Durantin,
C.
,
Marzat,
J.
, and
Balesdent,
M.
, 2016, “
Analysis of Multi-Objective Kriging-Based Methods for Constrained Global Optimization,” Comput. Optim. Appl.,
63(3), pp. 903–926.

Sasena,
M. J.
,
Papalambros,
P.
, and
Goovaerts,
P.
, 2002, “
Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization,” Eng. Optim.,
34(3), pp. 263–278.

Ghoreishi,
S. F.
, and
Allaire,
D. L.
, 2018, “
A Fusion-Based Multi-Information Source Optimization Approach Using Knowledge Gradient Policies,” AIAA Paper No. 2018-1159.

Parr,
J.
,
Holden,
C. M.
,
Forrester,
A. I.
, and
Keane,
A. J.
, 2010, “
Review of Efficient Surrogate Infill Sampling Criteria With Constraint Handling,” Second International Conference on Engineering Optimization, Lisbon, Portugal, Sept. 6–9, pp. 1–10.

https://pdfs.semanticscholar.org/df63/8813760971a61daaa91259ff65b817d0dd51.pdf
Shan,
S.
, and
Wang,
G. G.
, 2010, “
Metamodeling for High Dimensional Simulation-Based Design Problems,” ASME J. Mech. Des.,
132(5), p. 051009.

Forrester,
A. I.
, and
Keane,
A. J.
, 2009, “
Recent Advances in Surrogate-Based Optimization,” Prog. Aerosp. Sci.,
45(1–3), pp. 50–79.

Booker,
A. J.
,
Dennis,
J. E.
,
Frank,
P. D.
,
Serafini,
D. B.
,
Torczon,
V.
, and
Trosset,
M. W.
, 1999, “
A Rigorous Framework for Optimization of Expensive Functions by Surrogates,” Struct. Optim.,
17(1), pp. 1–13.

MoŠkus J.,1975, “On Bayesian Methods for Seeking the Extremum,” Optimization Techniques IFIP Technical Conference, Novosibirsk, July 1–7, pp. 400–405.

Žilinskas,
A.
, 1992, “
A Review of Statistical Models for Global Optimization,” J. Global Optim.,
2(2), pp. 145–153.

Rasmussen,
C. E.
, 2004, “
Gaussian Processes in Machine Learning,” Advanced Lectures on Machine Learning. ML 2003. Lecture Notes in Computer Science, In: Bousquet O., von Luxburg U., Rätsch G., eds., Vol. 3176, Springer, Berlin, pp. 63–71.

Osborne,
M. A.
,
Garnett,
R.
, and
Roberts,
S. J.
, 2009, “
Gaussian Processes for Global Optimization,” Third International Conference on Learning and Intelligent Optimization (LION3), Trento, Italy, Jan. 14–18, pp. 1–15.

Pandita,
P.
,
Bilionis,
I.
, and
Panchal,
J.
, 2016, “
Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions,” ASME J. Mech. Des.,
138(11), p. 111412.

Koullias,
S.
, and
Mavris,
D. N.
, 2014, “
Methodology for Global Optimization of Computationally Expensive Design Problems,” ASME J. Mech. Des.,
136(8), p. 081007.

Jones,
D. R.
,
Schonlau,
M.
, and
Welch,
W. J.
, 1998, “
Efficient Global Optimization of Expensive Black-Box Functions,” J. Global Optim.,
13(4), pp. 455–492.

Huang,
D.
,
Allen,
T. T.
,
Notz,
W. I.
, and
Miller,
R. A.
, 2006, “
Sequential Kriging Optimization Using Multiple-Fidelity Evaluations,” Struct. Multidiscip. Optim.,
32(5), pp. 369–382.

Humphrey,
D. G.
, and
Wilson,
J. R.
, 2000, “
A Revised Simplex Search Procedure for Stochastic Simulation Response Surface Optimization,” INFORMS J. Comput.,
12(4), pp. 272–283.

