Sequential kriging optimization using multiple-fidelity evaluations

By Deng Huang, Theodore T. Allen, William I. Notz, R. Allen Miller. 2006

454 citations recorded by the source corpus.

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

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 Management Science and Operations Research

Citations in the corpus

Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion (2019, methodology): “Multi-ﬁdelity optimization has been deeply studied on surrogate model optimization such as Bayesian optimization (Huang et al. 2006; Kandasamy et al. 2016; 2017).”
Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017, methodology): “In fact, in many multi-ﬁdelity papers, the ﬁnite approximations were obtained by discretising a continuous space (Huang et al., 2006; Kandasamy et al., 2016a).”
Kriging is well-suited to parallelize optimization (2010, unknown): “[ 18] focuses on multiple numerical simulators with different levels of ﬁdelity, and introduces the so-called augmented EI criterion, integrating possible heterogeneity in the simulation times.”
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations (2016, background): “Some literature have analysed multi-ﬁdelity methods in speciﬁc contexts such as hyper-parameter tuning, active learning and reinforcement learning [1, 5, 26, 33].”
Multi-Information Source Optimization (2016, methodology): “, finite element analyses with different mesh fineness by Huang et al. [2006a] or calculations based on shared data sets.”
A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations (2013, methodology): “A generalization of this model is proposed in Qian and Wu (2008); its application for optimization can be found in Han et al. (2010), Huang et al. (2006), Forrester et al. (2007), Laurenceau and Sagaut (2008) or Yamazaki and Mavriplis (2011).”
A Stochastic Collocation Algorithm with Multifidelity Models (2014, methodology): “In general, building in various analysis models using a Gaussian process surrogate construction is quite popular [13, 12, 11, 10].”
A Tutorial on Bayesian Optimization (2018, background): “(1998), innovations developed in that same literature include multi-fidelity optimization (Huang et al., 2006; Sóbester et al., 2004), multi-objective optimization (Keane, 2006; Knowles, 2006; Močkus and Močkus, 1991), and a study of convergence rates (Calvin, 1997; Calvin and Žilinskas, 2000; Calvi”
Recent Advances in Bayesian Optimization (2022, background): “[78] proposed an augmented EI function to account for diferent idelity levels of an inill point.”
Geometric Filtration Using Proper Orthogonal Decomposition for Aerodynamic Design Optimization (2010, methodology): “[12] in the creation of surrogate models of deterministic computational experiments and has been used successfully in the optimization of a number of different design problems [13–16].”
An effective warpage optimization method in injection molding based on the Kriging model (2008, methodology): “Recently, Huang et al. [ 21 ] have used the Kriging model to minimize die wear for metal-forming process design improvement.”
Multi-fidelity Gaussian Process Bandit Optimisation (2016, methodology): “, 1998) MF-SKO Multi-fidelity Sequential Kriging Optimisation (Huang et al., 2006) MF-NAIVE Naive multi-fidelity method described in Section 7.”
Review of multi-fidelity models (2016, background): “However, it is worth noting that MFMs can be developed using more than two ﬁdelities, as demonstrated in several studies, including Huang et al., 2006 [76], Forrester et al., 2007 [58], Qian et al., 2008 [147], Le Gratiet, 2013 [109] and Goh et al., 2013 [66].”