On Sequential Sampling for Global Metamodeling in Engineering Design

By Ruichen Jin, Wei Chen, Agus Sudjianto. Design Automation Conference, 2002

379 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fe8e4856c2d5cce3f7e70061f35dc16926b8d13a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Design of computer experiments: A review (2017, methodology): “[82] proposed two approaches namely the Maximin Scaled Distance (MSD) and the Cross Validation (CV).”
Use of Kriging Models to Approximate Deterministic Computer Models (2005, background): “It is defined as: ( ) ( ] 2 1 7sin 0.5 sin 0.7 , 0,5 . x The third test problem is the Branin test function [21].”
Kriging metamodeling in simulation: A review (2009, neutral): “) • Reference [23] studies sequential designs for Kriging metamodels, using (20), which assumes known parameters of the underlying covariance process.”
Investigation of an adaptive sampling method for data interpolation using radial basis functions (2010, methodology): “[14] provide a fairly comprehensive description of several methods and a comparison using Kriging and RBFs.”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, background): “[69] compared a few different sequential sampling schemes and found that sequential sampling allows engineers to control the sampling process and it is generally more efficient than one-stage sampling.”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, methodology): “Nevertheless, we would like to point out that sequential sampling strategies (such as the variants of the efficient global optimization [64] and enhanced sequential optimization algorithms [92]) have been heavily studied recently.”
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, result): “Jin et al. (2002) compared sequential sampling with one stage sampling.”
Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, background): “[47] compared a few different sequential sampling schemes and found that sequential sampling allows engineers to control the sampling process and it is generally more efficient than one-stage sampling.”
A comparative study of metamodeling methods considering sample quality merits (2010, methodology): “Jin et al. (2002) introduced some sequential sampling methods to be used in computer experiments.”
Predictive quantification of surrogate model fidelity based on modal variations with sample density (2015, methodology): “In the context of surrogate-based design, this information can be directly applied for (i) surrogate model refinement (Atamturktur et al. 2013; Jin et al. 2002; Kleijnen and Beers 2004), (ii) surrogate model selection (Chowdhury et al.”