Borror,
C. M.
,
Montgomery,
D. C.
, and
Myers,
R. H.
, 2002, “
Evaluation of Statistical Designs for Experiments Involving Noise Variables,” J. Qual. Technol.,
34(1), p. 54.

Gablonsky,
J. M.
, and
Kelley,
C. T.
, 2001, “
A Locally-Biased Form of the Direct Algorithm,” J. Global Optim.,
21(1), pp. 27–37.

Villemonteix,
J.
,
Vazquez,
E.
, and
Walter,
E.
, 2009, “
An Informational Approach to the Global Optimization of Expensive-to-Evaluate Functions,” J. Global Optim.,
44(4), p. 509.

Kushner,
H. J.
, and
Schweppe,
F. C.
, 1964, “
A Maximum Principle for Stochastic Control Systems,” J. Math. Anal. Appl.,
8(2), pp. 287–302.

Jones,
D. R.
, 2001, “
A Taxonomy of Global Optimization Methods Based on Response Surfaces,” J. Global Optim.,
21(4), pp. 345–383.

Shimoyama,
K.
,
Sato,
K.
,
Jeong,
S.
, and
Obayashi,
S.
, 2013, “
Updating Kriging Surrogate Models Based on the Hypervolume Indicator in Multi-Objective Optimization,” ASME J. Mech. Des.,
135(9), p. 094503.

Brochu,
E.
,
Cora,
V. M.
, and
De Freitas,
N.
, 2010, “
A Tutorial on Bayesian Optimization of Expensive Cost Functions, With Application to Active User Modeling and Hierarchical Reinforcement Learning,” e-print arXiv:1012.2599.

https://arxiv.org/abs/1012.2599
Lai,
T. L.
, and
Robbins,
H.
, 1985, “
Asymptotically Efficient Adaptive Allocation Rules,” Adv. Appl. Math.,
6(1), pp. 4–22.

Hernández-Lobato,
J. M.
,
Hoffman,
M. W.
, and
Ghahramani,
Z.
, 2014, “
Predictive Entropy Search for Efficient Global Optimization of Black-Box Functions,” Adv. Neutral Inf. Process. Syst.,

**1**, pp. 918–926.

https://pdfs.semanticscholar.org/fbce/d739237b1bd07a6f3fb627b4c948751ca659.pdf
Moore,
R. A.
,
Romero,
D. A.
, and
Paredis,
C. J.
, 2014, “
Value-Based Global Optimization,” ASME J. Mech. Des.,
136(4), p. 041003.

Thompson,
S. C.
, and
Paredis,
C. J.
, 2010, “
An Investigation Into the Decision Analysis of Design Process Decisions,” ASME J. Mech. Des.,
132(12), p. 121009.

Frazier,
P. I.
,
Powell,
W. B.
, and
Dayanik,
S.
, 2008, “
A Knowledge-Gradient Policy for Sequential Information Collection,” SIAM J. Control Optim.,
47(5), pp. 2410–2439.

Gupta,
S. S.
, and
Miescke,
K. J.
, 1996, “
Bayesian Look Ahead One-Stage Sampling Allocations for Selection of the Best Population,” J. Stat. Plann. Inference,
54(2), pp. 229–244.

Frazier,
P.
,
Powell,
W.
, and
Dayanik,
S.
, 2009, “
The Knowledge-Gradient Policy for Correlated Normal Beliefs,” INFORMS J. Comput.,
21(4), pp. 599–613.

Negoescu,
D. M.
,
Frazier,
P. I.
, and
Powell,
W. B.
, 2011, “
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery,” INFORMS J. Comput.,
23(3), pp. 346–363.

Wu,
J.
,
Poloczek,
M.
,
Wilson,
A. G.
, and
Frazier,
P.
, 2017, “
Bayesian Optimization With Gradients,” eprint arXiv:1703.04389

https://arxiv.org/abs/1703.04389
Andrianakis,
I.
, and
Challenor,
P. G.
, 2012, “
The Effect of the Nugget on Gaussian Process Emulators of Computer Models,” Comput. Stat. Data Anal.,
56(12), pp. 4215–4228.

