29 citations · 40 across the 40 of their papers we have counts for
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A Green-Integral-Constrained Neural Solver with Stochastic Physics-Informed Regularization
Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
Standard physics-informed neural networks (PINNs) struggle to simulate highly oscillatory Helmholtz solutions in heterogeneous media because pointwise minimization of second-order…
LAViG-FLOW: Latent Autoregressive Video Generation for Fluid Flow Simulations
Vittoria De Pellegrini, Tariq Alkhalifah
Modeling and forecasting subsurface multiphase fluid flow fields underpin applications ranging from geological CO2 sequestration (GCS) operations to geothermal production. This is…
Target noise: A pre-training based neural network initialization for efficient high resolution learning
Shaowen Wang, Tariq Alkhalifah
Weight initialization plays a crucial role in the optimization behavior and convergence efficiency of neural networks. Most existing initialization methods, such as Xavier and Kaim…
Diffusion priors enhanced velocity model building from time-lag images using a neural operator
Xiao Ma, Mohammad Hasyim Taufik, Tariq Alkhalifah
Velocity model building serves as a crucial component for achieving high precision subsurface imaging. However, conventional velocity model building methods are often computational…
Least-Squares-Embedded Optimization for Accelerated Convergence of PINNs in Acoustic Wavefield Simulations
Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
Physics-Informed Neural Networks (PINNs) have shown promise in solving partial differential equations (PDEs), including the frequency-domain Helmholtz equation. However, standard t…
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network
Shijun Cheng, Tariq Alkhalifah
Physics-informed neural networks (PINNs) face significant challenges in modeling multi-frequency wavefields in complex velocity models due to their slow convergence, difficulty in…