Simulation-Driven Modeling and Optimization

ASDOM, Reykjavik, August 2014

  • Slawomir Koziel
  • Leifur Leifsson
  • Xin-She Yang
Conference proceedings

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 153)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Sara Grundel, Nils Hornung, Sarah Roggendorf
    Pages 1-28
  3. Leifur Leifsson, Slawomir Koziel, Yonatan Tesfahunegn, Adrian Bekasiewicz
    Pages 55-73
  4. Mengmeng Zhang, Arthur William Rizzi
    Pages 75-109
  5. Carlo Olivieri, Francesco de Paulis, Antonio Orlandi, Slawomir Koziel
    Pages 111-133
  6. Marc Sans, Jordi Selga, Ana Rodríguez, Paris Vélez, Vicente E. Boria, Jordi Bonache et al.
    Pages 135-159
  7. Adrian Bekasiewicz, Slawomir Koziel, Wlodzimierz Zieniutycz, Leifur Leifsson
    Pages 207-231
  8. Abdel-Karim S. O. Hassan, Nadia H. Rafat, Ahmed S. A. Mohamed
    Pages 233-260
  9. K. Worden, I. Antoniadou, O. D. Tiboaca, G. Manson, R. J. Barthorpe
    Pages 325-345
  10. Richard P. Dwight, Stijn G. L. Desmedt, Pejman Shoeibi Omrani
    Pages 371-395
  11. Back Matter
    Pages 397-404

About these proceedings


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 design—while highly desirable—is difficult when using conventional numerical optimization routines which normally require a large number of system simulations, each one already expensive.


computational modeling computer simulation engineering optimization numerical optimization simulating complex systems computationally expensive simulation iterative simulation design processes surrogate-based modelling simulation-driven design optimization metaheuristic optimization efficient optimization algorithms aerodynamic optimization gas transport network design antenna design microwave structure design crystal nanostructures algorithms modeling optimization

Editors and affiliations

  • Slawomir Koziel
    • 1
  • Leifur Leifsson
    • 2
  • Xin-She Yang
    • 3
  1. 1.Engin. Optimization & Modeling CentReykjavik UniversityReykjavikIceland
  2. 2.College of EngineeringIowa State UniversityAmesUSA
  3. 3.School of Science and TechnologyMiddlesex UniversityLondonUnited Kingdom

Bibliographic information