structural reliability, and process
control.
Nonetheless, challenges remain
in standardizing UA&UQ methods,
improving AI model interpretability,
and integrating hybrid physics and AI
uncertainty models. Future research
should focus on scalable real-time UQ,
uncertainty-aware autonomous NDE,
and digital twin–driven predictive main-
tenance. These innovations will set new
benchmarks for automation, reliabil-
ity, and safety, ultimately enabling fully
automated, uncertainty-aware inspec-
tions for cost-effective and risk-informed
decision-making.
AUTHORS
Zi Li: Assistant Professor, Department of Physics
and Engineering, Alma College, Alma, MI
Department of Electrical and Computer Engi-
neering, Michigan State University, MI liz@
alma.edu lizi4@msu.edu
Yiming Deng: Professor, Nondestructive Evalu-
ation Laboratory, Department of Electrical and
Computer Engineering, Michigan State Univer-
sity, East Lansing, MI dengyimi@egr.msu.edu
Materials Evaluation 83 (8): 24–39
https://doi.org/10.32548/2025.me-04541
©2025 American Society for Nondestructive Testing
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