COMBINED DATA AND DEEP LEARNING MODEL UNCERTAINTIES: AN APPLICATION TO THE MEASUREMENT OF SOLID FUEL REGRESSION RATE
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Published:2023
Issue:5
Volume:13
Page:23-40
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ISSN:2152-5080
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Container-title:International Journal for Uncertainty Quantification
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language:en
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Short-container-title:Int. J. UncertaintyQuantification
Author:
Georgalis Georgios,Retfalvi Kolos,Desjardin Paul E.,Patra Abani
Abstract
In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have uncertainties originating from multiple sources, combining these in a systematic way for quantities of interest (QoI) remains a challenge. In this paper, we present a forward propagation uncertainty quantification (UQ) process to produce a probabilistic distribution for the observed regression rate r. We characterized two input data uncertainty sources from the experiment (the distortion from the camera <i>U</i><sub>c</sub> and the non-zero-angle fuel placement <i>U</i><sub>Y</sub>), the prediction and model form uncertainty from the deep neural network (<i>U</i><sub>m</sub>), as well as the variability from the manually segmented images used for training it (<i>U</i><sub>s</sub>). We conducted seven case studies on combinations of these uncertainty sources with the model form uncertainty. The main contribution of this paper is the investigation and inclusion of the experimental image data uncertainties involved, and how to include them in a workflow when the QoI is the result of multiple sequential processes.
Subject
Control and Optimization,Discrete Mathematics and Combinatorics,Modeling and Simulation,Statistics and Probability
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