Journal article
Evaluation of convergence behavior of metamodeling techniques for bridging scales in multi-scale multimaterial simulation
Journal of computational physics, Vol.294, pp.585-604
08/2015
DOI: 10.1016/j.jcp.2015.03.043
Abstract
The effectiveness of several metamodeling techniques, viz. the Polynomial Stochastic Collocation method, Adaptive Stochastic Collocation method, a Radial Basis Function Neural Network, a Kriging Method and a Dynamic Kriging Method is evaluated. This is done with the express purpose of using metamodels to bridge scales between micro- and macro-scale models in a multi-scale multimaterial simulation. The rate of convergence of the error when used to reconstruct hypersurfaces of known functions is studied. For sufficiently large number of training points, Stochastic Collocation methods generally converge faster than the other metamodeling techniques, while the DKG method converges faster when the number of input points is less than 100 in a two-dimensional parameter space. Because the input points correspond to computationally expensive micro/meso-scale computations, the DKG is favored for bridging scales in a multi-scale solver.
Details
- Title: Subtitle
- Evaluation of convergence behavior of metamodeling techniques for bridging scales in multi-scale multimaterial simulation
- Creators
- Oishik SenSean DavisGustaaf JacobsH.S Udaykumar
- Resource Type
- Journal article
- Publication Details
- Journal of computational physics, Vol.294, pp.585-604
- DOI
- 10.1016/j.jcp.2015.03.043
- ISSN
- 0021-9991
- eISSN
- 1090-2716
- Grant note
- DOI: 10.13039/100000181, name: Air Force Office of Scientific Research, award: FA9550-12-1-0115; DOI: 10.13039/100000001, name: National Science Foundation, award: DMS-115631
- Language
- English
- Date published
- 08/2015
- Academic Unit
- IIHR--Hydroscience and Engineering; Injury Prevention Research Center; Mechanical Engineering
- Record Identifier
- 9984121963502771
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