S.A. Dakroury

Bio: S.A. Dakroury is an academic researcher from McMaster University. The author has contributed to research in topics: Space mapping. The author has an hindex of 2, co-authored 3 publications receiving 986 citations.

Space mapping: the state of the art

Abstract: We review the space-mapping (SM) technique and the SM-based surrogate (modeling) concept and their applications in engineering design optimization. For the first time, we present a mathematical motivation and place SM into the context of classical optimization. The aim of SM is to achieve a satisfactory solution with a minimal number of computationally expensive "fine" model evaluations. SM procedures iteratively update and optimize surrogates based on a fast physically based "coarse" model. Proposed approaches to SM-based optimization include the original algorithm, the Broyden-based aggressive SM algorithm, various trust-region approaches, neural SM, and implicit SM. Parameter extraction is an essential SM subproblem. It is used to align the surrogate (enhanced coarse model) with the fine model. Different approaches to enhance uniqueness are suggested, including the recent gradient parameter-extraction approach. Novel physical illustrations are presented, including the cheese-cutting and wedge-cutting problems. Significant practical applications are reviewed.

Space mapping interpolating surrogates for highly optimized EM-based design of microwave devices

Abstract: We present a powerful optimization algorithm that incorporates space mapping (SM) and the new output space mapping (OSM) to yield highly optimized results in a handful of fine model evaluations. Our new method employs an SM-based interpolating surrogate (SMIS) framework that aims at matching the surrogate with the fine model locally. Accuracy and convergence properties are demonstrated using a seven-section capacitively-loaded impedance transformer. A highly optimized six-section H-plane waveguide filter design emerges after only four HFSS EM simulations, excluding necessary Jacobian estimations, using the new algorithm with sparse frequency sweeps.

Space mapping: the state of the art [circuit simulation, CAD]

Abstract: We review the space mapping (SM) technique and the SM-based surrogate (modeling) concept and their applications in engineering design optimization. The aim of SM is to achieve a satisfactory solution with a minimal number of computationally expensive "fine" model evaluations. SM procedures iteratively update and optimize surrogates based on a fast physically-based "coarse" model. Parameter extraction is an essential SM subproblem. It is used to align the surrogate (enhanced coarse model) with the fine model. Novel physical illustrations are presented, including the cheese cutting problem and wedge cutting problem. Significant practical applications are reviewed.

Review of surrogate modeling in water resources

Abstract: [1] Surrogate modeling, also called metamodeling, has evolved and been extensively used over the past decades. A wide variety of methods and tools have been introduced for surrogate modeling aiming to develop and utilize computationally more efficient surrogates of high-fidelity models mostly in optimization frameworks. This paper reviews, analyzes, and categorizes research efforts on surrogate modeling and applications with an emphasis on the research accomplished in the water resources field. The review analyzes 48 references on surrogate modeling arising from water resources and also screens out more than 100 references from the broader research community. Two broad families of surrogates namely response surface surrogates, which are statistical or empirical data-driven models emulating the high-fidelity model responses, and lower-fidelity physically based surrogates, which are simplified models of the original system, are detailed in this paper. Taxonomies on surrogate modeling frameworks, practical details, advances, challenges, and limitations are outlined. Important observations and some guidance for surrogate modeling decisions are provided along with a list of important future research directions that would benefit the common sampling and search (optimization) analyses found in water resources.

Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions

Abstract: The integration of optimization methodologies with computational analyses/simulations has a profound impact on the product design. Such integration, however, faces multiple challenges. The most eminent challenges arise from high-dimensionality of problems, computationally-expensive analysis/simulation, and unknown function properties (i.e., black-box functions). The merger of these three challenges severely aggravates the difficulty and becomes a major hurdle for design optimization. This paper provides a survey on related modeling and optimization strategies that may help to solve High-dimensional, Expensive (computationally), Black-box (HEB) problems. The survey screens out 207 references including multiple historical reviews on relevant subjects from more than 1,000 papers in a variety of disciplines. This survey has been performed in three areas: strategies tackling high-dimensionality of problems, model approximation techniques, and direct optimization strategies for computationally-expensive black-box functions and promising ideas behind non-gradient optimization algorithms. Major contributions in each area are discussed and presented in an organized manner. The survey exposes that direct modeling and optimization strategies to address HEB problems are scarce and sporadic, partially due to the difficulty of the problem itself. Moreover, it is revealed that current modeling research tends to focus on sampling and modeling techniques themselves and neglect studying and taking the advantages of characteristics of the underlying expensive functions. Based on the survey results, two promising approaches are identified to solve HEB problems. Directions for future research are also discussed.

Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come?

Abstract: The use of metamodeling techniques in the design and analysis of computer experiments has progressed remarkably in the past 25 years, but how far has the field really come? This is the question addressed in this paper, namely, the extent to which the use of metamodeling techniques in multidisciplinary design optimization have evolved in the 25 years since the seminal paper on design and analysis of computer experiments by Sacks et al. (“Design and Analysis of Computer Experiments,” Statistical Science, Vol. 4, No. 4, 1989, pp. 409–435). Rather than a technical review of the entire body of metamodeling literature, the focus is on the evolution and motivation for advancements in metamodeling with some discussion on the research itself; not surprisingly, much of the current research motivation is the same as it was in the past. Based on current research thrusts in the field, multifidelity approximations and ensembles (i.e., sets) of metamodels, as well as the availability of metamodels within commercial soft...

Space mapping

Abstract: In this article we review state-of-the-art concepts of space mapping and place them con- textually into the history of design optimization and modeling of microwave circuits. We formulate a generic space-mapping optimization algorithm, explain it step-by-step using a simple microstrip filter example, and then demonstrate its robustness through the fast design of an interdigital filter. Selected topics of space mapping are discussed, including implicit space mapping, gradient-based space mapping, the optimal choice of surrogate model, and tuning space mapping. We consider the application of space mapping to the modeling of microwave structures. We also discuss a software package for automated space-mapping optimization that involves both electromagnetic (EM) and circuit simulators.