Dr Rich Pyle
Meng, PhD
Current positions
Contact
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Research interests
NDE, Ultrasonics, Deep Learning, Domain Adaptation, Data Compression
Publications
Recent publications
02/09/2026AI-Generated Formative Feedback for Engineering Lab Reports: The 28th International Conference on Engineering and Product Design Education - INTERNATIONAL CONFERENCE ON ENGINEERING AND PRODUCT DESIGN EDUCATION
Proceedings of the International Conference on Engineering and Product Design Education (E&PDE 2026)
Interpretable and Explainable Machine Learning for Ultrasonic Defect Sizing
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Potential and limitations of NARX for defect detection in guided wave signals
Structural Health Monitoring
Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Thesis
Application of machine learning to ultrasonic nondestructive evaluation
Supervisors
Award date
24/01/2023
Related people
Dr Joel Ross
Associate Professor in Engineering Experimental Practice Education
School of Civil, Aerospace and Design Engineering
Professor Anthony Croxford
Professor of Ultrasonics and Dynamics
School of Electrical, Electronic and Mechanical Engineering
Professor Paul Wilcox
Professor of Dynamics
School of Electrical, Electronic and Mechanical Engineering
Dr Robert Hughes
Senior Lecturer
School of Electrical, Electronic and Mechanical Engineering
Professor Aydin Nassehi
Head of School
School of Electrical, Electronic and Mechanical Engineering