Ehsan Haghighat

24 papers Journal 24

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	LatticeGraphNet: a two-scale graph neural operator for simulating lattice structures. Eng. Comput. Ayush Jain, Ehsan Haghighat, Sai Nelaturi
2025	J	`jnl`	Multiphysics discovery with moving boundaries using Ensemble SINDy and peridynamic differential operator. Eng. Comput. Ali Can Bekar, Ehsan Haghighat, Erdogan Madenci
2024	J	`jnl`	LatticeGraphNet: A two-scale graph neural operator for simulating lattice structures. CoRR Ayush Jain, Ehsan Haghighat, Sai Nelaturi
2024	J	`jnl`	STONet: A novel neural operator for modeling solute transport in micro-cracked reservoirs. CoRR Ehsan Haghighat, Mohammad Hesan Adeli, S. Mohammad Mousavi, Ruben Juanes
2023	J	`jnl`	A novel deeponet model for learning moving-solution operators with applications to earthquake hypocenter localization. CoRR Ehsan Haghighat, Umair bin Waheed, George Em Karniadakis
2023	J	`jnl`	An efficient phase-field model of shear fractures using deviatoric stress split. CoRR Ehsan Haghighat, David Santillán
2023	J	`jnl`	Constitutive model characterization and discovery using physics-informed deep learning. Eng. Appl. Artif. Intell. Ehsan Haghighat, Sahar Abouali, Reza Vaziri
2023	J	`jnl`	Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks. J. Comput. Phys. Danial Amini, Ehsan Haghighat, Ruben Juanes
2023	J	`jnl`	Machine Learning-Enabled Precision Position Control and Thermal Regulation in Advanced Thermal Actuators. CoRR Seyed M. Mirvakili, Ehsan Haghighat, Douglas Sim
2023	J	`jnl`	Multiphysics discovery with moving boundaries using Ensemble SINDy and Peridynamic Differential Operator. CoRR Ali Can Bekar, Ehsan Haghighat, Erdogan Madenci
2022	J	`jnl`	An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator. CoRR A. Mavi, Ali Can Bekar, Ehsan Haghighat, Erdogan Madenci
2022	J	`jnl`	Application of Physics-Informed Neural Networks for Forward and Inverse Analysis of Pile-Soil Interaction. CoRR Mohammad Vahab, Babak Shahbodagh, Ehsan Haghighat, Nasser Khalili
2022	J	`jnl`	Constitutive model characterization and discovery using physics-informed deep learning. CoRR Ehsan Haghighat, Sahar Abouali, Reza Vaziri
2022	J	`jnl`	Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks. CoRR Danial Amini, Ehsan Haghighat, Ruben Juanes
2022	J	`jnl`	Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media. CoRR Danial Amini, Ehsan Haghighat, Ruben Juanes
2021	J	`jnl`	A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity. CoRR Mohammad Vahab, Ehsan Haghighat, Maryam Khaleghi, Nasser Khalili
2021	J	`jnl`	Deep learning for solution and inversion of structural mechanics and vibrations. CoRR Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci, Ruben Juanes
2021	J	`jnl`	PINNeik: Eikonal solution using physics-informed neural networks. Comput. Geosci. Umair bin Waheed, Ehsan Haghighat, Tariq Alkhalifah, Chao Song, Qi Hao
2021	J	`jnl`	Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training. CoRR Ehsan Haghighat, Danial Amini, Ruben Juanes
2020	J	`jnl`	A deep learning framework for solution and discovery in solid mechanics: linear elasticity. CoRR Ehsan Haghighat, Maziar Raissi, Adrian Moure, Héctor Gómez, Ruben Juanes
2020	J	`jnl`	A nonlocal physics-informed deep learning framework using the peridynamic differential operator. CoRR Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci, Ruben Juanes
2020	J	`jnl`	An energy-based error bound of physics-informed neural network solutions in elasticity. CoRR Mengwu Guo, Ehsan Haghighat
2020	J	`jnl`	Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture. CoRR Sina Amini Niaki, Ehsan Haghighat, Xinglong Li, Trevor Campbell, Reza Vaziri
2020	J	`jnl`	SciANN: A Keras wrapper for scientific computations and physics-informed deep learning using artificial neural networks. CoRR Ehsan Haghighat, Ruben Juanes