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Ehsan Haghighat
Ehsan Haghighat
Head of Simulations & AI at Eden -- Research Affiliate at MIT
Verified email at mit.edu - Homepage
Title
Cited by
Cited by
Year
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
E Haghighat, M Raissi, A Moure, H Gomez, R Juanes
Computer Methods in Applied Mechanics and Engineering 379, 113741, 2021
701*2021
SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
E Haghighat, R Juanes
Computer Methods in Applied Mechanics and Engineering 373, 113552, 2021
3342021
Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
SA Niaki, E Haghighat, T Campbell, A Poursartip, R Vaziri
Computer Methods in Applied Mechanics and Engineering 384, 113959, 2021
1712021
PINNeik: Eikonal solution using physics-informed neural networks
U bin Waheed, E Haghighat, T Alkhalifah, C Song, Q Hao
Computers & Geosciences 155, 104833, 2021
144*2021
A mesh-independent finite element formulation for modeling crack growth in saturated porous media based on an enriched-FEM technique
AR Khoei, M Vahab, E Haghighat, S Moallemi
International Journal of Fracture 188, 79-108, 2014
1122014
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
E Haghighat, AC Bekar, E Madenci, R Juanes
Computer Methods in Applied Mechanics and Engineering 385, 114012, 2021
992021
Thermo-hydro-mechanical modeling of impermeable discontinuity in saturated porous media with X-FEM technique
AR Khoei, S Moallemi, E Haghighat
Engineering Fracture Mechanics 96, 701-723, 2012
752012
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
E Haghighat, D Amini, R Juanes
Computer Methods in Applied Mechanics and Engineering 397, 115141, 2022
732022
Extended finite element modeling of deformable porous media with arbitrary interfaces
AR Khoei, E Haghighat
Applied Mathematical Modelling 35 (11), 5426-5441, 2011
632011
On modeling of discrete propagation of localized damage in cohesive‐frictional materials
E Haghighat, S Pietruszczak
International Journal for Numerical and Analytical Methods in Geomechanics …, 2015
52*2015
A physics-informed neural network approach to solution and identification of biharmonic equations of elasticity
M Vahab, E Haghighat, M Khaleghi, N Khalili
Journal of Engineering Mechanics 148 (2), 04021154, 2022
482022
PINNtomo: Seismic tomography using physics-informed neural networks
U Waheed, T Alkhalifah, E Haghighat, C Song, J Virieux
arXiv preprint arXiv:2104.01588, 2021
382021
Energy-based error bound of physics-informed neural network solutions in elasticity
M Guo, E Haghighat
Journal of Engineering Mechanics 148 (8), 04022038, 2022
342022
Constitutive model characterization and discovery using physics-informed deep learning
E Haghighat, S Abouali, R Vaziri
Engineering Applications of Artificial Intelligence 120, 105828, 2023
322023
Modeling of deformation and localized failure in anisotropic rocks
S Pietruszczak, E Haghighat
International Journal of Solids and Structures 67, 93-101, 2015
302015
A viscoplastic model of creep in shale
E Haghighat, FS Rassouli, MD Zoback, R Juanes
Geophysics 85 (3), MR155-MR166, 2020
292020
Physics-informed neural network solution of thermo–hydro–mechanical processes in porous media
D Amini, E Haghighat, R Juanes
Journal of Engineering Mechanics 148 (11), 04022070, 2022
242022
On modeling of fractured media using an enhanced embedded discontinuity approach
E Haghighat, S Pietruszczak
Extreme Mechanics Letters 6, 10-22, 2016
172016
On the solution of hyperbolic equations using the peridynamic differential operator
AC Bekar, E Madenci, E Haghighat
Computer Methods in Applied Mechanics and Engineering 391, 114574, 2022
152022
Machine Learning for Accelerating 2D Flood Models: potential and challenges
B Jamali, E Haghighat, A Ignjatovic, JP Leitão, A Deletic
Hydrological Processes, e14064, 2021
142021
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