Leifur Leifsson

Bio: Leifur Leifsson is an academic researcher from Iowa State University. The author has contributed to research in topics: Surrogate model & Airfoil. The author has an hindex of 22, co-authored 179 publications receiving 1894 citations. Previous affiliations of Leifur Leifsson include Ghent University & Reykjavík University.

Surrogate-Based Methods

Abstract: Objective functions that appear in engineering practice may come from measurements of physical systems and, more often, from computer simulations. In many cases, optimization of such objectives in a straightforward way, i.e., by applying optimization routines directly to these functions, is impractical. One reason is that simulation-based objective functions are often analytically intractable (discontinuous, non-differentiable, and inherently noisy). Also, sensitivity information is usually unavailable, or too expensive to compute. Another, and in many cases even more important, reason is the high computational cost of measurement/simulations. Simulation times of several hours, days or even weeks per objective function evaluation are not uncommon in contemporary engineering, despite the increase of available computing power. Feasible handling of these unmanageable functions can be accomplished using surrogate models: the optimization of the original objective is replaced by iterative re-optimization and updating of the analytically tractable and computationally cheap surrogate. This chapter briefly describes the basics of surrogate-based optimization, various ways of creating surrogate models, as well as several examples of surrogate-based optimization techniques.

Surrogate-Based Aerodynamic Shape Optimization by Variable-Resolution Models

Abstract: A surrogate-based optimization algorithm for transonic airfoil design is presented. The approach replaces the direct optimization of an accurate, but computationally expensive, high-fidelity computational fluid dynamics model by an iterative reoptimization of a physics-based surrogate model. The surrogate model is constructed, during each design iteration, using the low-fidelity model and the data obtained from one high-fidelity model evaluation. The low-fidelity model is based on the same governing fluid flow equations as the high-fidelity one, but uses coarser mesh resolution and relaxed convergence criteria. The shape-preserving response prediction technique is utilized to predict the high-fidelity model response, here, the airfoil pressure distribution. In this prediction process, the shape-preserving response prediction employs the actual changes of the low-fidelity model response due to the design variable adjustments. The shape-preserving response prediction algorithm is embedded into the trust reg...

Surrogate-based modeling and optimization : applications in engineering

Abstract: Space Mapping for Electromagnetic-Simulation-Driven Design Optimization, Slawomir Koziel, Leifur Leifsson, and Stanislav Ogurtsov.- Surrogate-Based Circuit Design Centering, Abdel-Karim S.O. Hassan and Ahmed S.A. Mohamed.- Simulation-Driven Antenna Design Using Surrogate-Based Optimization, Slawomir Koziel, Stanislav Ogurtsov, and Leifur Leifsson.- Practical Application of Space Mapping Techniques to the Synthesis of CSRR-based Artificial Transmission Lines, Ana Rodriguez, Jordi Selga, Ferran Martin and Vicente E. Boria.- The Efficiency of Difference Mapping on Space Mapping Based Optimization, Murat Simsek, Neslihan Serap Sengor.- Bayesian Support Vector Regression Modeling of Microwave Structures for Design Applications, J. Pieter Jacobs, Slawomir Koziel, Leifur Leifsson.- Artificial Neural Networks and Space Mapping For EM-Based Modelling and Design of Microwave Circuits, Jose Ernesto Rayas-Sanchez.- Model-Based Variation-Aware Integrated Circuit Design, Ting Zhu, Mustafa Berke Yelten, Michael B. Steer, and Paul D. Franzon.- Computing Surrogates for Gas Network Simulation using Model Order Reduction, Sara Grundel, Nils Hornung, Bernhard Klaassen, Peter Benner, and Tanja Clees.- Aerodynamic Shape Optimization by Space Mapping, Leifur Leifsson, Slawomir Koziel, Eirikur Jonsson, Stanislav Ogurtsov.- Efficient Robust Design with Stochastic Expansions, Yi Zhang, and Serhat Hosder.- Surrogate Models for Aerodynamic Shape Optimisation, Selvakumar Ulaganathan, and Nikolaos Asproulis.- Knowledge-Based Surrogate Modeling in Engineering Design Optimization, Qian Xu, Erich Wehrle, Horst Baier.- Switching Response Surface Models for Structural Health Monitoring of Bridges, Keith Worden, Elizabeth J. Cross, and James M.W. Brownjohn.- Surrogate Modeling of Stability Constraints for Optimization of Composite Structures.- S. Grihon, E. Burnaev, M. Belyaev, P. Prikhodko.- Engineering Optimization and Industrial Applications, Xin-She Yang.

Introduction to the Finite Element Method

Abstract: This chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Bat algorithm: a novel approach for global engineering optimization

Abstract: – Nature‐inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks., – The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature., – BA has been carefully implemented and carried out optimization for eight well‐known optimization tasks; then a comparison has been made between the proposed algorithm and other existing algorithms., – The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail.

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

Abstract: In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs...

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Abstract: Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.