428 citations · 1.6k across the 21 of their papers we have counts for
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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)…
Constitutive model characterization and discovery using physics-informed deep learning
Ehsan Haghighat, Sahar Abouali, Reza Vaziri
Classically, the mechanical response of materials is described through constitutive models, often in the form of constrained ordinary differential equations. These models have a ve…