skip to main content

Title: Evaluation of convergence behavior of metamodeling techniques for bridging scales in multi-scale multimaterial simulation

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.
Authors:
 [1] ;  [2] ;  [2] ;  [1]
  1. Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States)
  2. Aerospace Engineering, San Diego State University, San Diego, CA 92115 (United States)
Publication Date:
OSTI Identifier:
22465637
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Computational Physics; Journal Volume: 294; Other Information: Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; CALCULATION METHODS; CONVERGENCE; KRIGING; LAGRANGIAN FUNCTION; MATHEMATICAL SPACE; NEURAL NETWORKS; POLYNOMIALS; SCALE MODELS; SIMULATION; STOCHASTIC PROCESSES; TWO-DIMENSIONAL CALCULATIONS; TWO-DIMENSIONAL SYSTEMS