Uncertainty Quantification using Exponential Epi-Splines

Abstract

We quantify uncertainty in complex systems by a flexible, nonparametric framework for estimating probability density functions of output quantities of interest. The framework systematically incorporates soft information about the system from engineering judgement and experience to improve the estimates and ensure that they are consistent with prior knowledge. The framework is based on a maximum likelihood criterion with epi-splines facilitating rapid solution of the resulting optimization problems. In four numerical examples with few realizations of the system output, we identify the main features of output densities even for nonsmooth and discontinuous system function and high-dimensional inputs.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2013
Accession Number
ADA580235

Entities

People

  • J. O. Royset
  • N. Sukumar
  • R. J. Wets

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Data Analysis
  • Data Science
  • Differential Equations
  • Engineering
  • Equations
  • Information Science
  • Mathematics
  • Numerical Analysis
  • Optimization
  • Partial Differential Equations
  • Polynomials
  • Probability
  • Probability Density Functions
  • Random Variables
  • Statistics
  • Stochastic Processes

Readers

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