Recent advances in surrogate-based optimization

By Alexander I. J. Forrester, Andy J. Keane. 2009

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Citations in the corpus

Robots that can adapt like animals (2014, methodology): “Like all model-based optimization algorithms (e.g. surrogate-based algorithms 36,37,38 , kriging 39 , or DACE 40,41 ), Bayesian optimization creates a model of the objective function with a regression method, uses this model to select the next point to acquire, then updates the model, etc.”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, background): “2) Methods for assessing accuracy of metamodeling techniques 2) Raised the idea that the benefits of sequential sampling for global approximation are not clear yet 3) Benefits ofmetamodeling for visualization and design space exploration 3) Identified the importance of uncertainty quantification in ”
Recent advances and applications of surrogate models for finite element method computations: a review (2022, methodology): “Polynomial surrogates remain generally not well suited for the nonlinear, multidimensional, multi-modal design landscapes one deals with in engineering unless the ranges of the considered variables aremade sufficiently small, e.g. in trust-regionmethods (Forrester and Keane 2009).”
Data-driven operator inference for nonintrusive projection-based model reduction (2016, background): “, where the notion of state is unimportant and where the interpolation and regression of the outputs provide sufficiently accurate results, see [31].”
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].”
Predictive quantification of surrogate model fidelity based on modal variations with sample density (2015, methodology): “2000), Kriging (Simpson et al. 2001; Forrester and Keane 2009), Radial Basis Functions (RBF) (Hardy 1971), Neural Networks (Yegnanarayana 2004), Support Vector Regression (Gunn 1998), and hybrid surrogate models (Queipo et al.”
Bio-inspired computation: Where we stand and what's next (2019, background): “Two remarkable overviews are the ones in [186] and [187], which focus on aerospace sciences, a sector where surrogate model based optimization has been utilized in manifold design applications due to the complexity of conducting real experiments.”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “Kriging models [57] have been widely used in [53] and [58]”
Optimal Model Management for Multifidelity Monte Carlo Estimation (2016, methodology): “For example, available surrogate models may include projection-based reduced models [44, 43, 24, 3], data-fit interpolation and regression models [21], machine-learning-based support vector machines (SVMs) [16, 48, 11], and simplified-physics models [2, 37].”
Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function (2013, background): “is demonstrated for an analytic problem taken from [21].”
Managing computational complexity using surrogate models: a critical review (2020, background): “Forrester and Keane (2009) review recent advances in surrogate modeling, including (Bandler and Madsen 2001; Bandler et al. 2008).”