Surrogate-based Analysis and Optimization

By Néstor V. Queipo, Raphael T. Haftka, Wei Shyy, Tushar Goel, Rajkumar Vaidyanathan, P. Tucker. 2005

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “Queipo et al. (2005) presented an overview of “one-shot” surrogate based analysis and optimization methods in the aerospace science and engineering field.”
Pitfalls of using a single criterion for selecting experimental designs (2008, background): “There have been some studies on the influence of different experimental designs (EDs) on predictions [1–4].”
Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016, methodology): “Data-driven modeling approaches have become popular in recent years because of their wide applicability (Queipo et al., 2005; Wang & Shan, 2007; Cozad et al., 2014) and are used in one of the following three situations: (1) The existing model is too complex to be reduced to the desired degree by usi”
Comparing error estimation measures for polynomial and kriging approximation of noise-free functions (2009, methodology): “Split sample, k-fold cross-validation, and bootstrapping ( Queipo et al. 2005 ) are some of the distribution-free error measures.”
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].”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “Multiﬁdelity optimization reduces the runtime of the optimization process by using low-ﬁdelity models to accelerate the search [24, 112, 71, 70] or by using a low-ﬁdelity model in conjunction with adaptive corrections and a trust-region model-management scheme [1, 2, 24, 135, 172].”
Ensemble of metamodels with optimized weight factors (2008, background): “Queipo et al. (2005) provide a good review of different metamodeling techniques.”
Multiscale structural topology optimization with an approximate constitutive model for local material microstructure (2015, unknown): “Alternatively, reduction in computational cost can be achieved by using Reduced Order Models (ROMs) [19,20].”
Axial-Flow Ventilation Fan Design Through Multi-Objective Optimization to Enhance Aerodynamic Performance (2011, methodology): “Leave-one-out cross-validation (CV) [32] is also conducted to assess the accuracy of the RSA models.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, unknown): “[253] N.”
Review of multi-fidelity models (2016, background): “Kriging estimates the value of a function as the sum of a trend function (e.g., polynomial) representing low-frequency variation and a systematic departure representing high-frequency variation components [176].”
Comparison of surrogate models for turbomachinery design (2008, background): “Also included are descriptions of several designs of experiments and surrogate based strategies [22].”