Abstract
Computer simulation models are fundamental tools of contemporary engineering design. The components, structures, and systems considered in most engineering disciplines are far too complex to be accurately described using simple theoretical models. Therefore, numerical simulation is often the only way to evaluate the performance of the design with sufficient reliability. However, accurate, high-fidelity simulations are computationally expensive. Consequently, their use for design automation, especially when exploiting conventional optimization algorithms is often prohibitive. Availability of faster computers and more efficient simulation software does not always translate into computational speedup due to growing demand for improved accuracy and the need to evaluate larger and larger systems. Surrogate-based optimization (SBO) techniques belong to the most promising approaches capable of alleviating these difficulties. SBO allows for reducing the number of expensive objective function evaluations in a simulation-driven design process. This is obtained by replacing the direct optimization of the expensive model by iterative updating and re-optimization of its cheap surrogate model. Among proven SBO techniques, the methods exploiting physics-based low-fidelity models are probably the most efficient. This is because the knowledge about the system of interest embedded in the low-fidelity model allows constructing the surrogate model that has good generalization capability at a cost of just a few evaluations of the original model. This chapter reviews one of the most recent SBO techniques, the so-called shape-preserving response prediction (SPRP). We discuss the formulation of SPRP, its limitations, and generalizations, and, most importantly, demonstrate its applications to solve design problems in various engineering areas, including microwave engineering, antenna design, and aerodynamic shape optimization. We also discuss the use of SPRP for creating fast surrogate models with illustrations from the microwave engineering area.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Nocedal, J., Wright, S.: Numerical Optimization, 2nd edn. Springer, New York (2006)
Conn, A.R., Scheinberg, K., Vicente, L.N.: Introduction to Derivative-Free Optimization. MPS-SIAM Series on Optimization, MPS-SIAM (2009)
El Sabbagh, M.A., Bakr, M.H., Nikolova, N.K.: Sensitivity analysis of the scattering parameters of microwave filters using the adjoint network method. Int. J. RF Microw. Comput. Aided Eng. 16, 596–606 (2006)
Koziel, S., Echeverría-Ciaurri, D., Leifsson, L.: Surrogate-based methods. In: Koziel, S., Yang, X.S. (eds.) Computational Optimization, Methods and Algorithms, Series: Studies in Computational Intelligence, pp. 33–60. Springer, New York (2011)
Forrester, A.I.J., Keane, A.J.: Recent advances in surrogate-based optimization. Prog. Aerosp. Sci. 45, 50–79 (2009)
Simpson, T.W., Peplinski, J., Koch, P.N., Allen, J.K.: Metamodels for computer-based engineering design: survey and recommendations. Eng. Comput. 17, 129–150 (2001)
Queipo, N.V., Haftka, R.T., Shyy, W., Goel, T., Vaidynathan, R., Tucker, P.K.: Surrogate-based analysis and optimization. Prog. Aerosp. Sci. 41, 1–28 (2005)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004)
Rayas-Sánchez, J.E.: EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art. IEEE Trans. Microw. Theory Tech. 52, 420–435 (2004)
Alexandrov, N.M., Dennis, J.E., Lewis, R.M., Torczon, V.: A trust region framework for managing use of approximation models in optimization. Struct. Multidiscip. Optim. 15(1), 16–23 (1998)
Cheng, Q.S., Bandler, J.W., Koziel, S., Bakr, M.H., Ogurtsov, S.: The state of the art of microwave CAD: EM-based optimization and modeling. Int. J. RF Microw. Comput. Aided Eng. 20, 475–491 (2010)
Echeverria, D., Hemker, P.W.: Space mapping and defect correction. CMAM Int. Math. J. Comput. Methods Appl. Math. 5(2), 107–136 (2005)
Rautio, J.C.: Perfectly calibrated internal ports in EM analysis of planar circuits. In: IEEE MTT-S Int. Microwave Symp. Dig., Atlanta, pp. 1373–1376 (2008)
