Abstract
Computer simulations have become the principal design tool in many engineering disciplines. They are commonly used for verification purposes, but also, more and more often, directly utilized within the design process, e.g., to adjust geometry and/or material parameters of the system of interest so that given performance requirements are satisfied. As a matter of fact, simulation-driven design has become a necessity for a growing number of devices and systems, where traditional approaches (e.g., based on design-ready theoretical models) are no longer adequate. One of the reasons is the increasing level of complexity of engineering systems, as well as various system- and component-level interactions, which have to be taken into account in the design process (Koziel and Ogurtsov 2014a). A reliable evaluation of the system performance can only be obtained (apart from physical measurements of the fabricated prototype) through high-fidelity computer simulations (typically, these simulations are computationally expensive).
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Alexandrov, N.M., Lewis, R.M. (2001) An overview of first-order model management for engineering optimization. Optimization and Engineering. 2, 413–430.
Allaire, D., Willcox, K. (2014) A mathematical and computational framework for multifidelity design and analysis with computer models. Int. J. Uncertainty Quantification, 4, pp. 1–20.
Angiulli, G., Cacciola, M., Versaci, M. (2007) Microwave devices and antennas modelling by support vector regression machines. IEEE Trans. Magnetics, 43, pp. 1589–1592.
Back, T., Fogel, D.B., Michalewicz Z. (eds). (2000) Evolutionary computation 1: basic algorithms and operators. Taylor & Francis Group.
Bandler, J.W., Cheng, Q.S., Dakroury, S.A., Mohamed, A.S., Bakr, M.H., Madsen, K., Søndergaard, J. (2004a) Space mapping: the state of the art. IEEE Trans. Microwave Theory Tech., 52, pp. 337−361.
Bandler, J.W., Cheng, Q.S., Nikolova, N.K., Ismail, M.A. (2004b) Implicit space mapping optimization exploiting preassigned parameters. IEEE Trans. Microwave Theory Tech., 52, pp. 378–385.
Bandler, J.W., Cheng, Q.S., Hailu, D.M., and Nikolova, N.K. (2004c) “A space-mapping design framework,” IEEE Trans. Microwave Theory Tech., vol. 52, no. 11, pp. 2601–2610.
Bischof, C., Bücker, H.M., Hovland, P.D., Naumann, U., and Utke, J., (Eds.) (2008) Advances in Automatic Differentiation, Lecture Notes in Computational Science and Engineering, Springer.
Booker, A.J., Dennis, J.E., Frank, P.D., Serafini, D.B., Torczon, V., Trosset, M.W. (1999) A rigorous framework for optimization of expensive functions by surrogates. Structural Optimization. 17, 1–13.
Conn, A.R., Scheinberg, K., Vicente, L.N. (2009) Introduction to Derivative-Free Optimization. MPS-SIAM Series on Optimization.
Couckuyt, I., Forrester, A., Gorissen, D., De Turck, F., Dhaene, T. (2012) Blind Kriging: Implementation and performance analysis. Advances in Engineering Software, 49, pp. 1–13.
CST Microwave Studio, ver. (2011) CST AG, Bad Nauheimer Str. 19, D-64289 Darmstadt, Germany.
Dorigo M., Gambardella, L.M. (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1, pp. 53−66.
Director, S.W., Rohrer, R.A. (1969) The generalized adjoint network and network sensitivities. IEEE Trans. Circuit Theory, 16, pp. 318−323.
Echeverria, D., Hemker, P.W. (2005) Space mapping and defect correction. CMAM Int. Mathematical Journal Computational Methods in Applied Mathematics, 5, pp. 107–136.
El Sabbagh, M.A., Bakr, M.H., Nikolova, N.K., (2006) Sensitivity analysis of the scattering parameters of microwave filters using the adjoint network method. Int. J. RF and Microwave Computer-Aided Eng., 16, pp. 596–606.
FLUENT, ver. 15.0, ANSYS Inc., Southpointe, 275 Technology Drive, Canonsburg, PA 15317, 2015.
