34 citations · 43 across the 5 of their papers we have counts for
5 papers
A Novel Physics-Regularized Interpretable Machine Learning Model for Grain Growth
Weishi Yan, Joseph Melville, Vishal Yadav +6
Experimental grain growth observations often deviate from grain growth simulations, revealing that the governing rules for grain boundary motion are not fully understood. A novel d…
Closing the sim-to-real gap in guided wave damage detection with adversarial training of variational auto-encoders
Ishan D. Khurjekar, Joel B. Harley
Guided wave testing is a popular approach for monitoring the structural integrity of infrastructures. We focus on the primary task of damage detection, where signal processing tech…
Wave-Informed Matrix Factorization with Global Optimality Guarantees
Harsha Vardhan Tetali, Joel B. Harley, Benjamin D. Haeffele
With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing representation learni…
Uncertainty Aware Deep Neural Network for Multistatic Localization with Application to Ultrasonic Structural Health Monitoring
Ishan D. Khurjekar, Joel B. Harley
Guided ultrasonic wave localization uses spatially distributed multistatic sensor arrays and generalized beamforming strategies to detect and locate damage across a structure. The…
Accounting for Physics Uncertainty in Ultrasonic Wave Propagation using Deep Learning
Ishan D. Khurjekar, Joel B. Harley
Ultrasonic guided waves are commonly used to localize structural damage in infrastructures such as buildings, airplanes, bridges. Damage localization can be viewed as an inverse pr…