Details
| Original language | English |
|---|---|
| Article number | 052058 |
| Number of pages | 12 |
| Journal | IOP Conference Series: Materials Science and Engineering |
| Volume | 1043 |
| Issue number | 5 |
| Publication status | Published - 2 Feb 2021 |
| Event | 10th International Conference on Quality, Reliability, Risk, Maintenance,and Safety Engineering, QR2MSE 2020 - Shaanxi, China, Shaanxi, China Duration: 8 Oct 2020 → 11 Oct 2020 Conference number: 10 |
Abstract
Uncertainties that exist in nature or due to lack of knowledge have been widely recognized by researchers and engineering practitioners throughout engineering design and analysis for decades. Though great efforts have been devoted to the issues of uncertainty quantification (UQ) in various aspects, the methodologies on the quantification of aleatory uncertainty and epistemic uncertainty are usually logically inconsistent. For instance, the aleatory uncertainty is usually quantified in the framework of probability theory, whereas the epistemic uncertainty is quantified mostly by non-probabilistic methods. In the present paper, a probabilistically consistent framework for the quantification of both aleatory and epistemic uncertainty by synthesizing the probability density evolution method (PDEM) and the change of probability measure (COM) is outlined. The framework is then applied to the backward issues of uncertainty quantification. In particular, the uncertainty model updating issue is discussed in this paper. A numerical example is presented, and the results indicate the flexibility and efficiency of the proposed PDEM-COM framework.
ASJC Scopus subject areas
- Materials Science(all)
- General Materials Science
- Engineering(all)
- General Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: IOP Conference Series: Materials Science and Engineering, Vol. 1043, No. 5, 052058, 02.02.2021.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - A PDEM-COM framework for uncertainty quantification of backward issues involving both aleatory and epistemic uncertainties
AU - Wan, Z. Q.
AU - Chen, J. B.
AU - Beer, M.
N1 - Conference code: 10
PY - 2021/2/2
Y1 - 2021/2/2
N2 - Uncertainties that exist in nature or due to lack of knowledge have been widely recognized by researchers and engineering practitioners throughout engineering design and analysis for decades. Though great efforts have been devoted to the issues of uncertainty quantification (UQ) in various aspects, the methodologies on the quantification of aleatory uncertainty and epistemic uncertainty are usually logically inconsistent. For instance, the aleatory uncertainty is usually quantified in the framework of probability theory, whereas the epistemic uncertainty is quantified mostly by non-probabilistic methods. In the present paper, a probabilistically consistent framework for the quantification of both aleatory and epistemic uncertainty by synthesizing the probability density evolution method (PDEM) and the change of probability measure (COM) is outlined. The framework is then applied to the backward issues of uncertainty quantification. In particular, the uncertainty model updating issue is discussed in this paper. A numerical example is presented, and the results indicate the flexibility and efficiency of the proposed PDEM-COM framework.
AB - Uncertainties that exist in nature or due to lack of knowledge have been widely recognized by researchers and engineering practitioners throughout engineering design and analysis for decades. Though great efforts have been devoted to the issues of uncertainty quantification (UQ) in various aspects, the methodologies on the quantification of aleatory uncertainty and epistemic uncertainty are usually logically inconsistent. For instance, the aleatory uncertainty is usually quantified in the framework of probability theory, whereas the epistemic uncertainty is quantified mostly by non-probabilistic methods. In the present paper, a probabilistically consistent framework for the quantification of both aleatory and epistemic uncertainty by synthesizing the probability density evolution method (PDEM) and the change of probability measure (COM) is outlined. The framework is then applied to the backward issues of uncertainty quantification. In particular, the uncertainty model updating issue is discussed in this paper. A numerical example is presented, and the results indicate the flexibility and efficiency of the proposed PDEM-COM framework.
UR - http://www.scopus.com/inward/record.url?scp=85101604144&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/1043/5/052058
DO - 10.1088/1757-899X/1043/5/052058
M3 - Conference article
AN - SCOPUS:85101604144
VL - 1043
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
SN - 1757-8981
IS - 5
M1 - 052058
T2 - 10th International Conference on Quality, Reliability, Risk, Maintenance,and Safety Engineering, QR2MSE 2020
Y2 - 8 October 2020 through 11 October 2020
ER -