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Ehsan Haghighat
Ehsan Haghighat
Research Affiliate at MIT - Research Scientist at Carbon3D
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
161*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
105*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 (1), 79-108, 2014
912014
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
612012
Extended finite element modeling of deformable porous media with arbitrary interfaces
AR Khoei, E Haghighat
Applied Mathematical Modelling 35 (11), 5426-5441, 2011
572011
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
41*2015
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
342021
PINNeik: Eikonal solution using physics-informed neural networks
U bin Waheed, E Haghighat, T Alkhalifah, C Song, Q Hao
Computers & Geosciences 155, 104833, 2021
32*2021
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
302021
Modeling of deformation and localized failure in anisotropic rocks
S Pietruszczak, E Haghighat
International Journal of Solids and Structures 67, 93-101, 2015
242015
On modeling of fractured media using an enhanced embedded discontinuity approach
E Haghighat, S Pietruszczak
Extreme Mechanics Letters 6, 10-22, 2016
152016
An energy-based error bound of physics-informed neural network solutions in elasticity
M Guo, E Haghighat
arXiv preprint arXiv:2010.09088, 2020
132020
A viscoplastic model of creep in shaleViscoplastic model of creep
E Haghighat, FS Rassouli, MD Zoback, R Juanes
Geophysics 85 (3), MR155-MR166, 2020
112020
Anisotropic eikonal solution using physics-informed neural networks
U Waheed, E Haghighat, T Alkhalifah
SEG International Exposition and Annual Meeting, 2020
102020
Assessment of slope stability in cohesive soils due to a rainfall
S Pietruszczak, E Haghighat
International Journal for Numerical and Analytical Methods in Geomechanics …, 2013
82013
PINNtomo: Seismic tomography using physics-informed neural networks
UB Waheed, T Alkhalifah, E Haghighat, C Song, J Virieux
arXiv preprint arXiv:2104.01588, 2021
72021
Characterizing the mechanical behaviour of the Tournemire argillite
X Su, S Nguyen, E Haghighat, S Pietruszczak, D Labrie, JD Barnichon, ...
Geological Society, London, Special Publications 443 (1), 97-113, 2017
72017
A holistic approach to computing first-arrival traveltimes using neural networks
U bin Waheed, T Alkhalifah, E Haghighat, C Song
Advances in Subsurface Data Analytics, 251-278, 2022
62022
Machine Learning for Accelerating 2D Flood Models: potential and challenges
B Jamali, E Haghighat, A Ignjatovic, JP Leitão, A Deletic
Hydrological Processes, e14064, 2021
62021
Pinntomo: Seismic tomography using physics-informed neural networks
U bin Waheed, T Alkhalifah, E Haghighat, C Song, J Virieux
AGU Fall Meeting 2021, 2021
52021
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Articles 1–20