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.

This Paper was Originally Published in Japanese in Materia Japan 58 (2019) 503–510.