Vapnik,
V.
, 1998, Statistical Learning Theory,
Wiley,
New York.

Jakeman,
J. D.
, and
Wildey,
T.
, 2015, “
Enhancing Adaptive Sparse Grid Approximations and Improving Refinement Strategies Using Adjoint-Based a Posteriori Error Estimates,” J. Comput. Phys.,
280, pp. 54–71.

Rabitz,
H.
, and
Aliş,
Ö. F.
, 1999, “
General Foundations of High-Dimensional Model Representations,” J. Math. Chem.,
25(2/3), pp. 197–233.

Li,
K.
, and
Allaire,
D.
, 2016, “
A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions,” ASME Paper No. DETC2016-60195.

Gorodetsky,
A. A.
,
Karaman,
S.
, and
Marzouk,
Y. M.
, 2015, “
Function-Train: A Continuous Analogue of the Tensor-Train Decomposition,” eprint arXiv:1510.09088.

https://arxiv.org/abs/1510.09088
Allaire,
D.
, and
Willcox,
K.
, 2010, “
Surrogate Modeling for Uncertainty Assessment With Application to Aviation Environmental System Models,” AIAA J.,
48(8), pp. 1791–1803.

Amaral,
S.
,
Allaire,
D.
, and
Willcox,
K.
, 2017, “
Optimal L_{2}-norm Empirical Importance Weights for the Change of Probability Measure,” Stat. Comput.,
27(3), pp. 625–643.

Altman,
N. S.
, 1992, “
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression,” Am. Statistician,
46(3), pp. 175–185.

Branin,
F. H.
, 1972, “
Widely Convergent Method for Finding Multiple Solutions of Simultaneous Nonlinear Equations,” IBM J. Res. Dev.,
16(5), pp. 504–522.

Huang,
D.
,
Allen,
T. T.
,
Notz,
W. I.
, and
Zeng,
N.
, 2006, “
Global Optimization of Stochastic Black-Box Systems Via Sequential Kriging Meta-Models,” J. Global Optim.,
34(3), pp. 441–466.

Ray,
T.
, 2003, “
Golinski's Speed Reducer Problem Revisited,” AIAA J.,
41(3), pp. 556–558.

Hassan,
R.
,
Cohanim,
B.
,
De Weck,
O.
, and
Venter,
G.
, 2005, “
A Comparison of Particle Swarm Optimization and the Genetic Algorithm,” AIAA Paper No. 2005-1897.

Li,
H.
, and
Papalambros,
P.
, 1985, “
A Production System for Use of Global Optimization Knowledge,” J. Mech., Transm., Autom. Des.,
107(2), pp. 277–284.

Ku,
K. J.
,
Rao,
S.
, and
Chen,
L.
, 1998, “
Taguchi-Aided Search Method for Design Optimization of Engineering Systems,” Eng. Optim.,
30(1), pp. 1–23.

Kanukolanu,
D.
,
Lewis,
K. E.
, and
Winer,
E. H.
, 2006, “
A Multidimensional Visualization Interface to Aid in Trade-Off Decisions During the Solution of Coupled Subsystems Under Uncertainty,” ASME J. Comput. Inf. Sci. Eng.,
6(3), pp. 288–299.

McKay,
M. D.
,
Beckman,
R. J.
, and
Conover,
W. J.
, 1979, “
Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code,” Technometrics,
21(2), pp. 239–245.

De Boer,
P.-T.
,
Kroese,
D. P.
,
Mannor,
S.
, and
Rubinstein,
R. Y.
, 2005, “
A Tutorial on the Cross-Entropy Method,” Ann. Oper. Res.,
134(1), pp. 19–67.

Glover,
F.
, 1989, “
Tabu Search—Part I,” ORSA J. Comput.,
1(3), pp. 190–206.