activity
20162025
most citedSolving the acoustic VTI wave equation using physics-informed neural networks

240 citations · 624 across the 9 of their papers we have counts for

collaborators

9 papers

physics.geo-ph2025

A new practical and effective source-independent full-waveform inversion with a velocity-distribution supported deep image prior: Applications to two real datasets

Chao Song, Tariq Alkhalifah, Umair Bin Waheed +2

Full-waveform inversion (FWI) is an advanced technique for reconstructing high-resolution subsurface physical parameters by progressively minimizing the discrepancy between observe…

physics.comp-ph2021★ 6 cited

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…

physics.geo-ph2021

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…

physics.geo-ph2021★ 134 cited

Wavefield reconstruction inversion via physics-informed neural networks

Chao Song, Tariq Alkhalifah

Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce cycle skipping in full-waveform inversion (FWI). WRI often requires expensive m…

physics.comp-ph2021★ 41 cited

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…

physics.comp-ph2021

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…

physics.comp-ph2020★ 240 cited

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…

physics.comp-ph2020★ 203 cited

PINNeik: Eikonal solution using physics-informed neural networks

Umair bin Waheed, Ehsan Haghighat, Tariq Alkhalifah +2

The eikonal equation is utilized across a wide spectrum of science and engineering disciplines. In seismology, it regulates seismic wave traveltimes needed for applications like so…

physics.optics2016

Inverse design of resonant nanostructures for extraordinary optical transmission of periodic metallic slits

Yongbo Deng, Chao Song, Yihui Wu +2

This paper has presented inversely determining the resonant configuration of the bilateral nanostructures for periodic metallic slits with extraordinary optical transmission perfor…