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Article Dans Une Revue Lecture Notes in Computer Science Année : 2020

Model-based sensitivity analysis of nondestructive testing systems using machine learning algorithms

Résumé

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 1% accuracy of the physics model. CNN required 35, 56 and 56 high-fidelity model evaluations, respectively, for the same three cases.
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cea-04555898 , version 1 (23-04-2024)

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Jethro Nagawkar, Leifur Leifsson, Roberto Miorelli, Pierre Calmon. Model-based sensitivity analysis of nondestructive testing systems using machine learning algorithms. Lecture Notes in Computer Science, 2020, Lecture Notes in Computer Science, 12141, pp.71-83. ⟨10.1007/978-3-030-50426-7_6⟩. ⟨cea-04555898⟩
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