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cs.LG2025
A Foundation Model for Material Fracture Prediction
Agnese Marcato, Aleksandra Pachalieva, Ryley G. Hill +14
Accurately predicting when and how materials fail is critical to designing safe, reliable structures, mechanical systems, and engineered components that operate under stress. Yet,…
cs.LG2024
Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse Tsunameter Networks
Edward McDugald, Arvind Mohan, Darren Engwirda +2
We investigate the potential of an attention-based neural network architecture, the Senseiver, for sparse sensing in tsunami forecasting. Specifically, we focus on the Tsunami Data…