Choubineh, Abouzar, Chen, Jie, Coenen, Frans 
ORCID: 0000-0003-1026-6649 and Ma, Fei ORCID: 0000-0001-6099-480X
(2023)
Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data.
ELECTRONICS, 12 (6).
p. 1453.
Abstract
<jats:p>Although Deep Learning (DL) models have been introduced in various fields as effective prediction tools, they often do not care about uncertainty. This can be a barrier to their adoption in real-world applications. The current paper aims to apply and evaluate Monte Carlo (MC) dropout, a computationally efficient approach, to investigate the reliability of several skip connection-based Convolutional Neural Network (CNN) models while keeping their high accuracy. To do so, a high-dimensional regression problem is considered in the context of subterranean fluid flow modeling using 376,250 generated samples. The results demonstrate the effectiveness of MC dropout in terms of reliability with a Standard Deviation (SD) of 0.012–0.174, and of accuracy with a coefficient of determination (R2) of 0.7881–0.9584 and Mean Squared Error (MSE) of 0.0113–0.0508, respectively. The findings of this study may contribute to the distribution of pressure in the development of oil/gas fields.</jats:p>
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | deep learning, fluid flow modeling, mixed GMsFEM, Monte Carlo dropout, regression, reliability, standard deviation |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 11 Apr 2023 09:47 |
| Last Modified: | 14 Mar 2024 21:44 |
| DOI: | 10.3390/electronics12061453 |
| Open Access URL: | https://www.mdpi.com/2079-9292/12/6/1453 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169514 |
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