This paper presents a new methodology to extend the Data-Driven Identification (DDI) to heterogeneous samples made of multiple elastic materials. By using the Correspondence Analysis (CA) technique to post-process DDI, we are able to identify multiple material databases representative of the material behavior of each phase. Simultaneously, we localize the different phases (matrix and inclusions) in the sample. For different contrasts between phases, the method is tested on synthetically generated data and a parametric study is performed. Furthermore, we show that it is possible to iterate between DDI and CA in order to improve the method’s predictions when it is limited by scarce input data. In all the cases studied, the methodology proves to be effective for estimating stresses, as well as for identifying the different phases in the sample.

本文提出了一种将数据驱动识别(DDI)扩展到由多种弹性材料制成的异质样品的新方法。通过使用对应分析(CA) 技术对 DDI 进行后处理，我们能够识别代表每个相的材料行为的多个材料数据库。同时，我们定位样品中的不同相（基质和夹杂物）。对于阶段之间的不同对比，该方法在综合生成的数据和进行参数研究。此外，我们表明可以在 DDI 和 CA 之间进行迭代，以便在受到稀缺输入数据限制时改进方法的预测。在所有研究的案例中，该方法证明对于估计应力以及识别样品中的不同相是有效的。