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	11745	2019
The Wiener--Askey polynomial chaos for stochastic differential equations D Xiu, GE Karniadakis SIAM journal on scientific computing 24 (2), 619-644, 2002	5927	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	4499	2021
Microflows and nanoflows: fundamentals and simulation G Karniadakis, A Beskok, N Aluru Springer Science & Business Media, 2006	4129*	2006
Spectral/hp element methods for computational fluid dynamics G Karniadakis, SJ Sherwin Oxford University Press, USA, 2005	3576	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	1911	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	1872	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	1815	1991
Modeling uncertainty in flow simulations via generalized polynomial chaos D Xiu, GE Karniadakis Journal of computational physics 187 (1), 137-167, 2003	1799	2003
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations M Raissi, A Yazdani, GE Karniadakis Science 367 (6481), 1026-1030, 2020	1666	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	1565	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	1547	1999
Hidden physics models: Machine learning of nonlinear partial differential equations M Raissi, GE Karniadakis Journal of Computational Physics 357, 125-141, 2018	1360	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	1165	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	1007	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	968	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	922*	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	811	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	808	2021

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