George Em Karniadakis

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

Geverifieerd e-mailadres voor brown.edu - Homepage

Math+Machine Learning Probabilistic Scientific Computing Stochastic Multiscale Modeling


Titel Sorteren op citaties Sorteren op jaar Sorteren op titel	Geciteerd door Geciteerd door	Jaar
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	10894	2019
The Wiener--Askey polynomial chaos for stochastic differential equations D Xiu, GE Karniadakis SIAM journal on scientific computing 24 (2), 619-644, 2002	5861	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	4140	2021
Microflows and nanoflows: fundamentals and simulation G Karniadakis, A Beskok, N Aluru Springer Science & Business Media, 2006	4096*	2006
Spectral/hp element methods for computational fluid dynamics G Karniadakis, SJ Sherwin Oxford University Press, USA, 2005	3542	2005
Discontinuous Galerkin methods: theory, computation and applications B Cockburn, GE Karniadakis, CW Shu Springer Science & Business Media, 2012	3032*	2012
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	1800	1991
Modeling uncertainty in flow simulations via generalized polynomial chaos D Xiu, GE Karniadakis Journal of computational physics 187 (1), 137-167, 2003	1785	2003
DeepXDE: A deep learning library for solving differential equations L Lu, X Meng, Z Mao, GE Karniadakis SIAM review 63 (1), 208-228, 2021	1767	2021
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	1719	2021
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations M Raissi, A Yazdani, GE Karniadakis Science 367 (6481), 1026-1030, 2020	1582	2020
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	1529	1999
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	1500	2017
Hidden physics models: Machine learning of nonlinear partial differential equations M Raissi, GE Karniadakis Journal of Computational Physics 357, 125-141, 2018	1314	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	1047	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	954	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	899	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	857*	2020
fPINNs: Fractional physics-informed neural networks G Pang, L Lu, GE Karniadakis SIAM Journal on Scientific Computing 41 (4), A2603-A2626, 2019	759	2019
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	756	2020

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