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2006.00446
Cited By
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
31 May 2020
E. Haghighat
A. Bekar
E. Madenci
R. Juanes
PINN
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Papers citing
"A nonlocal physics-informed deep learning framework using the peridynamic differential operator"
21 / 21 papers shown
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Component Fourier Neural Operator for Singularly Perturbed Differential Equations
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Yiwen Pang
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ML-based identification of the interface regions for coupling local and nonlocal models
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Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics
Computer Methods in Applied Mechanics and Engineering (CMAME), 2023
Honghui Du
QiZhi He
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494
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21 Nov 2023
A peridynamic-informed deep learning model for brittle damage prediction
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A. Sheidaei
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155
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02 Oct 2023
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEs
Jinshuai Bai
Guirong Liu
Ashish Gupta
Laith Alzubaidi
Xinzhu Feng
Yuantong T. Gu
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278
1
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13 Apr 2023
Physics-aware deep learning framework for linear elasticity
Anisha Roy
Rikhi Bose
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365
9
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19 Feb 2023
Utilising physics-guided deep learning to overcome data scarcity
Jinshuai Bai
Laith Alzubaidi
Qingxia Wang
E. Kuhl
Bennamoun
Yuantong T. Gu
PINN
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491
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0
24 Nov 2022
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
Zhongkai Hao
Songming Liu
Yichi Zhang
Chengyang Ying
Yao Feng
Hang Su
Jun Zhu
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469
168
0
15 Nov 2022
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing
S. Faroughi
N. Pawar
C. Fernandes
Maziar Raissi
Subasish Das
N. Kalantari
S. K. Mahjour
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310
75
0
14 Nov 2022
An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator
Computer Methods in Applied Mechanics and Engineering (CMAME), 2022
A. Mavi
A. Bekar
E. Haghighat
E. Madenci
202
37
0
21 Oct 2022
Constitutive model characterization and discovery using physics-informed deep learning
Engineering applications of artificial intelligence (EAAI), 2022
E. Haghighat
S. Abouali
R. Vaziri
PINN
AI4CE
396
83
0
18 Mar 2022
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media
Journal of engineering mechanics (J. Eng. Mech.), 2022
Daniel Amini
E. Haghighat
R. Juanes
PINN
AI4CE
296
37
0
03 Mar 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Journal of Scientific Computing (J. Sci. Comput.), 2022
S. Cuomo
Vincenzo Schiano Di Cola
F. Giampaolo
G. Rozza
Maizar Raissi
F. Piccialli
PINN
668
2,152
0
14 Jan 2022
CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method
Computer Methods in Applied Mechanics and Engineering (CMAME), 2021
P. Chiu
Jian Cheng Wong
C. Ooi
M. Dao
Yew-Soon Ong
PINN
400
331
0
29 Oct 2021
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
E. Haghighat
Daniel Amini
R. Juanes
PINN
AI4CE
348
144
0
06 Oct 2021
A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity
M. Vahab
E. Haghighat
M. Khaleghi
N. Khalili
PINN
293
65
0
16 Aug 2021
A physics-informed variational DeepONet for predicting the crack path in brittle materials
S. Goswami
Minglang Yin
Yue Yu
G. Karniadakis
AI4CE
237
285
0
16 Aug 2021
Deep learning for solution and inversion of structural mechanics and vibrations
E. Haghighat
A. Bekar
E. Madenci
R. Juanes
PINN
AI4CE
254
14
0
18 May 2021
An overview on deep learning-based approximation methods for partial differential equations
C. Beck
Martin Hutzenthaler
Arnulf Jentzen
Benno Kuckuck
693
175
0
22 Dec 2020
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization
Rishikesh Ranade
C. Hill
Jay Pathak
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361
151
0
17 May 2020
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