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George Em Karniadakis
George Em Karniadakis
The Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering
Bestätigte E-Mail-Adresse bei brown.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
128542019
The Wiener--Askey polynomial chaos for stochastic differential equations
D Xiu, GE Karniadakis
SIAM journal on scientific computing 24 (2), 619-644, 2002
59932002
Physics-informed machine learning
GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang
Nature Reviews Physics 3 (6), 422-440, 2021
49962021
Microflows and nanoflows: fundamentals and simulation
G Karniadakis, A Beskok, N Aluru
Springer Science & Business Media, 2006
4151*2006
Spectral/hp element methods for computational fluid dynamics
G Karniadakis, SJ Sherwin
Oxford University Press, USA, 2005
36222005
Discontinuous Galerkin methods: theory, computation and applications
B Cockburn, GE Karniadakis, CW Shu
Springer Science & Business Media, 2012
3093*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
21372021
DeepXDE: A deep learning library for solving differential equations
L Lu, X Meng, Z Mao, GE Karniadakis
SIAM review 63 (1), 208-228, 2021
20312021
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
18331991
Modeling uncertainty in flow simulations via generalized polynomial chaos
D Xiu, GE Karniadakis
Journal of computational physics 187 (1), 137-167, 2003
18112003
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
M Raissi, A Yazdani, GE Karniadakis
Science 367 (6481), 1026-1030, 2020
17972020
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
16542017
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
15551999
Hidden physics models: Machine learning of nonlinear partial differential equations
M Raissi, GE Karniadakis
Journal of Computational Physics 357, 125-141, 2018
14232018
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
13342021
Physics-informed neural networks for high-speed flows
Z Mao, AD Jagtap, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 360, 112789, 2020
11012020
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
10572021
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
994*2020
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
8772020
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
8762021
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