Data Refinement for Confidence Management in Model-Based Predictions

Abstract

The activities under this grant were in the general area of Verification and Validation using the Polynomial Chaos formalism developed by the PI over the course of the past 15 years. In particular, three significant questions were formulated and addressed in the course of this research: 1. How to develop polynomial chaos representations of physical parameters from experimental measurements of these parameters? 2. How to propagate the uncertainty in these parameters (as reflected in their Polynomial Chaos representations) into the dynamical behavior of the physical system. 3. How important is data refinement: what is the significance, on the predictive value of a computational model, of collecting additional experimental measurements as opposed to performing further numerical refinement (using, for example, mesh refinement). The remainder of this report will review the highlights of each of the above three topics.

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Document Details

Document Type
Technical Report
Publication Date
Dec 31, 2004
Accession Number
ADA437394

Entities

People

  • Roger Ghanem

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Data Science
  • Differential Equations
  • Eigenvalues
  • Equations
  • Functional Analysis
  • Hilbert Space
  • Mathematical Analysis
  • Monte Carlo Method
  • Partial Differential Equations
  • Polynomials
  • Random Variables
  • Statistics
  • Stochastic Processes
  • Theses
  • Validation

Readers

  • Computational Modeling and Simulation
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Technical Research and Report Writing.