Conn, A.R., Gould, N.I.M., Toint, P.L.: Trust Region Methods, MPS-SIAM Series on Optimization (2000)
Koziel, S.: Shape-preserving response prediction for microwave design optimization. IEEE Trans. Microw. Theory Tech. 58, 2829–2837 (2010)
Koziel, S., Ogurtsov, S., Szczepanski, S.: Rapid antenna design optimization using shape-preserving response prediction. Bull. Pol. Acad. Sci. Technical Sci. 60, 143–149 (2012)
Koziel, S., Leifsson, L.: Transonic airfoil shape optimization using variable-resolution models and pressure distribution alignment. In: AIAA Applied Aerodynamic Conference, Honolulu, 27–30 June 2011, AIAA-2011-3177
Bandler, J.W., Cheng, Q.S., Dakroury, S.A., Mohamed, A.S., Bakr, M.H., Madsen, K., Sondergaard, J.: Space mapping: the state of the art. IEEE Trans. Microw. Theory Tech. 52, 337–361 (2004)
Koziel, S., Bandler, J.W., Cheng, Q.S.: Robust trust-region space-mapping algorithms for microwave design optimization. IEEE Trans. Microw. Theory Tech. 58, 2166–2174 (2010)
Booker, A.J., Dennis Jr., J.E., Frank, P.D., Serafini, D.B., Torczon, V., Trosset, M.W.: A rigorous framework for optimization of expensive functions by surrogates. Struct. Optim. 17, 1–13 (1999)
Guan, X., Ma, Z., Cai, P., Anada, T., Hagiwara, G.: A microstrip dual-band bandpass filter with reduced size and improved stopband characteristics. Microw. Opt. Tech. Lett. 50, 618–620 (2008)
em TM Version 12.54, Sonnet Software, Inc., 100 Elwood Davis Road, North Syracuse, NY 13212, USA, 2010
Agilent ADS, Version 2011, Agilent Technologies, 1400 Fountaingrove Parkway, Santa Rosa, CA 95403–1799 (2011)
Chen, Z.N.: Wideband microstrip antennas with sandwich substrate. IET Microw. Ant. Prop. 2, 538–546 (2008)
CST Microwave Studio, CST AG, Bad Nauheimer Str. 19, D-64289 Darmstadt, Germany (2011)
Abbott, I.H., Von Doenhoff, A.E.: Theory of Wing Sections. Dover Publications, New York (1959)
ICEM CFD, ver. 14, ANSYS Inc., Southpointe, 275 Technology Drive, Canonsburg, PA 15317 (2011)
FLUENT, ver. 14, ANSYS Inc., Southpointe, 275 Technology Drive, Canonsburg, PA 15317 (2011)
Kuo, J.T., Chen, S.P., Jiang, M.: Parallel-coupled microstrip filters with over-coupled end stages for suppression of spurious responses. IEEE Microw. Wirel. Comput. Lett. 13, 440–442 (2003)
RT/duroid® 5870/5880 High Frequency Laminates, Data Sheet, Rogers Corporation, Publication #92-101 (2010)
Koziel, S., Leifsson, L.: Generalized shape-preserving response prediction for accurate modeling of microwave structures. IET Microw. Ant. Prop. 6, 1332–1339 (2012)
Koziel, S.: Shape-preserving response prediction for microwave circuit modeling. In: IEEE MTT-S Int. Microw. Symp. Dig, Anaheim, pp. 1660–1663 (2010)
Bandler, J.W., Cheng, Q.S., Koziel, S.: Simplified space mapping approach to enhancement of microwave device models. Int. J. RF Microw. Comput. Aided Eng. 16, 518–535 (2006)
Koziel, S., Szczepanski, S.: Accurate modeling of microwave structures using shape-preserving response prediction. IET Microw. Antennas Propag. 5, 1116–1122 (2011)
Salleh, M.K.M., Pringent, G., Pigaglio, O., Crampagne, R.: Quarter-wavelength side-coupled ring resonator for bandpass filters. IEEE Trans. Microw. Theory Tech. 56, 156–162 (2008)
FEKO® User’s Manual, Suite 5.4, 2008, EM Software & Systems-S.A. (Pty) Ltd., 32 Techno Lane, Technopark, Stellenbosch, 7600, South Africa
Koziel, S., Ogurtsov, S., Cheng, Q.S., Bandler, J.W.: Rapid electromagnetic-based microwave design optimisation exploiting shape-preserving response prediction and adjoint sensititivies. IET Microwaves Antennas Prop. 8, 775--781 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Koziel, S., Leifsson, L. (2014). Shape-Preserving Response Prediction for Surrogate Modeling and Engineering Design Optimization. In: Koziel, S., Leifsson, L., Yang, XS. (eds) Solving Computationally Expensive Engineering Problems. Springer Proceedings in Mathematics & Statistics, vol 97. Springer, Cham. https://doi.org/10.1007/978-3-319-08985-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-08985-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08984-3
Online ISBN: 978-3-319-08985-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)