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Information regarding precipitate shapes is critical for estimating materialparameters. Hence, we considered estimating a region of material parameterspace in which a computational model produces precipitates having shapessimilar to those observed in the experimental images. This region, called thelower-error region (LER), reflects intrinsic information of the materialcontained in the precipitate shapes. However, the computational cost of LERestimation can be high because the accurate computation of the model isrequired many times to better explore parameters. To overcome this difficulty,we used a Gaussian-process-based multifidelity modeling, in which training datacan be sampled from multiple computations with different accuracy levels(fidelity). Lower-fidelity samples may have lower accuracy, but thecomputational cost is lower than that for higher-fidelity samples. Our proposedsampling procedure iteratively determines the most cost-effective pair of apoint and a fidelity level for enhancing the accuracy of LER estimation. Wedemonstrated the efficiency of our method through estimation of the interfaceenergy and lattice mismatch between MgZn2 and {\alpha}-Mg phases in an Mg-basedalloy. The results showed that the sampling cost required to obtain accurateLER estimation could be drastically reduced.

Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling