TY - JOUR
T1 - A framework for data-driven analysis of materials under uncertainty
T2 - Countering the curse of dimensionality
AU - Bessa, M. A.
AU - Bostanabad, R.
AU - Liu, Z.
AU - Hu, A.
AU - Apley, Daniel W.
AU - Brinson, C.
AU - Chen, W.
AU - Liu, Wing Kam
N1 - Funding Information:
The authors warmly thank the support from the Air Force Office of Scientific Research Grant No. FA9550-14-1-0032.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/6/15
Y1 - 2017/6/15
N2 - 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”).
AB - 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”).
KW - Design of experiments
KW - Machine learning and data mining
KW - Plasticity
KW - Reduced order model
KW - Self-consistent clustering analysis
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U2 - 10.1016/j.cma.2017.03.037
DO - 10.1016/j.cma.2017.03.037
M3 - Article
AN - SCOPUS:85017641819
SN - 0374-2830
VL - 320
SP - 633
EP - 667
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
ER -