2024-03-28T18:57:21Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:00031021
2023-01-16T04:24:31Z
320:321:322
Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling
Takeno, Shion
102579
Tsukada, Yuhki
102580
Fukuoka, Hitoshi
102581
Koyama, Toshiyuki
102582
Shiga, Motoki
102583
Karasuyama, Masayuki
102584
Information regarding precipitate shapes is critical for estimating material parameters. Hence, we considered estimating a region of material parameter space in which a computational model produces precipitates having shapes similar to those observed in the experimental images. This region, called the lower-error region (LER), reflects intrinsic information of the material contained in the precipitate shapes. However, the computational cost of LER estimation can be high because the accurate computation of the model is required many times to better explore parameters. To overcome this difficulty, we used a Gaussian-process-based multifidelity modeling, in which training data can be sampled from multiple computations with different accuracy levels (fidelity). Lower-fidelity samples may have lower accuracy, but the computational cost is lower than that for higher-fidelity samples. Our proposed sampling procedure iteratively determines the most cost-effective pair of a point and a fidelity level for enhancing the accuracy of LER estimation. We demonstrated the efficiency of our method through estimation of the interface energy and lattice mismatch between MgZn2 and α−Mg phases in an Mg-based alloy. The results showed that the sampling cost required to obtain accurate LER estimation could be drastically reduced.
journal article
American Physical Society
2020-08-11
application/pdf
Physical Review Materials
8
4
083802
2475-9953
https://nagoya.repo.nii.ac.jp/record/31021/files/PhysRevMaterials4_083802.pdf
eng
https://doi.org/10.1103/PhysRevMaterials.4.083802
© 2020 American Physical Society