Ehsan Haghighat | DeepAI

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∙ 06/07/2023

A novel deeponet model for learning moving-solution operators with applications to earthquake hypocenter localization

Seismicity induced by human activities poses a significant threat to pub...

0 Ehsan Haghighat, et al. ∙

∙ 03/13/2023

An efficient phase-field model of shear fractures using deviatoric stress split

We propose a phase-field model of shear fractures using the deviatoric s...

0 Ehsan Haghighat, et al. ∙

∙ 09/07/2022

Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks

We propose a solution strategy for parameter identification in multiphas...

0 Danial Amini, et al. ∙

∙ 03/18/2022

Constitutive model characterization and discovery using physics-informed deep learning

Classically, the mechanical response of materials is described through c...

0 Ehsan Haghighat, et al. ∙

∙ 03/03/2022

Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media

Physics-Informed Neural Networks (PINNs) have received increased interes...

0 Danial Amini, et al. ∙

∙ 10/06/2021

Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training

Physics-informed neural networks (PINNs) have received significant atten...

0 Ehsan Haghighat, et al. ∙

∙ 08/16/2021

A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity

We explore an application of the Physics Informed Neural Networks (PINNs...

19 Mohammad Vahab, et al. ∙

∙ 05/18/2021

Deep learning for solution and inversion of structural mechanics and vibrations

Deep learning has been the most popular machine learning method in the l...

0 Ehsan Haghighat, et al. ∙

∙ 11/27/2020

Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture

We present a Physics-Informed Neural Network (PINN) to simulate the ther...

13 Sina Amini Niaki, et al. ∙

∙ 10/18/2020

An energy-based error bound of physics-informed neural network solutions in elasticity

An energy-based a posteriori error bound is proposed for the physics-inf...

0 Mengwu Guo, et al. ∙

∙ 05/31/2020

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

The Physics-Informed Neural Network (PINN) framework introduced recently...

9 Ehsan Haghighat, et al. ∙

∙ 05/11/2020

SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

In this paper, we introduce SciANN, a Python package for scientific comp...

1 Ehsan Haghighat, et al. ∙

∙ 02/14/2020

A deep learning framework for solution and discovery in solid mechanics

We present the application of a class of deep learning, known as Physics...

1 Ehsan Haghighat, et al. ∙

∙ 02/14/2020

A deep learning framework for solution and discovery in solid mechanics: linear elasticity

We present the application of a class of deep learning, known as Physics...

1 Ehsan Haghighat, et al. ∙

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