Development of Data-Driven System in Materials Integration

被引：8

作者：

Inoue, Junya ^{[1
]}

Okada, Masato ^{[2
]}

Nagao, Hiromichi ^{[3
]}

Yokota, Hideo ^{[4
]}

Adachi, Yoshitaka ^{[5
]}

机构：

[1] Univ Tokyo, Inst Ind Sci, Tokyo 1538904, Japan

[2] Univ Tokyo, Grad Sch Frontier Sci, Kashiwa, Chiba 2778561, Japan

[3] Univ Tokyo, Earthquake Res Inst, Tokyo 1130032, Japan

[4] RIKEN Ctr Adv Photon, Image Proc Res Team, Wako, Saitama 3510198, Japan

[5] Nagoya Univ, Sch Engn, Nagoya, Aichi 4648603, Japan

来源：

MATERIALS TRANSACTIONS | 2020年 / 61卷 / 11期

关键词：

materials integration; structural materials; integrated computational materials engineering; sparse modelling; data assimilation; database; ENSEMBLE KALMAN FILTER; DATA ASSIMILATION; GRAIN-GROWTH; PREDICTION; MODEL;

D O I：

10.2320/matertrans.MT-MA2020006

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A Data-driven analysis system developed in the first-term SIP "Structural Material for Innovation" is briefly explained using several practical applications. The developed system is composed of two major systems: the data-driven prediction system and the 3D/4D analysis system. In the data-driven prediction system, the two methods in data science, that is, data assimilation and sparse modeling, are applied to optimize model parameters for the physical and phenomenological models developed in other MI systems, such as the structure and performance prediction and microstructure prediction modules, using experimental and numerical databases. Whereas, in the 3D/4D analysis system, it is demonstrated that the microstructural database can be efficiently utilized to predict mechanical properties, as well as to extract detailed geometrical information concerning the constituent microstructures.

引用

页码：2058 / 2066

页数：9

共 31 条

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[2]

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