Forrester, A.I.J., Keane, A.J. (2009) Recent advances in surrogate-based optimization, Prog. in Aerospace Sciences, 45, pp. 50−79.
Goldberg, D.E., (1989) Genetic algorithms in search, optimization & machine learning. Pearson Education.
Gorissen, D., Crombecq, K., Couckuyt, I., Dhaene, T., Demeester, P. (2010) A surrogate modeling and adaptive sampling toolbox for computer based design. Journal of Machine Learning Research, 11, pp. 2051-2055.
Griewank, A., (2000) Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Society for Industrial and Applied Mathematics (SIAM), Philadelphia.
Haykin, S. (1998) Neural Networks: A Comprehensive Foundation. 2nd ed. Prentice Hall.
HFSS (2010) Release 13.0, ANSYS, http://www.ansoft.com/products/hf/hfss/.
Hosder, S. (2012) Stochastic Response Surfaces Based On Non-Intrusive Polynomial Chaos for Uncertainty Quantification. International Journal of Mathematical Modeling and Numerical Optimization, Volume 3, No. 1/2, pp. 117–139.
Jacobs, J.P. (2012) Ba200sian support vector regression with automatic relevance determination kernel for modeling of antenna input characteristics. IEEE Trans. Antennas. Prop., 60, pp. 2114-2118.
Jameson, A., (1988). Aerodynamic design via control theory. Journal of Scientific Computing, 3, 233–260.
Jin, Y. (2011) Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm and Evolutionary Computation, 1, pp. 61–70.
Jones, D., Schonlau, M., Welch, W. (1998) Efficient global optimization of expensive black-box functions. Journal of Global Optimization. 13, pp. 455–492.
Kennedy, J. (1997) The particle swarm: social adaptation of knowledge. Proc. 1997 Int. Conf. Evolutionary Computation, Indianapolis, IN, pp. 303−308.
Kolda, T.G., Lewis, R.M., Torczon, V. (2003). Optimization by direct search: new perspectives on some classical and modern methods. SIAM Rev., 45, pp. 385−482.
Koziel, S., Cheng, Q.S., Bandler, J.W. (2008a) Space mapping. IEEE Microwave Magazine, 9, pp. 105–122.
Koziel, S., Bandler, J.W., Madsen, K. (2008b) Quality assessment of coarse models and surrogates for space mapping optimization. Optimization Eng. 9, 375–391.
Koziel, S., Bandler, J.W., Madsen, K. (2009a) Space mapping with adaptive response correction for microwave design optimization. IEEE Trans. Microwave Theory Tech., 57, pp. 478–486.
Koziel, S. (2010a) Shape-preserving response prediction for microwave design optimization. IEEE Trans. Microwave Theory and Tech., 58, pp. 2829–2837.
Koziel, S., Echeverría-Ciaurri, D., Leifsson, L. (2011) Surrogate-based methods, in S. Koziel and X.S. Yang (Eds.) Computational Optimization, Methods and Algorithms, Series: Studies in Computational Intelligence, Springer-Verlag, pp. 33–60.
Koziel, S., Ogurtsov, S., Couckuyt, I., Dhaene, T. (2013b) Variable-fidelity electromagnetic simulations and co-kriging for accurate modeling of antennas. IEEE Trans. Antennas Prop., 61, pp. 1301–1308.
Koziel, S., and Leifsson, L. (2013c) Surrogate-Based Aerodynamic Shape Optimization by Variable-Resolution Models. AIAA Journal, vol. 51, no. 1, pp. 94–106.
Koziel, S., Ogurtsov, S. (2012a) Model management for cost-efficient surrogate-based optimization of antennas using variable-fidelity electromagnetic simulations. IET Microwaves Ant. Prop., 6, pp. 1643–1650.
Koziel, S., Ogurtsov, S. (2014a) Antenna design by simulation-driven optimization. Surrogate-based approach. Springer.
Koziel, S., Ogurtsov, S. (2014b) Fast simulation-driven design of integrated photonic components using surrogate models. IET Microwaves, Antennas Prop.
