Engineering Design via Surrogate Modelling - A Practical Guide

By Alexander I. J. Forrester, András Sóbester, Andy J. Keane. 2008

3,033 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Software

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 Industrial and Manufacturing Engineering

In Software

Citations in the corpus

Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (2019, background): “ethical considerations. Several areas of the sciences and engineering are already beneﬁting from computational modeling, including the medical device industry [23, 19], car and aircraft manufacturing [5], and public policy design [40], to name a few. Despite their massive adoption, researchers can o”
Solving Irregular and Data-enriched Differential Equations using Deep Neural Networks (2019, methodology): “To mitigate the expense of running forward models, surrogate modeling [51] and multifidelity hierarchies [52] are often conceived in order to develop cost-effective ways of examining the response of a PDE relative to its parametric responses.”
The mechanics of composite corrugated structures: A review with applications in morphing aircraft (2015, methodology): “[114]) was used as an approximate modelling technique to mimic the behaviour of the finite element simulations as closely as possible, while reducing the cost of computations.”
Sensitivity analysis of environmental models: A systematic review with practical workflow (2014, methodology): “A practice-oriented introduction to these techniques can be found for example in Forrester et al. (2008) (Section 1.4) and Press et al. (1992) (Section 7.7).”
Performance study of multi-fidelity gradient enhanced kriging (2015, background): “3.1 Analytical examples The one-dimensional analytical function is obtained from Forrester et al. (2008).”
Two roles for ecological surrogacy: Indicator surrogates and management surrogates (2016, background): “95 We begin with a definition of ecological surrogacy to distinguish it from surrogacy in 96 medicine, engineering, and other fields (Forrester et al. 2008, Barton et al. 2015): 97 Ecological surrogate: An ecological process or element (e.g., species, ecosystem, or abiotic 98 factor) that is used…”
Convergence properties of the expected improvement algorithm with fixed mean and covariance functions (2007, neutral): “Since Jones et al. (1998), the expected improvement (EI) algorithm has become a very popular algorithm to optimize an expensive-to-evaluate function.”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “Kriging models [57] have been widely used in [53] and [58]”
A Tutorial on Bayesian Optimization (2018, methodology): “Over a longer period, since the 1960s, BayesOpt has been used extensively for designing engineering systems (Moˇckus, 1989; Jones et al., 1998; Forrester et al., 2008).”
Data-driven operator inference for nonintrusive projection-based model reduction (2016, methodology): “In contrast to projection-based reduced models, data-fit surrogate models are constructed using interpolation and regression techniques to directly learn the map from inputs to outputs of the full model [19, 20, 21, 22].”
A Python surrogate modeling framework with derivatives (2019, background): “Wing weight [11] The estimate of the weight of a light aircraft wing is given by”
Review of multi-fidelity models (2016, methodology): “The Forrester function, introduced by Forrester et al. (2008) [71], is a simple yet insightful test case, often used to introduce the basics of multi-fidelity optimization due to its tractability and interpretable low and high-fidelity versions.”
Extraction of mechanical properties of materials through deep learning from instrumented indentation (2020, methodology): “This Bayesian multifidelity modeling based on Gaussian process regression (GPR) (33) can also alleviate the extreme computational cost of training.”