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Interpretable & Explainable Machine Learning for Ultrasonic Defect Sizing

Supporting data and code for paper "Interpretable & Explainable Machine Learning for Ultrasonic Defect Sizing" to be published in IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control

Complete download (zip, 3.5 GiB)

Creator(s) Richard Pyle
Contributor(s) Robert Hughes
Publication date 27 Jan 2023
Language eng
Publisher University of Bristol
Licence Non-Commercial Government Licence for public sector information
DOI 10.5523/bris.2o82rzo6d5ly32h7msblzq4y8v
Citation Richard Pyle (2023): Interpretable & Explainable Machine Learning for Ultrasonic Defect Sizing. https://doi.org/10.5523/bris.2o82rzo6d5ly32h7msblzq4y8v
Total size 3.5 GiB

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