Enabling high-dimensional hierarchical uncertainty quantification by ANOVA and tensor-train decomposition

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Volumn 34, Issue 1, 2015, Pages 63-76

  Zhang, Zheng ^a   Yang, Xiu ^b   Oseledets, Ivan V ^c   Karniadakis, George E ^b   Daniel, Luca ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b BROWN UNIVERSITY   (United States)

^c SKOLKOVO INSTITUTE OF SCIENCE AND TECHNOLOGY   (Russian Federation)

Author keywords

Analysis of variance (ANOVA); circuit simulation; generalized polynomial chaos (gPC); hierarchical uncertainty quantification; high dimensionality; microelectromechanical systems (MEMS) simulation; stochastic modeling and simulation; tensor train; uncertainty quantification

Indexed keywords

CIRCUIT SIMULATION; ELECTROMECHANICAL DEVICES; HIERARCHICAL SYSTEMS; MATLAB; MEMS; PERSONAL COMPUTERS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TENSORS; UNCERTAINTY ANALYSIS;

GENERALIZED POLYNOMIAL CHAOS (GPC); HIGH DIMENSIONALITY; MICRO ELECTROMECHANICAL SYSTEM (MEMS); TENSOR TRAINS; UNCERTAINTY QUANTIFICATIONS;

ANALYSIS OF VARIANCE (ANOVA);

EID: 84919819040     PISSN: 02780070     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCAD.2014.2369505     Document Type: Article

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