| 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 | 334 | 2021 |
| 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 | 171 | 2021 |
| 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 | 113 | 2014 |
| 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 | 100 | 2021 |
| 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 | 75 | 2012 |
| 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 | 73 | 2022 |
| Extended finite element modeling of deformable porous media with arbitrary interfaces AR Khoei, E Haghighat Applied Mathematical Modelling 35 (11), 5426-5441, 2011 | 63 | 2011 |
| 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 | 49 | 2022 |
| PINNtomo: Seismic tomography using physics-informed neural networks U Waheed, T Alkhalifah, E Haghighat, C Song, J Virieux arXiv preprint arXiv:2104.01588, 2021 | 38 | 2021 |
| Energy-based error bound of physics-informed neural network solutions in elasticity M Guo, E Haghighat Journal of Engineering Mechanics 148 (8), 04022038, 2022 | 34 | 2022 |
| Constitutive model characterization and discovery using physics-informed deep learning E Haghighat, S Abouali, R Vaziri Engineering Applications of Artificial Intelligence 120, 105828, 2023 | 32 | 2023 |
| Modeling of deformation and localized failure in anisotropic rocks S Pietruszczak, E Haghighat International Journal of Solids and Structures 67, 93-101, 2015 | 30 | 2015 |
| A viscoplastic model of creep in shale E Haghighat, FS Rassouli, MD Zoback, R Juanes Geophysics 85 (3), MR155-MR166, 2020 | 29 | 2020 |
| 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 | 24 | 2022 |
| On modeling of fractured media using an enhanced embedded discontinuity approach E Haghighat, S Pietruszczak Extreme Mechanics Letters 6, 10-22, 2016 | 17 | 2016 |
| 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 | 15 | 2022 |
| Machine Learning for Accelerating 2D Flood Models: potential and challenges B Jamali, E Haghighat, A Ignjatovic, JP Leitão, A Deletic Hydrological Processes, e14064, 2021 | 14 | 2021 |
Ruben JuanesProfessor, MIT在 mit.edu 的电子邮件经过验证
