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Simulation-Driven Modeling and Optimization: ASDOM, Reykjavik, August 2014

✍ Scribed by Slawomir Koziel, Leifur Leifsson, Xin-She Yang (eds.)


Publisher
Springer International Publishing
Year
2016
Tongue
English
Leaves
405
Series
Springer Proceedings in Mathematics & Statistics 153
Edition
1
Category
Library

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✦ Synopsis


This edited volume is devoted to the now-ubiquitous use of computational models across most disciplines of engineering and science, led by a trio of world-renowned researchers in the field. Focused on recent advances of modeling and optimization techniques aimed at handling computationally-expensive engineering problems involving simulation models, this book will be an invaluable resource for specialists (engineers, researchers, graduate students) working in areas as diverse as electrical engineering, mechanical and structural engineering, civil engineering, industrial engineering, hydrodynamics, aerospace engineering, microwave and antenna engineering, ocean science and climate modeling, and the automotive industry, where design processes are heavily based on CPU-heavy computer simulations. Various techniques, such as knowledge-based optimization, adjoint sensitivity techniques, and fast replacement models (to name just a few) are explored in-depth along with an array of the latest techniques to optimize the efficiency of the simulation-driven design process.

High-fidelity simulation models allow for accurate evaluations of the devices and systems, which is critical in the design process, especially to avoid costly prototyping stages. Despite this and other advantages, the use of simulation tools in the design process is quite challenging due to associated high computational cost. The steady increase of available computational resources does not always translate into the shortening of the design cycle because of the growing demand for higher accuracy and necessity to simulate larger and more complex systems. For this reason, automated simulation-driven designβ€”while highly desirableβ€”is difficult when using conventional numerical optimization routines which normally require a large number of system simulations, each one already expensive.

✦ Table of Contents


Front Matter....Pages i-viii
Numerical Aspects of Model Order Reduction for Gas Transportation Networks....Pages 1-28
Parameter Studies for Energy Networks with Examples from Gas Transport....Pages 29-54
Fast Multi-Objective Aerodynamic Optimization Using Space-Mapping-Corrected Multi-Fidelity Models and Kriging Interpolation....Pages 55-73
Assessment of Inverse and Direct Methods for Airfoil and Wing Design....Pages 75-109
Performance Optimization of EBG-Based Common Mode Filters for Signal Integrity Applications....Pages 111-133
Unattended Design of Wideband Planar Filters Using a Two-Step Aggressive Space Mapping (ASM) Optimization Algorithm....Pages 135-159
Two-Stage Gaussian Process Modeling of Microwave Structures for Design Optimization....Pages 161-184
Efficient Reconfigurable Microstrip Patch Antenna Modeling Exploiting Knowledge Based Artificial Neural Networks....Pages 185-206
Expedited Simulation-Driven Multi-Objective Design Optimization of Quasi-Isotropic Dielectric Resonator Antenna....Pages 207-231
Optimal Design of Photonic Crystal Nanostructures....Pages 233-260
Design Optimization of LNAs and Reflectarray Antennas Using the Full-Wave Simulation-Based Artificial Intelligence Models with the Novel Metaheuristic Algorithms....Pages 261-298
Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives....Pages 299-323
Linear and Nonlinear System Identification Using Evolutionary Optimisation....Pages 325-345
A Surrogate-Model-Assisted Evolutionary Algorithm for Computationally Expensive Design Optimization Problems with Inequality Constraints....Pages 347-370
Sobol Indices for Dimension Adaptivity in Sparse Grids....Pages 371-395
Back Matter....Pages 397-404

✦ Subjects


Calculus of Variations and Optimal Control; Optimization; Mathematical Modeling and Industrial Mathematics; Computational Science and Engineering


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