Data-driven optimization of dynamic reconfigurable systems of systems.

Tucker, Conrad S. ; Eddy, John P.

Sandia National Laboratories

关键词: Mining;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Sensitivity Analysis;    Simulation;    Efficiency;   

DOI  :  10.2172/1011663
RP-ID  :  SAND2010-8037
RP-ID  :  AC04-94AL85000
RP-ID  :  1011663

美国|英语

来源: UNT Digital Library

【 摘 要 】

This report documents the results of a Strategic Partnership (aka University Collaboration) LDRD program between Sandia National Laboratories and the University of Illinois at Urbana-Champagne. The project is titled 'Data-Driven Optimization of Dynamic Reconfigurable Systems of Systems' and was conducted during FY 2009 and FY 2010. The purpose of this study was to determine and implement ways to incorporate real-time data mining and information discovery into existing Systems of Systems (SoS) modeling capabilities. Current SoS modeling is typically conducted in an iterative manner in which replications are carried out in order to quantify variation in the simulation results. The expense of many replications for large simulations, especially when considering the need for optimization, sensitivity analysis, and uncertainty quantification, can be prohibitive. In addition, extracting useful information from the resulting large datasets is a challenging task. This work demonstrates methods of identifying trends and other forms of information in datasets that can be used on a wide range of applications such as quantifying the strength of various inputs on outputs, identifying the sources of variation in the simulation, and potentially steering an optimization process for improved efficiency.