George Em Karniadakis

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Ali BeskokSouthern Methodist UniversityAdresse e-mail validée de smu.edu
Michael S. TriantafyllouHenry L. and Grace Doherty Professor of Ocean Science & Engineering, MITAdresse e-mail validée de mit.edu
Igor V. PivkinProfessor of Computational Science, USI Lugano and Swiss Institute of Bioinformatics, SwitzerlandAdresse e-mail validée de usi.ch
Spencer J. SherwinProfessor of Computational Fluid Mechanics, Imperial College LondonAdresse e-mail validée de imperial.ac.uk
Dongbin XiuProfessor of Mathematics, The Ohio State UniversityAdresse e-mail validée de osu.edu
Xuejin Li (李学进)Zhejiang UniversityAdresse e-mail validée de zju.edu.cn
Suchuan DongDepartment of Mathematics, Purdue UniversityAdresse e-mail validée de math.purdue.edu
Didier LucorSenior researcher, CNRS, LISN, Orsay, FranceAdresse e-mail validée de lisn.upsaclay.fr
Xiaoliang WanLouisiana State UniversityAdresse e-mail validée de math.lsu.edu
Robert M. Kirby OR R.M. KirbySchool of Computing, University of UtahAdresse e-mail validée de cs.utah.edu
Mohsen ZayernouriAssociate Professor at Michigan State UniversityAdresse e-mail validée de msu.edu
Wenxiao PanAssociate Professor of Mechanical Engineering, University of Wisconsin-MadisonAdresse e-mail validée de wisc.edu
Bernardo CockburnProfessor of Mathematics, University of MinnesotaAdresse e-mail validée de math.umn.edu
George S. TriantafyllouProfessor of HydrodynamicsAdresse e-mail validée de deslab.ntua.gr
Timothy WarburtonJohn K. Costain Faculty Chair, Virginia Polytechnic Institute and State UniversityAdresse e-mail validée de vt.edu
Jasmine FooProfessor of Mathematics, University of MinnesotaAdresse e-mail validée de umn.edu
Leopold GrinbergAMD

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George Em Karniadakis

The Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering

Adresse e-mail validée de brown.edu - Page d'accueil

Math+Machine Learning Probabilistic Scientific Computing Stochastic Multiscale Modeling


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	11940	2019
The Wiener--Askey polynomial chaos for stochastic differential equations D Xiu, GE Karniadakis SIAM journal on scientific computing 24 (2), 619-644, 2002	5932	2002
Physics-informed machine learning GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang Nature Reviews Physics 3 (6), 422-440, 2021	4582	2021
Microflows and nanoflows: fundamentals and simulation G Karniadakis, A Beskok, N Aluru Springer Science & Business Media, 2006	4130*	2006
Spectral/hp element methods for computational fluid dynamics G Karniadakis, SJ Sherwin Oxford University Press, USA, 2005	3585	2005
Discontinuous Galerkin methods: theory, computation and applications B Cockburn, GE Karniadakis, CW Shu Springer Science & Business Media, 2012	3065*	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	1947	2021
DeepXDE: A deep learning library for solving differential equations L Lu, X Meng, Z Mao, GE Karniadakis SIAM review 63 (1), 208-228, 2021	1897	2021
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	1819	1991
Modeling uncertainty in flow simulations via generalized polynomial chaos D Xiu, GE Karniadakis Journal of computational physics 187 (1), 137-167, 2003	1798	2003
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations M Raissi, A Yazdani, GE Karniadakis Science 367 (6481), 1026-1030, 2020	1690	2020
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	1581	2017
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	1550	1999
Hidden physics models: Machine learning of nonlinear partial differential equations M Raissi, GE Karniadakis Journal of Computational Physics 357, 125-141, 2018	1366	2018
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	1197	2021
Physics-informed neural networks for high-speed flows Z Mao, AD Jagtap, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 360, 112789, 2020	1025	2020
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	980	2021
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	936*	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	820	2020
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data L Yang, X Meng, GE Karniadakis Journal of Computational Physics 425, 109913, 2021	817	2021

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