George Em Karniadakis

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

Verificeret mail på brown.edu - Startside

Math+Machine Learning Probabilistic Scientific Computing Stochastic Multiscale Modeling


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
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	13057	2019
The Wiener--Askey polynomial chaos for stochastic differential equations D Xiu, GE Karniadakis SIAM journal on scientific computing 24 (2), 619-644, 2002	6010	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	5069	2021
Microflows and nanoflows: fundamentals and simulation G Karniadakis, A Beskok, N Aluru Springer Science & Business Media, 2006	4159*	2006
Spectral/hp element methods for computational fluid dynamics G Karniadakis, SJ Sherwin Oxford University Press, USA, 2005	3629	2005
Discontinuous Galerkin methods: theory, computation and applications B Cockburn, GE Karniadakis, CW Shu Springer Science & Business Media, 2012	3098*	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	2180	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	2058	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	1837	1991
Modeling uncertainty in flow simulations via generalized polynomial chaos D Xiu, GE Karniadakis Journal of computational physics 187 (1), 137-167, 2003	1819	2003
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations M Raissi, A Yazdani, GE Karniadakis Science 367 (6481), 1026-1030, 2020	1813	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	1664	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	1554	1999
Hidden physics models: Machine learning of nonlinear partial differential equations M Raissi, GE Karniadakis Journal of Computational Physics 357, 125-141, 2018	1430	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	1357	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	1114	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	1067	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	1005*	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	895	2021
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	891	2020

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