Uncertainty quantification using evidence theory in multidisciplinary design optimization

By Harish Agarwal, John E. Renaud, Evan L. Preston, D. Padmanabhan. Reliability Engineering & System Safety, 2004

372 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0fe2be76e83bba2e8cab383aaf813ad5412b7945

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Chao Jiang
2. Sankaran Mahadevan
3. Xu Han
4. Jia Guo
5. Zhen Luo
6. Wangxi Zhang
7. B. Wang
8. Liang Gao

In Statistics and Probability

1. Chao Jiang
2. Xu Han
3. Sankaran Mahadevan
4. Zhen Luo
5. Wangxi Zhang
6. B. Wang
7. Jia Guo
8. Liang Gao

Citations in the corpus

• Unified Uncertainty Analysis by the First Order Reliability Method (2008, background): “Several examples include (1) studies on the relationships and differences between probability and non-probability theories [21, 22]; (2) study on bounding the value of information with epistemic distribution parameters [23]; (3) optimization design by possibility and evidence theory [24, 25]; (4) se”
• Sensitivity Analysis with Mixture of Epistemic and Aleatory Uncertainties (2007, background): “Exploratory research on epistemic uncertainty by evidence theory has recently been conducted, including studies in risk assessment, decision making, and design optimization [8,14,17–25].”
• Reliability Analysis for Multidisciplinary Systems with Random and Interval Variables (2010, methodology): “Examples of using intervals in multidisciplinary systems are given in [13,14].”
• Structural reliability analysis using a copula-function-based evidence theory model (2014, methodology): “[34] proposed the application of evidence theory based on the trust region method to address multidisciplinary optimization problems in the reliability design.”
• Reliability-based design optimization of multidisciplinary system under aleatory and epistemic uncertainty (2016, methodology): “Agarwal et al. (2004) developed an evidence theory-based approach to multidisciplinary RBDO under epistemic uncertainty alone using continuous response surfaces for belief and plausibility functions and a sequential approximate optimization approach. Yao et al. (2013) proposed a multidisciplinary RB”
• An efficient method for reliability analysis under epistemic uncertainty based on evidence theory and support vector regression (2015, methodology): “Agarwal et al. (2004) utilised evidence theory to quantify epistemic uncertainty in multidisciplinary systems analysis.”