Multi-fidelity optimization via surrogate modelling

By Alexander I. J. Forrester, András Sóbester, Andy J. Keane. 2007

1,131 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Environmental Engineering, Management Science and Operations Research

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Citations in the corpus

Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017, methodology): “BO techniques have been used in developing multi-ﬁdelity optimisation methods in various applications such as hyper-parameter tuning and industrial design (Forrester et al., 2007; Huang et al., 2006; Klein et al., 2015; Poloczek et al., 2016; Swersky et al., 2013).”
Multi-Information Source Optimization (2016, background): “In this problem, IS are supposed to form a strict hierarchy [16, 14, 6, 24, 20, 19, 15].”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, methodology): “[144] combined cokriging (extension of kriging for the case of several responses) with a Bayesian model update criterion based on an error estimate that reflects the amount of noise in the observed data and demonstrated the approach by awing aerodynamic design problem.”
Review of multi-fidelity models (2016, methodology): “Many MFMs can harness (or can be extended to) more than two fidelities, as evidenced by the works of Huang et al. (2006) [95], Forrester et al. (2007) [71], Qian et al. (2008) [174], and Goh et al. (2013) [80], Kandasamy et al. (2016) [104] and others.”
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization (2019, background): “This is a special case of a multi-ﬁdelty optimization problem (Forrester et al., 2007), with ﬁdelity parameter M .”
Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not (2008, methodology): “115 combined co-kriging (extension of kriging for the case of several responses) with a Bayesian model update criterion based on an error estimate that reflects the amount of noise in the observed data, and demonstrated the approach by a wing aerodynamic design problem.”
Performance study of multi-fidelity gradient enhanced kriging (2015, methodology): “For example, various multi-fidelity surrogate modelling methods are already presented in the literature (Craig et al. 1998; Cumming and Goldstein 2009; Goldstein and Wooff 2007; Higdon et al. 2004; Kennedy and O’Hagan 2000; Forrester et al. 2007; Bandler et al. 1994).”
A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent (2019, background): “1) find applications in multi-agent target seeking [7], distributed machine learning [15, 30, 12, 3, 53, 1, 5], and wireless networks [11, 24, 3], among other scenarios.”
Recent advances in surrogate-based optimization (2009, methodology): “[23] extend the method from prediction to optimization and present the aerodynamic design of a wing using correlated empirical and panel codes.”
Model-based methods for continuous and discrete global optimization (2017, background): “expensive simulation data can be combined into one surrogate model [46, 47].”
A Tutorial on Bayesian Optimization (2018, neutral): “Work on multi-ﬁdelity optimization includes Huang et al. (2006); S´obester et al. (2004); Forrester et al. (2007); McLeod et al. (2017); Kandasamy et al. (2016).”
The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting (2019, methodology): “These methods take the name of multifidelity models (Fernández-Godino et al., 2016; Forrester et al., 2007).”