An overview of component qualification using Bayesian statistics and energy methods.
The below overview is designed to give the reader a limited understanding of Bayesian and Maximum Likelihood (MLE) estimation; a basic understanding of some of the mathematical tools to evaluate the quality of an estimation; an introduction to energy methods and a limited discussion of damage potential. This discussion then goes on to presented a limited presentation as to how energy methods and Bayesian estimation are used together to qualify components. Example problems with solutions have been supplied as a learning aid. Bold letters are used to represent random variables. Un-bolded letter represent deterministic values. A concluding section presents a discussion of attributes and concerns.
- Research Organization:
- Sandia National Laboratories
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1029778
- Report Number(s):
- SAND2011-6006
- Country of Publication:
- United States
- Language:
- English
Similar Records
Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm
Investigation of methodology for uncertainty quantification of model parameters
Bayesian operator inference for data-driven reduced-order modeling
Journal Article
·
Tue Apr 04 20:00:00 EDT 2017
· Journal of Hydrology
·
OSTI ID:1371944
Investigation of methodology for uncertainty quantification of model parameters
Conference
·
Fri Jul 01 00:00:00 EDT 2016
·
OSTI ID:22977511
Bayesian operator inference for data-driven reduced-order modeling
Journal Article
·
Thu Jul 14 20:00:00 EDT 2022
· Computer Methods in Applied Mechanics and Engineering
·
OSTI ID:1960781