Leifsson, L., Koziel, S., Hermannsson, E., and Fakhraie, R. (2014) Trawl-Door Design Optimization by Local Surrogate Models. 55th AIAA/ ASMe/ASCE /AHS/SC Structures, Structural Dynamics, and Materials Conference, National Harbor, Maryland, Jan. 13–17.
Leifsson, L., Koziel, S., Kurgan, P. (2014) Automated low-fidelity model setup for surrogate-based aerodynamic optimization. In S. Koziel, L. Leifsson, and X.S. Yang (Eds.) Solving Computationally Extensive Engineering Problems: Methods and Applications, Springer, pp. 87–112.
Leifsson, L., Koziel, S., Hosder, S., and Riggins, D.W. (2014b) Physics-based Multi-fidelity Surrogate Modeling with Entropy-based Availability Method. AIAA Modeling and Simulation Technologies Conference, National Harbor, Maryland, Jan. 13–17.
Lim, D., Jin, Y., Ong, Y., Sendhoff, B. (2010) Generalizing surrogate-assisted evolutionary computation. IEEE Trans. Evol. Comp., 14, pp. 329–355.
Nocedal, J., Wright, S.J. (2000) Numerical Optimization, Springer Series in Operations Research, Springer.
Palacios, F., Colonno, M.R., Aranake, A.C., Campos, A., Copeland, S.R., Economon, T.D., Lonkar, A.K., Lukaczyk, T.W., Taylor, T.W.R., Alonso, J.J. (2013) Stanford University unstructures (SU2): an open-source integrated computational environment for multi-physics simulation and design. In 51th AIAA Aerospace Sciences Meeting, Grapevine, TX, USA.
Papadimitriou, D.I., and Giannakoglou, K.C., (2008) Aerodynamic shape optimization using first and second order adjoint and direct approaches. Arch. Comput. Methods Eng., 15, 447–488.
Pironneau, O. (1984) Optimal Shape Design for Elliptic Systems. Springer-Verlag, New York.
Queipo, N.V., Haftka, R.T., Shyy, W., Goel, T., Vaidynathan, R., Tucker, P.K. (2005) Surrogate-based analysis and optimization. Progress in Aerospace Sciences, 41, pp. 1–28.
Shaker, G.S.A., Bakr, M.H., Sangary, N., Safavi-Naeini, S. (2009) Accelerated antenna design methodology exploiting parameterized Cauchy models. J. Progress in Electromagnetic Research (PIER B), 18, pp. 279–309.
Simpson, T.W., Maurey, T.M., Korte, J.J., and Mistree, F. (2001) Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA Journal, vol. 39, no. 12, pp. 2233–2241.
Simpson, T.W., Peplinski, J., Koch, P.N., Allen, J.K. (2001) Metamodels for computer-based engineering design: survey and recommendations. Engineering with Computers, 17, pp. 129−150.
Smola, A.J., Schölkopf, B. (2004) A tutorial on support vector regression. Statistics and Computing, 14, pp. 199−222.
Sobester, A., Forrester, A.I.J. (2015) Aircraft aerodynamic design: geometry and optimization. John Wiley & Sons.
Star-CCM+ (2015) CD-adapco Group, 60 Broadhollow Road, Melville, NY 11747, USA.
Styblinski, M.A., Oplaski, L.J. (1986) Algorithms and software tools for IC yield optimization based on fundamental fabrication parameters. IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., 5, pp. 79–89.
Toropov, V.V. (1989) Simulation approach to structural optimization. Structural Optimization, 1, pp. 37–46.
Wild, S.M., Regis, R.G., Shoemaker, C.A. (2008) ORBIT: Optimization by radial basis function interpolation in trust-regions. SIAM J. Sci. Comput., 30, pp. 3197−3219.
Yang, X.S. (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley.
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Koziel, S., Leifsson, L. (2016). Introduction. In: Simulation-Driven Design by Knowledge-Based Response Correction Techniques. Springer, Cham. https://doi.org/10.1007/978-3-319-30115-0_1
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