Sledovat
George Em Karniadakis
George Em Karniadakis
The Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering
E-mailová adresa ověřena na: brown.edu - Domovská stránka
Název
Citace
Citace
Rok
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M Raissi, P Perdikaris, GE Karniadakis
Journal of Computational physics 378, 686-707, 2019
135842019
The Wiener--Askey polynomial chaos for stochastic differential equations
D Xiu, GE Karniadakis
SIAM journal on scientific computing 24 (2), 619-644, 2002
60532002
Physics-informed machine learning
GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang
Nature Reviews Physics 3 (6), 422-440, 2021
53432021
Microflows and nanoflows: fundamentals and simulation
G Karniadakis, A Beskok, N Aluru
Springer Science & Business Media, 2006
4172*2006
Spectral/hp element methods for computational fluid dynamics
G Karniadakis, SJ Sherwin
Oxford University Press, 2005
36542005
Discontinuous Galerkin methods: theory, computation and applications
B Cockburn, GE Karniadakis, CW Shu
Springer Science & Business Media, 2012
3112*2012
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
L Lu, P Jin, G Pang, Z Zhang, GE Karniadakis
Nature machine intelligence 3 (3), 218-229, 2021
23172021
DeepXDE: A deep learning library for solving differential equations
L Lu, X Meng, Z Mao, GE Karniadakis
SIAM review 63 (1), 208-228, 2021
21212021
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
M Raissi, A Yazdani, GE Karniadakis
Science 367 (6481), 1026-1030, 2020
18622020
High-order splitting methods for the incompressible Navier-Stokes equations
GE Karniadakis, M Israeli, SA Orszag
Journal of computational physics 97 (2), 414-443, 1991
18411991
Modeling uncertainty in flow simulations via generalized polynomial chaos
D Xiu, GE Karniadakis
Journal of computational physics 187 (1), 137-167, 2003
18312003
Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations
M Raissi, P Perdikaris, GE Karniadakis
arXiv preprint arXiv:1711.10561, 2017
17142017
Report: a model for flows in channels, pipes, and ducts at micro and nano scales
A Beskok, GE Karniadakis
Microscale thermophysical engineering 3 (1), 43-77, 1999
15651999
Hidden physics models: Machine learning of nonlinear partial differential equations
M Raissi, GE Karniadakis
Journal of Computational Physics 357, 125-141, 2018
14482018
Physics-informed neural networks (PINNs) for fluid mechanics: A review
S Cai, Z Mao, Z Wang, M Yin, GE Karniadakis
Acta Mechanica Sinica 37 (12), 1727-1738, 2021
14422021
Physics-informed neural networks for high-speed flows
Z Mao, AD Jagtap, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 360, 112789, 2020
11412020
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
X Jin, S Cai, H Li, GE Karniadakis
Journal of Computational Physics 426, 109951, 2021
10872021
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
AD Jagtap, K Kawaguchi, GE Karniadakis
Journal of Computational Physics 404, 109136, 2020
1029*2020
Physics-informed neural networks for heat transfer problems
S Cai, Z Wang, S Wang, P Perdikaris, GE Karniadakis
Journal of Heat Transfer 143 (6), 060801, 2021
9302021
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
AD Jagtap, E Kharazmi, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 365, 113028, 2020
9202020
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Články 1–20