Bessa, M. A.; Bostanabad, R.; Liu, Z.; Hu, A.; Apley, Daniel W.; Brinson, C.; Chen, W.; Liu, Wing Kam A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality. (English) Zbl 1439.74014 Comput. Methods Appl. Mech. Eng. 320, 633-667 (2017). Summary: A new data-driven computational framework is developed to assist in the design and modeling of new material systems and structures. The proposed framework integrates three general steps: (1) design of experiments, where the input variables describing material geometry (microstructure), phase properties and external conditions are sampled; (2) efficient computational analyses of each design sample, leading to the creation of a material response database; and (3) machine learning applied to this database to obtain a new design or response model. In addition, the authors address the longstanding challenge of developing a data-driven approach applicable to problems that involve unacceptable computational expense when solved by standard analysis methods – e.g. finite element analysis of representative volume elements involving plasticity and damage. In these cases the framework includes the recently developed “self-consistent clustering analysis” method in order to build large databases suitable for machine learning. The authors believe that this will open new avenues to finding innovative materials with new capabilities in an era of high-throughput computing (“big-data”). Cited in 95 Documents MSC: 74A20 Theory of constitutive functions in solid mechanics 74Sxx Numerical and other methods in solid mechanics 74-05 Experimental work for problems pertaining to mechanics of deformable solids 74-10 Mathematical modeling or simulation for problems pertaining to mechanics of deformable solids 65Nxx Numerical methods for partial differential equations, boundary value problems Keywords:design of experiments; reduced order model; self-consistent clustering analysis; machine learning; data mining; plasticity Software:OQMD; spBayes; top.m; VCFEM-HOMO PDFBibTeX XMLCite \textit{M. A. Bessa} et al., Comput. Methods Appl. Mech. Eng. 320, 633--667 (2017; Zbl 1439.74014) Full Text: DOI References: [1] Meyers, M. A.; Chen, P.-Y.; Lin, A. Y.-M.; Seki, Y., Biological materials: Structure and mechanical properties, Prog. Mater. Sci., 53, 1, 1-206 (2008) [2] Jang, D.; Meza, L. R.; Greer, F.; Greer, J. 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