Simulation-Driven Design by Knowledge-Based Response Correction Techniques
Published on 27. May 2018
Book
Paperback/Softback
XI, 262 pages
978-3-319-80726-3 (ISBN)
Description
Focused on efficient simulation-driven multi-fidelity optimization techniques, this monograph on simulation-driven optimization covers simulations utilizing physics-based low-fidelity models, often based on coarse-discretization simulations or other types of simplified physics representations, such as analytical models. The methods presented in the book exploit as much as possible any knowledge about the system or device of interest embedded in the low-fidelity model with the purpose of reducing the computational overhead of the design process. Most of the techniques described in the book are of response correction type and can be split into parametric (usually based on analytical formulas) and non-parametric, i.e., not based on analytical formulas. The latter, while more complex in implementation, tend to be more efficient.
The book presents a general formulation of response correction techniques as well as a number of specific methods, including those based on correcting the low-fidelity model response (output space mapping, manifold mapping, adaptive response correction and shape-preserving response prediction), as well as on suitable modification of design specifications. Detailed formulations, application examples and the discussion of advantages and disadvantages of these techniques are also included. The book demonstrates the use of the discussed techniques for solving real-world engineering design problems, including applications in microwave engineering, antenna design, and aero/hydrodynamics.
The book presents a general formulation of response correction techniques as well as a number of specific methods, including those based on correcting the low-fidelity model response (output space mapping, manifold mapping, adaptive response correction and shape-preserving response prediction), as well as on suitable modification of design specifications. Detailed formulations, application examples and the discussion of advantages and disadvantages of these techniques are also included. The book demonstrates the use of the discussed techniques for solving real-world engineering design problems, including applications in microwave engineering, antenna design, and aero/hydrodynamics.
More details
Edition
Softcover reprint of the original 1st ed. 2016
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
93 farbige Abbildungen, 74 s/w Abbildungen
XI, 262 p. 167 illus., 93 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
423 gr
ISBN-13
978-3-319-80726-3 (9783319807263)
DOI
10.1007/978-3-319-30115-0
Schweitzer Classification
Other editions
Additional editions
Slawomir Koziel | Leifur Leifsson
Simulation-Driven Design by Knowledge-Based Response Correction Techniques
Book
05/2016
Springer
€53.49
Shipment within 10-15 days
Persons
Anna Pietrenko-Dabrowska received the M.Sc. and Ph.D. degrees in electronic engineering from Gdansk University of Technology, Poland, in 1998 and 2007, respectively. Currently, she is an Associate Professor with Gdansk University of Technology, Poland. She is the Associate Editor of Int. J. Numerical Modeling, and Academic Editor of Int. J. Ant. Prop. She is also a guest co-editor of special issue of Int. J. Numerical Modeling (Advances in Forward and Inverse Surrogate Modeling for High-Frequency Design). She is a program committee member of international conferences (IEEE MTT-s Int. Conf. Num. EM and Multiphysics Modeling and Optim., NEMO 2019, Int. Conf. Comp. Science, ICCS 2019). Her research interests include simulation-driven design, design optimization, experiment design, control theory, modeling of microwave and antenna structures, numerical analysis. She is also a co-author of the book "Performance-driven surrogate modeling of high-frequency structures," (Springer, 2020).
Slawomir Koziel received the M.Sc. and Ph.D. degrees in electronic engineering from Gdansk University of Technology, Poland, in 1995 and 2000, respectively. He also received the M.Sc. degrees in theoretical physics and in mathematics, in 2000 and 2002, respectively, as well as the PhD in mathematics in 2003, from the University of Gdansk, Poland. He is currently a Professor with the Department of Engineering, Reykjavik University, Iceland. His research interests include CAD and modeling of microwave circuits, simulation-driven design, surrogate-based optimization, space mapping, circuit theory, evolutionary computation and numerical analysis. In recent years, he has been working extensively on surrogate-based modeling and optimization techniques as well as computationally efficient simulation-driven design methods for microwave engineering and aerospace engineering. He has published several book chapters and over 1,000 research papers. He is a founder and director of Engineering Optimization & Modeling Center at Reykjavik University.
Slawomir Koziel is a recipient of Fulbright Scholarship for the academic year 2003/2004. He has served on the Editorial Board of various international journals, program committee member as well as co-organizer of numerous special sessions and workshops at international conferences. He is an Associate Editor of several journals (IET Microwaves Ant. Prop., El. Lett., Int. J. Math. Modeling Num. Opt., Int. J. Numerical Modeling). He has also been a guest co-editor of several special issues of international journals (including Optimization and Engineering, Int. J. RF and Microwave CAE, Int. J. Math. Modelling and Num. Opt, IEEE Trans. Microwave Theory Techn.), as well as a co-author of several books, including "Performance-driven surrogate modeling of high-frequency structures," (Springer, 2020), "Simulation-based optimization of antenna arrays," (World Scientific, 2019), "Simulation-driven design by knowledge-based response correction techniques" (Springer, 2016), and "Antenna design by simulation-driven optimization" (Springer, 2014), and a co-editor of several other books.
Content
Introduction.- Simulation-Driven Design.- Fundamentals of Numerical Optimization.- Introduction to Surrogate-Based Modeling and Surrogate-Based Optimization.- Design Optimization Using Response Correction Techniques.- Surrogate-Based Optimization Using Parametric Response Correction.- Non-Parametric Response Correction Techniques.- Expedited Simulation-Driven Optimization Using Adaptively Adjusted Design Specification.- Surrogate-Assisted Design Optimization Using Response Features.- Enhancing Response Correction Techniques by Adjoint Sensitivity.- Multi-Objective Optimization Using Variable-Fidelity Models and Response Correction.- Physics-Base Surrogate Models Using Response Correction.- Summary and Discussion.- References.