3 papers
cs.LG2026
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…
cs.LG2025
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…
physics.geo-ph2025
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 increasing attention for solving partial differential equations, including the Helmholtz equation, due to their flexibility and…