428 citations · 1.5k across the 19 of their papers we have counts for
6 papers · 1 filter
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
Danial Amini, Ehsan Haghighat, Ruben Juanes
We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs). We…
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media
Danial Amini, Ehsan Haghighat, Ruben Juanes
Physics-Informed Neural Networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDE)…
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…
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…
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci +1
The Physics-Informed Neural Network (PINN) framework introduced recently incorporates physics into deep learning, and offers a promising avenue for the solution of partial differen…
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…