Advanced Numerical Methods for Computing Statistical Quantities of Interest from Solutions of SPDES

Abstract

Computational simulation-based predictions are central to science and engineering and to risk assessment and decision making in economics, public policy, and military venues, including several of importance to Air Force missions. Unfortunately, predictions are often fraught with uncertainty so that effective means for quantifying that uncertainty are of pararuount importance. The research effort investigates and resolves important algorithmic, mathematical, and practical issues related to the efficient, accurate, and robust computational determination of the quantities of interest used by engineers and decision makers that are determined from solutions of partial differential equations.

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Document Details

Document Type
Technical Report
Publication Date
Jan 19, 2012
Accession Number
ADA563949

Entities

People

  • Max Gunzburger

Organizations

  • Florida State University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computational Fluid Dynamics
  • Computational Science
  • Differential Equations
  • Engineering
  • Equations
  • Mathematics
  • Navier Stokes Equations
  • Numerical Analysis
  • Partial Differential Equations
  • Probability
  • Public Policy
  • Random Variables
  • Sampling
  • Simulations
  • White Noise

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design