Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Evolutionary Intelligence
Published in: Evolutionary Intelligence 4/2022

24-01-2020 | Special Issue

Use a sequential gradient-enhanced-Kriging optimal experimental design method to build high-precision approximate model for complex simulation problem

Authors: Yaohui Li, Junjun Shi, Jingfang Shen

Published in: Evolutionary Intelligence | Issue 4/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The surrogate model based on Kriging has been widely used to approximate simulation problems of expensive computing. Although the accuracy of the gradient enhanced Kriging (GEK) is often higher than that of ordinary Kriging, designers cannot avoid more time consuming during gradient calculation of GEK. To this end, a sequential gradient-enhanced-Kriging optimal experimental design method with the Gaussian correlation function (GCF) is investigated to approximate complex black-box simulation problems by introducing gradient information of Kriging parameters. Due to the differentiable GCF, the gradient information can be simply evaluated. This characteristic make the proposed method effectively improve the modeling accuracy and efficiency of GEK. As expected, the test results from benchmark functions and the cycloid gear pump simulation show the feasibility, stability and applicability of the proposed method.
previous article Frequency offset estimation based on a combination of FFT and CZT for DP-QDB coherent receiver
next article A Kriging-based multi-point sequential sampling optimization method for complex black-box problem

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Park C, Apley DJTJoMLR (2018) Patchwork Kriging for large-scale gaussian process regression. J Mach Learn Res 19(1):269–311MathSciNetMATH
2.
Rashidinia J et al (2018) A stable gaussian radial basis function method for solving nonlinear unsteady convection–diffusion–reaction equations. Comput Math Appl 75(5):1831–1850MathSciNetCrossRefMATH
3.
Sliwa J (2019) Assessing complex evolving cyber-physical systems (case study: smart medical devices). Int J High Perform Comput Netw 13(3):294–303CrossRef
4.
Sóbester A, Leary SJ, Keane AJ (2005) On the design of optimization strategies based on global response surface approximation models. J Glob Optim 33(1):31–59MathSciNetCrossRefMATH
5.
Dorgham O et al (2018) Enhancing the security of exchanging and storing DICOM medical images on the cloud. Int J Cloud Appl Comput 8(1):154–172
6.
Goléa NE-H, Melkemi KE (2019) ROI-based fragile watermarking for medical image tamper detection. IJHPCN 13(2):199–210CrossRef
7.
Amato F et al (2018) Intelligent medical record management: a diagnosis support system. Int J High Perform Comput Netw 12(4):391–399CrossRef
8.
Couckuyt I, Dhaene T, Demeester P (2014) ooDACE toolbox: a flexible object-oriented Kriging implementation. J Mach Learn Res 15:3183–3186MATH
9.
Forrester A, Keane A (2008) Engineering design via surrogate modelling: a practical guide. Wiley, HobokenCrossRef
10.
Lockwood BA, Anitescu M (2012) Gradient-enhanced universal Kriging for uncertainty propagation. Nucl Sci Eng 170(2):168–195CrossRef
11.
Han Z-H et al (2017) Weighted gradient-enhanced Kriging for high-dimensional surrogate modeling and design optimization. AIAA J 55(12):4330–4346CrossRef
12.
Chen Z et al (2015) An important boundary sampling method for reliability-based design optimization using Kriging model. Struct Multidiscip Optim 52(1):55–70MathSciNetCrossRef
13.
Wang GG, Shan S (2007) Review of metamodeling techniques in support of engineering design optimization. J Mech Des 129(4):370–380CrossRef
14.
Huang Y et al (2019) Mapping soil electrical conductivity using ordinary Kriging combined with back-propagation network. Chin Geogr Sci 29(2):270–282CrossRef
15.
Kleijnen JP (2017) Regression and Kriging metamodels with their experimental designs in simulation: a review. Eur J Oper Res 256(1):1–16MathSciNetCrossRefMATH
16.
