Wavelet-Based Simulation Model Validation of Functional data

Abstract

As computer hardware technology continues to advance, so does the scientific communitys capability to develop high resolution computer models able to simulate complex systems and processes. This advancement has led to many challenges associated with verification and validation (V and V). These challenges include adapting methods to high dimensional functional data, maintaining the necessary objectivity, and accounting for noisy data. Department of Defense(DoD) simulation models require validation techniques that are able to overcome these challenges before the models can be relied upon. Model validation substantiates that the model chosen sufficiently represents the system and that it produces results consistent with real-world data within the range of model applicability. In this research, new statistical techniques will be proposed that improve upon existing simulation validation techniques. These techniques incorporate the use of wavelets to decompose the time-series data into the time-frequency spectrum allowing for objective and comprehensive assessment of the model. In addition, these techniques offer an improved method of analysis for noisy, high-dimensional data. These techniques are applied to assess the validity of simulation models, which will help ensure the accurate representation of the system they are meant to simulate.

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Document Details

Document Type
Technical Report
Publication Date
Sep 14, 2017
Accession Number
AD1055551

Entities

People

  • Andrew D. Atkinson

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Mining
  • Data Science
  • Department Of Defense
  • Industrial Engineering
  • Information Processing
  • Information Science
  • Knowledge Management
  • Monte Carlo Method
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Surveys

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Distributed Systems and Data Platform Development
  • Image Processing and Computer Vision.