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Automated uncertainty quantification analysis using system model and data

Published

Author(s)

Saideep Nannapaneni, Sankaran Mahadevan, David Lechevalier, Anantha Narayanan Narayanan, Sudarsan Rachuri

Abstract

Understanding the sources of, and quantifying the magnitude of, uncertainty can improve decision-making and, thereby, make manufacturing systems more efficient. Accomplishing all of them requires knowledge in four separate domains: statistics, data science, optimization, and, of course, manufacturing. In this paper, we focus on quantifying uncertainty, usually called uncertainty quantification (UQ). More specifically, we propose a methodology to perform UQ automatically using Bayesian networks (BN) constructed from three types of sources: a descriptive system model, physics-based mathematical models, and data. The system model is a high-level model describing the system and its parameters; we develop this model using the Generic Modeling Environment (GME) platform. Physics-based models, which are usually in the form of equations, are assumed to be in a text format. Data are also assumed to be available in a text format. The proposed methodology is divided into two tasks - (1) Automated Bayesian network construction using these three sources and (2) Automated UQ analysis. The first task involves creating a meta-model for the Bayesian network using GME and a syntax representation for the conditional probability tables/ distributions. The actual Bayesian network is an instance model of the Bayesian network meta-model. We describe algorithms for automated BN construction and UQ analysis, which are implemented programmatically using the GME platform. We finally demonstrate the proposed techniques quantifying the uncertainty of two systems.
Proceedings Title
2015 IEEE International Conference on Big Data (IEEE Big Data 2015) Proceedings
Conference Dates
October 29-November 1, 2015
Conference Location
Santa Clara, CA, US
Conference Title
2015 IEEE International Conference on Big Data (IEEE Big Data 2015)

Keywords

Bayesian network, meta-model, generic modeling environment, uncertainty quantification, automation

Citation

Nannapaneni, S. , Mahadevan, S. , Lechevalier, D. , Narayanan, A. and Rachuri, S. (2015), Automated uncertainty quantification analysis using system model and data, 2015 IEEE International Conference on Big Data (IEEE Big Data 2015) Proceedings, Santa Clara, CA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=919222 (Accessed February 27, 2024)
Created October 28, 2015, Updated October 12, 2021