428 citations · 1.6k across the 21 of their papers we have counts for
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Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Ehsan Haghighat, Danial Amini, Ruben Juanes
Physics-informed neural networks (PINNs) have received significant attention as a unified framework for forward, inverse, and surrogate modeling of problems governed by partial dif…
A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity
Mohammad Vahab, Ehsan Haghighat, Maryam Khaleghi +1
We explore an application of the Physics Informed Neural Networks (PINNs) in conjunction with Airy stress functions and Fourier series to find optimal solutions to a few reference…
Solving the Eikonal equation for compressional and shear waves in anisotropic media using peridynamic differential operator
Ali Can Bekar, Erdogan Madenci, Ehsan Haghighat +2
The traveltime of compressional (P) and shear (S) waves have proven essential in many applications of earthquake and exploration seismology. An accurate and efficient traveltime co…
Deep learning for solution and inversion of structural mechanics and vibrations
Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci +1
Deep learning has been the most popular machine learning method in the last few years. In this chapter, we present the application of deep learning and physics-informed neural netw…
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…