Stella M. Clarke

Bio: Stella M. Clarke is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: The Internet & Web application. The author has an hindex of 1, co-authored 2 publications receiving 461 citations.

Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses

Abstract: A variety of metamodeling techniques have been developed in the past decade to reduce the computational expense of computer-based analysis and simulation codes. Metamodeling is the process of building a model of a model to provide a fast surrogate for a computationally expensive computer code. Common metamadeling techniques include response surface methodology, kriging, radial basis functions, and multivariate adaptive regression splines. In this paper, we investigate support vector regression (SVR) as an alternative technique for approximating complex engineering analyses. The computationally efficient theory behind SVR is reviewed, and SVR approximations are compared against the aforementioned four mefamodeling techniques using a test bed of 26 engineering analysis functions. SVR achieves more accurate and more robust function approximations than the four metamodeling techniques, and shows great potential for metamodeling applications, adding to the growing body of promising empirical performance of SVR.

A Web-Based Information Management System for Bus Test Data for the Pennsylvania Transportation Institute

Abstract: Computer-based information management systems have allowed many companies and facilities to greatly improve their data storage and processing capabilities over traditional paper and file methods. The Internet and its associated web-based technologies have further contributed to information processing capabilities. The Federal Transit Administration’s Bus Testing Program, operated by the Pennsylvania Transportation Institute at the Pennsylvania State University requires an upgrade to such a system. Currently, all bus test data is entered directly into a hardcopy report, and data analysis must be performed manually. The objective in this research is to develop an information management system for the bus test data to facilitate access to the data, enable search queries, and perform statistical analyses to examine current trends or predict performance characteristics of future buses. A database has been created in Microsoft Access and linked to a user-friendly graphical user interface developed in Visual Basic for fast and nearly error-free data entry. A web-based infrastructure comprised of HTML, XML, ASP, and customized COM objects is used to display, search, and analyze the bus test data. The resulting information system saves time and money for PTI while enabling easy access to bus test reports.Copyright © 2002 by ASME

Review of Metamodeling Techniques in Support of Engineering Design Optimization

Abstract: Computation-intensive design problems are becoming increasingly common in manufacturing industries. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. To address such a challenge, approximation or metamodeling techniques are often used. Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design optimization. This work reviews the state-of-the-art metamodel-based techniques from a practitioner’s perspective according to the role of metamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems. Challenges and future development of metamodeling in support of engineering design is also analyzed and discussed.Copyright © 2006 by ASME

Support Vector Regression

Abstract: Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields - time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.

State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments

Abstract: Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models. We discuss a toolkit of designs for simulators with limited DOE expertise who want to select a design and an appropriate analysis for their experiments. Furthermore, we provide a research agenda listing problems in the design of simulation experiments-as opposed to real-world experiments-that require more investigation. We consider three types of practical problems: (1) developing a basic understanding of a particular simulation model or system, (2) finding robust decisions or policies as opposed to so-called optimal solutions, and (3) comparing the merits of various decisions or policies. Our discussion emphasizes aspects that are typical for simulation, such as having many more factors than in real-world experiments, and the sequential nature of the data collection. Because the same problem type may be addressed through different design types, we discuss quality attributes of designs, such as the ease of design construction, the flexibility for analysis, and efficiency considerations. Moreover, the selection of the design type depends on the metamodel (response surface) that the analysts tentatively assume; for example, complicated metamodels require more simulation runs. We present several procedures to validate the metamodel estimated from a specific design, and we summarize a case study illustrating several of our major themes. We conclude with a discussion of areas that merit more work to achieve the potential benefits-either via new research or incorporation into standard simulation or statistical packages.

A User's Guide to the Brave New World of Designing Simulation Experiments

Abstract: The article of record as published may be located at http://dx.doi.org/10.1287/ijoc.1050.0136