From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information

A. Gray; A. Wimbush; M. de Angelis; P. O. Hristov; D. Calleja; E. Miralles-Dolz; R. Rocchetta

doi:10.1016/j.ymssp.2021.108210

From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information

A. Gray
, A. Wimbush
, M. de Angelis
, P. O. Hristov
, D. Calleja
, E. Miralles-Dolz
, R. Rocchetta

Research output: Contribution to journal › Article › peer-review

42 Scopus citations

Abstract

In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient methodologies to integrate data into design decisions, and to conduct reliability analysis, and risk/reliability based design optimisation. To demonstrate its efficacy, the framework has been applied to the NASA 2020 uncertainty quantification challenge. The results and discussion in the paper are with respect to this application.

Original language	English (US)
Article number	108210
Journal	Mechanical Systems and Signal Processing
Volume	165
DOIs	https://doi.org/10.1016/j.ymssp.2021.108210
State	Published - Feb 15 2022
Externally published	Yes

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Civil and Structural Engineering
Aerospace Engineering
Mechanical Engineering
Computer Science Applications

Keywords

Bayesian calibration
Epistemic uncertainty
Optimisation under uncertainty
Probability bounds analysis
Uncertainty propagation
Uncertainty reduction

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10.1016/j.ymssp.2021.108210

Cite this

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title = "From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information",

abstract = "In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient methodologies to integrate data into design decisions, and to conduct reliability analysis, and risk/reliability based design optimisation. To demonstrate its efficacy, the framework has been applied to the NASA 2020 uncertainty quantification challenge. The results and discussion in the paper are with respect to this application.",

keywords = "Bayesian calibration, Epistemic uncertainty, Optimisation under uncertainty, Probability bounds analysis, Uncertainty propagation, Uncertainty reduction",

author = "A. Gray and A. Wimbush and \{de Angelis\}, M. and Hristov, \{P. O.\} and D. Calleja and E. Miralles-Dolz and R. Rocchetta",

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T2 - A comprehensive framework for uncertainty quantification in engineering with limited information

AU - Gray, A.

AU - Wimbush, A.

AU - de Angelis, M.

AU - Hristov, P. O.

AU - Calleja, D.

AU - Miralles-Dolz, E.

AU - Rocchetta, R.

PY - 2022/2/15

Y1 - 2022/2/15

N2 - In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient methodologies to integrate data into design decisions, and to conduct reliability analysis, and risk/reliability based design optimisation. To demonstrate its efficacy, the framework has been applied to the NASA 2020 uncertainty quantification challenge. The results and discussion in the paper are with respect to this application.

AB - In this paper we present a framework for addressing a variety of engineering design challenges with limited empirical data and partial information. This framework includes guidance on the characterisation of a mixture of uncertainties, efficient methodologies to integrate data into design decisions, and to conduct reliability analysis, and risk/reliability based design optimisation. To demonstrate its efficacy, the framework has been applied to the NASA 2020 uncertainty quantification challenge. The results and discussion in the paper are with respect to this application.

KW - Bayesian calibration

KW - Epistemic uncertainty

KW - Optimisation under uncertainty

KW - Probability bounds analysis

KW - Uncertainty propagation

KW - Uncertainty reduction

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UR - https://www.scopus.com/pages/publications/85114783669#tab=citedBy

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SN - 0888-3270

VL - 165

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

M1 - 108210

ER -

From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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