A bayesian inference-based approach to empirical training of strongly coupled constituent models

被引：1
作者：
Flynn G.S. [1 ]
Chodora E. [2 ]
Atamturktur S. [3 ]
Brown D.A. [4 ]
机构：
[1] Los Alamos National Laboratory, Los Alamos, 87545, NM
[2] Mechanical Engineering, Clemson University, Clemson, 29634, SC
[3] Architectural Engineering, The Pennsylvania State University, State College, 16801, PA
[4] Mathematical and Statistical Sciences, Clemson University, Clemson, 29634, SC
来源：
Journal of Verification, Validation and Uncertainty Quantification | 2019年 / 4卷 / 02期
关键词：
Elasto-plastic deformation; Empirical surrogate; Gaussian process model; Model calibration; Multiphysics; Multiscale; Statistical inference;
D O I：
10.1115/1.4044804
中图分类号：
学科分类号：
摘要：
Partitioned analysis enables numerical representation of complex systems through the coupling of smaller, simpler constituent models, each representing a different phenomenon, domain, scale, or functional component. Through this coupling, inputs and outputs of constituent models are exchanged in an iterative manner until a converged solution satisfies all constituents. In practical applications, numerical models may not be available for all constituents due to lack of understanding of the behavior of a constituent and the inability to conduct separate-effect experiments to investigate the behavior of the constituent in an isolated manner. In such cases, empirical representations of missing constituents have the opportunity to be inferred using integral-effect experiments, which capture the behavior of the system as a whole. Herein, we propose a Bayesian inferencebased approach to estimate missing constituent models from available integral-effect experiments. Significance of this novel approach is demonstrated through the inference of a material plasticity constituent integrated with a finite element model to enable efficient multiscale elasto-plastic simulations. © 2019 by ASME.
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