2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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…
Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields
Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
Physics-Informed Neural Networks (PINNs) have gained attention for solving partial differential equations, including the scattered Helmholtz equation, due to their flexibility and…
Ensemble Deep Learning for enhanced seismic data reconstruction
Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
Seismic data often contain gaps due to various obstacles in the investigated area and recording instrument failures. Deep learning techniques offer promising solutions for reconstr…