240 citations · 968 across the 42 of their papers we have counts for
6 papers · 1 filter
LatentPINNs: Generative physics-informed neural networks via a latent representation learning
Mohammad H. Taufik, Tariq Alkhalifah
Physics-informed neural networks (PINNs) are promising to replace conventional partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. How…
Efficient physics-informed neural networks using hash encoding
Xinquan Huang, Tariq Alkhalifah
Physics-informed neural networks (PINNs) have attracted a lot of attention in scientific computing as their functional representation of partial differential equation (PDE) solutio…
Laplace HypoPINN: Physics-Informed Neural Network for hypocenter localization and its predictive uncertainty
Muhammad Izzatullah, Isa Eren Yildirim, Umair Bin Waheed +1
Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and th…
StorSeismic: A new paradigm in deep learning for seismic processing
Randy Harsuko, Tariq Alkhalifah
Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e. geometrical) of the data. We present StorSeism…
PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
Xinquan Huang, Tariq Alkhalifah
Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in seismic modeling and inversion. However, when dealing with hi…
Direct domain adaptation through reciprocal linear transformations
Tariq Alkhalifah, Oleg Ovcharenko
We propose a direct domain adaptation (DDA) approach to enrich the training of supervised neural networks on synthetic data by features from real-world data. The process involves a…