Model-Based Sensitivity Analysis of Nondestructive Testing Systems Using Machine Learning Algorithms
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ABSTRACT: Model-based sensitivity analysis is crucial in quantifying which input variability parameter is important for nondestructive testing (NDT) systems. In this work, neural networks (NN) and convolutional NN (CNN) are shown to be computationally efficient at making model prediction for NDT systems, when compared to models such as polynomial chaos expansions, Kriging and polynomial chaos Kriging (PC-Kriging). Three different ultrasonic benchmark cases are considered. NN outperform these three models for all the cases, while CNN outperformed these three models for two of the three cases. For the third case, it performed as well as PC-Kriging. NN required 48, 56 and 35 high-fidelity model evaluations, respectively, for the three cases to reach within
SUBMITTER: Krzhizhanovskaya V
PROVIDER: S-EPMC7302568 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
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