skip to main content

DOE PAGESDOE PAGES

This content will become publicly available on February 4, 2017

Title: Bayesian methods for characterizing unknown parameters of material models

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed to characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.
Authors:
 [1] ;  [2] ;  [3]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. Bedford, NH (United States)
  2. Cornell Univ., Ithaca, NY (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
OSTI Identifier:
1237654
Report Number(s):
SAND--2015-3898J
Journal ID: ISSN 0307-904X; PII: S0307904X16300427
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Applied Mathematical Modelling
Additional Journal Information:
Journal Volume: 5; Journal Issue: 1; Journal ID: ISSN 0307-904X
Research Org:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING