COMBINED DATA AND DEEP LEARNING MODEL UNCERTAINTIES: AN APPLICATION TO THE MEASUREMENT OF SOLID FUEL REGRESSION RATE
-
Published:2023
Issue:5
Volume:13
Page:23-40
-
ISSN:2152-5080
-
Container-title:International Journal for Uncertainty Quantification
-
language:en
-
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
Reference37 articles.
1. Moreno-Rodenas, A.M., Tscheikner-Gratl, F., Langeveld, J.G., and Clemens, F.H., Uncertainty Analysis in a Large-Scale Water Quality Integrated Catchment Modelling Study, Water Res., 158:46-60, 2019. 2. Tan, J., Villa, U., Shamsaei, N., Shao, S., Zbib, H.M., and Faghihi, D., A Predictive Discrete-Continuum Multiscale Model of Plasticity with Quantified Uncertainty, Int. J. Plasticity, 138:102935, 2021. 3. Jones, R.E., Redle, M.T., Kolla, H., and Plews, J.A., A Minimally Invasive, Efficient Method for Propagation of Full-Field Uncertainty in Solid Dynamics, Int. J. Numer. Methods Eng., 122(23):6955-6983, 2021. 4. Smith, R., Uncertainty Quantification, Philadelphia: SIAM, 2014. 5. Psaros, A.F., Meng, X., Zou, Z., Guo, L., and Karniadakis, G.E., Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons, J. Comput. Phys., 477:111902, 2023.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|