Park T et al (2016) Robust Kriging models in computer experiments. J Oper Res Soc 67(4):644–653CrossRef
17.
Shao W et al (2012) Extended Gaussian Kriging for computer experiments in engineering design. Eng Comput 28(2):161–178CrossRef
18.
Chung H S, Alonso J (2002) Design of a low-boom supersonic business jet using cokriging approximation models. In: 9th AIAA/ISSMO symposium on multidisciplinary analysis and optimization (September 4–6, 2002, Atlanta, Georgia), pp 5598:1–21
19.
Chung H-S, Alonso JJ (2002) Using gradients to construct cokriging approximation models for high-dimensional design optimization problems. AIAA Pap 317:2002
20.
Ulaganathan S et al (2015) Performance study of gradient-enhanced Kriging. Eng Comput 32(1):15–34CrossRef
21.
Liu W (2003) Development of gradient-enhanced Kriging approximations for multidisciplinary design optimization. Doctoral dissertation, University of Notre Dame
22.
Laurenceau J, Sagaut P (2008) Building efficient response surfaces of aerodynamic functions with Kriging and cokriging. AIAA J 46(2):498–507CrossRef
23.
Han Z-H, Görtz S, Zimmermann R (2013) Improving variable-fidelity surrogate modeling via gradient-enhanced Kriging and a generalized hybrid bridge function. Aerosp Sci Technol 25(1):177–189CrossRef
24.
Ulaganathan S et al (2016) Performance study of gradient-enhanced Kriging. Eng Comput 32(1):15–34CrossRef
25.
Lester WA Jr, Krauss M (1964) Gaussian correlation functions: two-electron systems. J Chem Phys 41(5):1407–1413CrossRef
26.
Zorn R (1997) Deviation from Gaussian behavior in the self-correlation function of the proton motion in polybutadiene. Phys Rev B 55(10):6249CrossRef
27.
Stein ML (2012) Interpolation of spatial data: some theory for Kriging. Springer, Berlin
28.
Parr JM et al (2012) Infill sampling criteria for surrogate-based optimization with constraint handling. Eng Optim 44(10):1147–1166CrossRef
29.
Lin Y (2004) An efficient robust concept exploration method and sequential exploratory experimental design. Georgia Institute of Technology, Atlanta
30.
Kenny QY, Li W, Sudjianto A (2000) Algorithmic construction of optimal symmetric Latin hypercube designs. J Stat Plan Inference 90(1):145–159MathSciNetCrossRefMATH
31.
Lophaven SN, Nielsen HB, Søndergaard J, Aspects of the matlab toolbox DACE (2002) Informatics and mathematical modelling. Technical University of Denmark, DTU, Lyngby
32.
Wang L et al (2018) Construction of orthogonal symmetric Latin hypercube designs. Stat Sin 28(3):1503–1520MathSciNetMATH
33.
Martin JD, Simpson TW (2005) Use of Kriging models to approximate deterministic computer models. AIAA J 43(4):853–863CrossRef
34.
35.
Du KL, Swamy MNS (2016) Particle swarm optimization. In: Search and optimization by metaheuristics. Springer, Birkhäuser, Cham, pp 153–173CrossRef
36.
Fabiani M et al (1999) Modeling and simulation of gerotor gearing in lubricating oil pumps. SAE Trans 108(3):989–1003
Metadata
Title
Use a sequential gradient-enhanced-Kriging optimal experimental design method to build high-precision approximate model for complex simulation problem
Authors
Yaohui Li
Junjun Shi
Jingfang Shen
Publication date
24-01-2020
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 4/2022
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-019-00345-z

Other articles of this Issue 4/2022

Evolutionary Intelligence 4/2022 Go to the issue

Special Issue

Model for estimating power dissipation along the interconnect length in single on-chip topology

Special Issue

Prediction of rock mass rating using neural network with an improved rider optimization algorithm

Special Issue

Simulation of urban pattern evolution trend based on satellite GIS and remote sensing

Special Issue

Design of distributed temperature-measuring system based on 1-wire bus for ultra-kilometre frozen deep well

Special Issue

Neural multi-task collaborative filtering

Special Issue

Construction of cascaded depth model based on boosting feature selection and classification

Premium Partner

    Image Credits