By Slawomir Koziel, Leifur Leifsson
Contemporary engineering layout is seriously according to laptop simulations. actual, high-fidelity simulations are used not just for layout verification yet, much more importantly, to regulate parameters of the approach to have it meet given functionality necessities. regrettably, exact simulations are frequently computationally very pricey with assessment instances so long as hours or perhaps days in line with layout, making layout automation utilizing traditional tools impractical. those and different difficulties could be alleviated by means of the improvement and employment of so-called surrogates that reliably signify the pricy, simulation-based version of the method or equipment of curiosity yet they're much extra moderate and analytically tractable.
This quantity positive factors surrogate-based modeling and optimization recommendations, and their functions for fixing tough and computationally pricey engineering layout difficulties. It starts off via featuring the fundamental techniques and formulations of the surrogate-based modeling and optimization paradigm after which discusses proper modeling innovations, optimization algorithms and layout methods, in addition to cutting-edge advancements. The chapters are self-contained with easy ideas and formulations besides functions and examples. The e-book can be necessary to researchers in engineering and arithmetic, particularly those that hire computationally heavy simulations of their layout work.
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Extra resources for Surrogate-Based Modeling and Optimization: Applications in Engineering
Aerosp. Sci. 41, 1–28 (2005) 6. : Recent advances in surrogate-based optimization. Prog. Aerosp. Sci. 45, 50–79 (2009) 7. : Surrogate-based methods. S. ) Computational Optimization, Methods and Algorithms. Studies in Computational Intelligence, pp. 33–60. Springer, Berlin (2011) 8. : Space mapping: the state of the art. IEEE Trans. Microw. Theory Tech. 52, 337– 361 (2004) 9. : Rapid design optimization of antennas using space mapping and response surface approximation models. Int. J. RF Microw.
Ahmed El-Sharabasy, Faculty of Engineering, Cairo University, which have been reviewed in this chapter. References 1. : Yield optimization for arbitrary statistical distributions: part I—theory. IEEE Trans. Circuits Syst. 27, 245–253 (1980) 2. : The ellipsoidal technique for design centering and region approximation. IEEE Trans. -Aided Des. 10, 1006–1014 (1991) 3. : Statistical circuit design with the use of a modified ellipsoidal technique. Int. J. Microw. -Aided Eng. 7, 117– 129 (1997) 4. : A boundary gradient search technique and its application in design centering.
There are 11 performance mea- Surrogate-Based Circuit Design Centering 35 sures of interest given in Table 1. In this table, the area is defined as the sum of the product of the widths and lengths of all the designable transistors. Table 1 also shows the specification bounds of the performance measures, and their values at the initial and final design parameters. 8. 2 %. 6 % by using 246 yield evaluations. 06]T at which the yield is 93 % by using 259 yield evaluations. A comparison between the stratified sampling and the Monte Carlo method showed that the standard deviation of the stratified sampling yield estimator by using 1,000 samples is almost upper bounded by that of the Monte Carlo estimator by using 1,800 samples, which saves about 44 % of the circuit simulations .
Surrogate-Based Modeling and Optimization: Applications in Engineering by Slawomir Koziel, Leifur Leifsson