Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not

By Timothy W. Simpson, V. Toropov, Vladimir Balabanov, Felipe Antonio Chegury Viana. 2008

346 citations recorded by the source corpus.

Subfields: Building and Construction, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/de7e7d2c579e92149c194cd49ef34e86276d820d

How researchers see this paper

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

In Building and Construction

In Computational Theory and Mathematics

In Computer Vision and Pattern Recognition

Citations in the corpus

Model-based methods for continuous and discrete global optimization (2017, methodology): “[2] present an interesting history of development in the field.”
A comparative study of metamodeling methods considering sample quality merits (2010, background): “In the past, some basic sample quality merits, such as orthogonality, rotatability, minimum variance and D-optimality, have been developed for the design of experiments ( Simpson et al. 2001b ).”
Using Cross Validation to Design Conservative Surrogates (2010, methodology): “S URROGATE-BASED analysis and optimization has become a major tool in engineering design of complex systems [1–6].”
Managing computational complexity using surrogate models: a critical review (2020, background): “Simpson et al. (2008) report that the prevalent theme in six highly cited metamodeling (or design and analysis of computer experiments) review papers is indeed the high cost of computer simulations.”
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design (2010, background): “One simulation may take many minutes, hours, days or even weeks, quickly rendering parameter studies impractical (Forrester et al., 2008; Simpson et al., 2008).”
Review of surrogate modeling in water resources (2012, methodology): “More recent literature uses DACE to refer to the suite of all metamodel/ emulation techniques [e.g., Ratto et al., 2012; Simpson et al., 2008].”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, background): “…and extensive application of metamodelling strategies over more than four decades in a wide variety of optimization problems from different disciplines (Simpson et al., 2008) suggests that this research field is sufficiently mature so that the pros and cons of metamodelling can be fairly evaluated.”