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Research Papers

Model Selection Under Limited Information Using a Value-of-Information-Based Indicator

[+] Author and Article Information
Matthias Messer1

The Systems Realization Laboratory, The George W. Woodruff School of Mechanical Engineering, and The School of Electrical and Computer Engineering, Georgia Institute of Technology, Savannah, GA 31407

Jitesh H. Panchal2

The Systems Realization Laboratory, The George W. Woodruff School of Mechanical Engineering, and The School of Electrical and Computer Engineering, Georgia Institute of Technology, Savannah, GA 31407

Vivek Krishnamurthy, Benjamin Klein, P. Douglas Yoder

The Systems Realization Laboratory, The George W. Woodruff School of Mechanical Engineering, and The School of Electrical and Computer Engineering, Georgia Institute of Technology, Savannah, GA 31407

Janet K. Allen3 n4

The Systems Realization Laboratory, The George W. Woodruff School of Mechanical Engineering, and The School of Electrical and Computer Engineering, Georgia Institute of Technology, Savannah, GA 31407janet.allen@ou.edu

Farrokh Mistree4

The Systems Realization Laboratory, The George W. Woodruff School of Mechanical Engineering, and The School of Electrical and Computer Engineering, Georgia Institute of Technology, Savannah, GA 31407

1

Present address: Innovation Center, Freudenberg, 69465 Weinheim, Germany.

2

Present address: School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164.

3

Corresponding author.

4

Present address: The University of Oklahoma, Norman, OK.

J. Mech. Des 132(12), 121008 (Dec 07, 2010) (13 pages) doi:10.1115/1.4002751 History: Received August 24, 2008; Revised August 05, 2009; Published December 07, 2010; Online December 07, 2010

Designers are continuously challenged by complexity, as well as by the excessive instantiation and execution times of models, particularly in the context of integrated product and materials design. In order to manage these challenges, a systematic strategy for evaluating and selecting models is presented in this paper. The systematic strategy is based on value-of-information for design decision making. It consists of a (i) process performance indicator (PPI) to quantify the impact of model refinement from a decision-centric perspective and (ii) a method involving model evaluation. Using this method, a least complex but valid model is evaluated, and, only if necessary, gradually refined it until the most appropriate one is selected. The systematic approach is particularly well suited for integrated product and materials design, and all other scenarios where the perfect knowledge of the true system behavior and bounds of error are not available throughout the design space. The proposed strategy is applied to the design of photonic crystal waveguides for use in a next-generation optoelectronic communication system. In this paper, it is shown that the systematic strategy based on the PPI is useful for evaluating and selecting models particularly when accuracy of the prediction or the associated error bounds are not known.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 3

Mathematical utility-based cDSP (6,30)

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Figure 4

Overview of systematic strategy to evaluate and select models

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Figure 5

Optoelectronic communication system design problem: (a) typical optoelectronic communication system and (b) photonic crystal waveguide structures

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Figure 6

Geometry of the optical coupler and slow-light waveguide

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Figure 7

Reciprocal domain modal expansion method using periodic supercell boundary conditions in lateral direction and periodic boundary conditions along the length of waveguides

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Figure 8

Reciprocal domain modal expansion method using periodic supercell boundary conditions in the lateral direction and open boundary conditions along the length of waveguides

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Figure 2

PPI evaluation for multiple models

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Figure 1

PPI evaluation for a single model

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