hand, but also to identify the source of information that should be used to query this point in the decision space. The selection criterion for the source used, accounts for the discrepancy between the source and the ‘ground truth’ as well as its cost. It is shown that when there is a hard constraint on the budget available to carry out the optimization, accounting for the cost of querying individual sources is essential.