Computer Models in National Defense and Homeland Security (David Banks, organizer)

David Banks (Duke University)
Statistical Issues in Model Validation

Saturday 8:30-9:00, Fountain I

Abstract:

Model validation is an old problem, and it is getting fresh attention as people struggle to address complex problems in military and national security simulation. This talk reviews many of the core issues, and then focuses upon agent-based models, physics based models, and local dimensional strategies for useful simplification.


Kirstie Bellman (Aerospace Corportation)
The Challenges of Modeling Complex Embedded Systems Correctly and Usefully

Saturday 9:00-9:30, Fountain I

Abstract:

In order to manage, control and analyze complex systems, one often must call upon a diversity of modeling methods and approaches, as well as types of data and expertise. It has been realized for some time by the DoD modeling community that integrating heterogeneous models, databases, and processing programs is one of the most challenging issues facing those who want to build and use advanced modeling, simulation and analysis (MSA) methods for the complex systems that DoD is now building. These formidable integration challenges have been made more difficult by the increasing use of MSA in embedded systems. This talk describes how MSA is changed by being embedded, especially in the time-sensitive applications common to DoD, and discusses some of the methods that must be developed by the MSA community in order to evaluate and correctly utilize such capabilities.


Leslie M. Moore (Los Alamos National Laboratory)
Computer Experiment Designs to Achieve Multiple Objectives

Saturday 9:30-10:00, Fountain I

Abstract:

Issues and strategies for designing computer experiments are reviewed for conducting sensitivity analysis and construction of an emulator. Simulator codes are a basis for inference in many complex problems including weapons performance, materials aging, infrastructure modeling, nuclear reactor production, and manufacturing process improvement. Goals of computer experiments include sensitivity analysis to gain understanding of the input space and construction of an emulator that may form a basis for uncertainty analysis or prediction. Orthogonal arrays, or highly fractionated factorial designs, and near-orthogonal arrays are used for computer experiments for sensitivity analyses. Latin hypercube samples, possibly selected by space-filling criterion, are in common use when Gaussian spatial processes are the modeling paradigm or uncertainty analysis is the objective. Orthogonal-array based Latin hypercube designs are used to achieve both objectives. Improvement in terms of obtaining a space-filling design will be demonstrated for orthogonal-array based Latin hypercube design. The impact of competing experiment objectives will be discussed in terms of loss of efficiency in sensitivity analysis conducted with data from a Latin hypercube design.