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Found 6 papers, 0 papers with code
Wave Physics-informed Matrix Factorizations
With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing techniques that incorporate known physical constraints into the learned representation.
Modeling Fission Gas Release at the Mesoscale using Multiscale DenseNet Regression with Attention Mechanism and Inception Blocks
Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool for understanding how microstructure evolution impacts FGR, but they are computationally intensive.
Closing the sim-to-real gap in guided wave damage detection with adversarial training of variational auto-encoders
The detection performance is affected by a mismatch between the wave propagation model and experimental wave data.
Wave-Informed Matrix Factorization with Global Optimality Guarantees
With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing representation learning techniques that can incorporate known physical constraints into the learned representation.
Uncertainty Aware Deep Neural Network for Multistatic Localization with Application to Ultrasonic Structural Health Monitoring
Methods can compare data with models of wave propagation to locate damage.
Accounting for Physics Uncertainty in Ultrasonic Wave Propagation using Deep Learning
After evaluating the localization error on test data with uncertainty, we observe that the deep learning model trained with uncertainty can learn robust representations.
