Frank Pedersen

Bio: Frank Pedersen is an academic researcher from McMaster University. The author has contributed to research in topics: Space mapping & Minimax. The author has an hindex of 6, co-authored 12 publications receiving 201 citations. Previous affiliations of Frank Pedersen include Aalborg University.

EM-based surrogate modeling and design exploiting implicit, frequency and output space mappings

Abstract: We present a significant improvement to the novel implicit space mapping (ISM) concept for EM-based microwave modeling and design. ISM calibrates a suitable coarse (surrogate) model against a fine model (full-wave EM simulation) by relaxing certain coarse model preassigned parameters. Based on an explanation of residual response misalignment, our new approach further fine-tunes the surrogate by exploiting an "output space" mapping (OSM). An accurate design of an HTS filter, easily implemented in Agilent ADS, emerges after only four EM simulations using ISM and OSM with sparse frequency sweeps. For the first time also, frequency space mapping is implemented in an ISM framework.

A space-mapping interpolating surrogate algorithm for highly optimized EM-based design of microwave devices

Abstract: We justify and elaborate in detail on a powerful new optimization algorithm that combines space mapping (SM) with a novel output SM. In a handful of fine-model evaluations, it delivers for the first time the accuracy expected from classical direct optimization using sequential linear programming. Our new method employs a space-mapping-based interpolating surrogate (SMIS) framework that aims at locally matching the surrogate with the fine model. Accuracy and convergence properties are demonstrated using a seven-section capacitively loaded impedance transformer. In comparing our algorithm with major minimax optimization algorithms, the SMIS algorithm yields the same minimax solution within an error of 10/sup -15/ as the Hald-Madsen algorithm. A highly optimized six-section H-plane waveguide filter design emerges after only four HFSS electromagnetic simulations, excluding necessary Jacobian estimations, using our algorithm with sparse frequency sweeps.

A method for optimizing the performance of buildings

Abstract: A method for optimizing the performance of buildings This thesis describes a method for optimizing the performance of buildings. Design decisions made in early stages of the building design process have a significant impact on the performance of buildings, for instance, the performance with respect to the energy consumption, economical aspects, and the indoor environment. The method is intended for supporting design decisions for buildings, by combining methods for calculating the performance of buildings with numerical optimization methods. The method is able to find optimum values of decision variables representing different features of the building, such as its shape, the amount and type of windows used, and the amount of insulation used in the building envelope. The parties who influence design decisions for buildings, such as building owners, building users, architects, consulting engineers, contractors, etc., often have different and to some extent conflicting requirements to buildings. For instance, the building owner may be more concerned about the cost of constructing the building, rather than the quality of the indoor climate, which is more likely to be a concern of the building user. In order to support the different types of requirements made by decision-makers for buildings, an optimization problem is formulated, intended for representing a wide range of design decision problems for buildings. The problem formulation involves so-called performance measures, which can be calculated with simulation software for buildings. For instance, the annual amount of energy required by the building, the cost of constructing the building, and the annual number of hours where overheating occurs, can be used as performance measures. The optimization problem enables the decision-makers to specify many different requirements to the decision variables, as well as to the performance of the building. Performance measures can for instance be required to assume their minimum or maximum value, they can be subjected to upper or lower bounds, or they can be required to assume certain values. The optimization problem makes it possible to optimize virtually any aspect of the building performance; however, the primary focus of this study is on energy consumption, economy, and indoor environment. The performance measures regarding the energy and indoor environment are calculated using existing simulation software, with minor modifications. The cost of constructing the building is calculating using unit prices for construction jobs, which can be found in price catalogues. Simple algebraic expressions are used as models for these prices. The model parameters are found by using data-fitting. In order to solve the optimization problem formulated earlier, a gradient-free sequential quadratic programming (SQP) filter algorithm is proposed. The algorithm does not require information about the first partial derivatives of the functions that define the optimization problem. This means that techniques such as using finite difference approximations can be avoided, which reduces the time needed for solving the optimization problem. Furthermore, the algorithm uses so-called domain constraint functions in order to ensure that the input to the simulation software is feasible. Using this technique avoids performing time-consuming simulations for unrealistic design decisions. The algorithm is evaluated by applying it to a set of test problems with known solutions. The results indicate that the algorithm converges fast and in a stable manner, as long as there are no active domain constraints. In this case, convergence is either deteriorated or prevented. This case is described in the thesis. The proposed building optimization method uses the gradient-free SQP filter algorithm in order to solve the formulated optimization problem, which involves performance measures that are calculated using simulation software for buildings. The method is tested by applying it to a building design problem involving an office building. The results indicate that the method is able to find design decisions that satisfy all requirements to the decision variables and performance measures. Furthermore, the time needed by the algorithm for solving the optimization problem is acceptable. There are still a number of unresolved issues regarding the building optimization method, which are suggested as further research in the field of building optimization methods. Two papers are included in Appendix concerning so-called space mapping algorithms. These algorithms are relevant for developing fast and reliable building optimization methods.

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.

Modeling thermally active building components using space mapping

Abstract: In order to efficiently implement thermally active building components in new buildings, it is necessary to evaluate the thermal interaction between them and other building components. Applying parameter investigation or numerical optimization methods to a differential-algebraic (DAE) model of a building provides a systematic way of estimating efficient building designs. However, using detailed numerical calculations of the components in the building is a time consuming process, which may become prohibitive if the DAE model is to be used for parameter variation or optimization. Unfortunately simplified models of the components do not always provide useful solutions, since they are not always able to reproduce the correct thermal behavior. The space mapping technique transforms a simplified, but computationally inexpensive model, in order to align it with a detailed model or measurements. This paper describes the principle of the space mapping technique, and introduces a simple space mapping technique. The technique is applied to a lumped parameter model of a thermo active component, which provides a model of the thermal performance of the component as a function of two design parameters. The technique significantly reduces the modeling error.

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

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.

EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art

Abstract: This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments, and clusterization. Innovative strategies for ANN-based design exploiting microwave knowledge are reviewed, including neural space-mapping methods. The problem of developing synthesis neural networks is treated. EM-based statistical analysis and yield optimization using neural networks is reviewed. The key issues in transient EM-based design using neural networks are summarized. The use of ANNs to speed up "global modeling" for EM-based design of monolithic microwave integrated circuits is briefly described. Future directions in ANN techniques to microwave design are suggested.