41 citations · 41 across the 1 of their papers we have counts for
3 papers
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…
cs.OH2020
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
Ehsan Haghighat, Ruben Juanes
In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely used deep-…
cs.LG2020
A deep learning framework for solution and discovery in solid mechanics
Ehsan Haghighat, Maziar Raissi, Adrian Moure +2
We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporat…