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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Reconstructing Modal Shapes in Resonant Ultrasound Spectroscopy from Sparse Spatial Data via Attention-Based Machine Learning
Author(s) Marc J. Murphy
On-Site Speaker (Planned) Marc J. Murphy
Abstract Scope We present an attention-based deep learning framework for reconstructing resonant ultrasound spectroscopy (RUS) mode shapes from highly sparse spatial measurements, addressing an extreme inpainting problem relevant to non-destructive evaluation. The model architecture combines a convolutional neural network (CNN) encoder–decoder backbone with a transformer-based bottleneck, integrating convolutional inductive biases with the global contextual learning capabilities of self- and cross-attention. This hybrid design enables the network to robustly infer global mode shape features while remaining invariant to input sparsity and measurement artifacts. Experimental results demonstrate that the model can accurately reconstruct RUS mode shapes from fewer than 8% of the original spatial test points, achieving over 99% reconstruction accuracy. This capability opens new avenues for efficient modal characterization and inverse design in scenarios where dense spatial sampling is impractical.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Mechanical Properties,

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