Segmented Bayesian Calibration of Multidisciplinary Models

DeCarlo, Erin C.; Smarslok, Benjamin P.; Mahadevan, Sankaran

doi:10.2514/1.j054960

Cited by 19 publications

(8 citation statements)

References 29 publications

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“…The Bayesian Network can be calibrated using experimental data using Bayes' rule. DeCarlo et al [87][88][89] use it for increasing confidence of a coupled aerothermoelastic analysis.…”

mentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.

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“…The Bayesian Network can be calibrated using experimental data using Bayes' rule. DeCarlo et al [87][88][89] use it for increasing confidence of a coupled aerothermoelastic analysis.…”

mentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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“…3.3.1 (i.e., Eqs. (10) and (11)). If S I L < S e1 , where S e1 is a threshold for the first-order sensitivity index (S e1 ¼ 0:5 for the numerical examples given in Sec.…”

Section: Level 2 Gsamentioning

confidence: 99%

“…While most of the current multidisciplinary reliability analysis (MDRA) methods have only considered aleatory uncertainty, a couple of studies have also included model uncertainty [2,8,9]. The above MDRA methods have only considered time-independent reliability analysis, whereas time-dependent behavior and uncertainty sources are of interest in many practical multidisciplinary systems [10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis

Mahadevan

2017

Journal of Mechanical Design

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Multidisciplinary systems with transient behavior under time-varying inputs and coupling variables pose significant computational challenges in reliability analysis. Surrogate models of individual disciplinary analyses could be used to mitigate the computational effort; however, the accuracy of the surrogate models is of concern, since the errors introduced by the surrogate models accumulate at each time-step of the simulation. This paper develops a framework for adaptive surrogate-based multidisciplinary analysis (MDA) of reliability over time (A-SMART). The proposed framework consists of three modules, namely, initialization, uncertainty propagation, and three-level global sensitivity analysis (GSA). The first two modules check the quality of the surrogate models and determine when and where we should refine the surrogate models from the reliability analysis perspective. Approaches are proposed to estimate the potential error of the failure probability estimate and to determine the locations of new training points. The three-level GSA method identifies the individual surrogate model for refinement. The combination of the three modules facilitates adaptive and efficient allocation of computational resources, and enables high accuracy in the reliability analysis result. The proposed framework is illustrated with two numerical examples.

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“…The Approximate Bayesian Computation (ABC) method [25,26] is utilized to overcome the above obstacle by replacing the full likelihood with an approximate and efficient function containing the information of the observations and the instance of 𝛉. In this context, it is natural to construct the approximate likelihood employing the distance metrics.…”

Section: Approximate Bayesian Computation With Stochastic Distance Me...mentioning

confidence: 99%