41 citations · 47 across the 3 of their papers we have counts for
5 papers
Is it time to swish? Comparing activation functions in solving the Helmholtz equation using physics-informed neural networks
Ali Al-Safwan, Chao Song, Umair bin Waheed
Solving the wave equation numerically constitutes the majority of the computational cost for applications like seismic imaging and full waveform inversion. An alternative approach…
Wavefield solutions from machine learned functions
Tariq Alkhalifah, Chao Song, Umair bin Waheed +1
Solving the wave equation is one of the most (if not the most) fundamental problems we face as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation pr…
PINNtomo: Seismic tomography using physics-informed neural networks
Umair bin Waheed, Tariq Alkhalifah, Ehsan Haghighat +2
Seismic traveltime tomography using transmission data is widely used to image the Earth's interior from global to local scales. In seismic imaging, it is used to obtain velocity mo…
A holistic approach to computing first-arrival traveltimes using neural networks
Umair bin Waheed, Tariq Alkhalifah, Ehsan Haghighat +1
Since the original algorithm by John Vidale in 1988 to numerically solve the isotropic eikonal equation, there has been tremendous progress on the topic addressing an array of chal…
Solving the acoustic VTI wave equation using physics-informed neural networks
Chao Song, Tariq Alkhalifah, Umair bin Waheed
Frequency-domain wavefield solutions corresponding to the anisotropic acoustic wave equations can be used to describe the anisotropic nature of the earth. To solve a frequency-doma…