Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions

By S. Shan, G. G. Wang. 2010

625 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/58590f6e9eafd0835df01d16dc80208ad41e8ed1

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In Artificial Intelligence

In Civil and Structural Engineering

In Computational Theory and Mathematics

Citations in the corpus

Design Structure Matrix Extensions and Innovations: A Survey and New Opportunities (2016, background): “, [72], [73]), particularly multidisciplinary design optimization (MDO) [74]–[76].”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “For more information one can refer to recent review studies (Queipo et al. 2005; Forrester and Keane 2009; Shan and Wang 2010b; Parr et al. 2010; Jin 2011; Martins and Lambe 2013; Haftka et al. 2016) and the references therein.”
Auction optimization using regression trees and linear models as integer programs (2014, neutral): “eturning a decision rule given a state with scenario tree techniques from multi-stage stochastic programming. This line of work shares similarities with the ﬁeld of black-box optimization, see, e.g., [14, 15, 31]. In black-box optimization, methods are used to approximate a function with unknown ana”
Bayesian Optimization for Adaptive Experimental Design: A Review (2020, background): “General strategies for tackling high-dimensionality include [103]: reducing the design space, screening important variables, decomposing the design into simpler sub-problems, mapping into a lower-dimensional space, and visualisation.”
A Tutorial on Bayesian Optimization (2018, neutral): “See also Shan and Wang (2010).”
Review of surrogate modeling in water resources (2012, background): “…Ratto et al., 2007; Young and Ratto, 2009], (2) decomposition aiming to decompose the original problem into a set of smaller-scale subproblems [Shan and Wang, 2010b], and (3) space reduction being concerned with shrinking the feasible decision variable space by reducing the variable ranges to…”
Managing computational complexity using surrogate models: a critical review (2020, neutral): “7 (Shan and Wang 2010).”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, background): “Shan and Wang [208,209] note that new metamodeling techniques are needed to handle the peculiarities of high-dimensional problems.”