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ORCIDJan S. Hesthaven
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- affiliation: EPFL, Lausanne, Switzerland
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Journal Articles
- 2024
[j83]Anders Melander
, Emil Strøm, Finnur Pind, Allan P. Engsig-Karup, Cheol-Ho Jeong, Tim Warburton, Noel Chalmers, Jan S. Hesthaven:
Massively parallel nodal discontinous Galerkin finite element method simulator for room acoustics. Int. J. High Perform. Comput. Appl. 38(3): 154-174 (2024)- [j82]Junming Duan, Jan S. Hesthaven:
Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems. J. Comput. Phys. 497: 112621 (2024) - [j81]Federico Pichi, Beatriz Moya, Jan S. Hesthaven:
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs. J. Comput. Phys. 501: 112762 (2024) - [j80]Mariella Kast, Jan S. Hesthaven:
Positional embeddings for solving PDEs with evolutional deep neural networks. J. Comput. Phys. 508: 112986 (2024) - [j79]Marco Sutti, Jan S. Hesthaven:
Perfectly matched layers for the Boltzmann equation: Stability and sensitivity analysis. J. Comput. Phys. 509: 113047 (2024) - 2023
- [j78]Fatemeh Nassajian Mojarrad, Maria Han Veiga, Jan S. Hesthaven, Philipp Öffner:
A new variable shape parameter strategy for RBF approximation using neural networks. Comput. Math. Appl. 143: 151-168 (2023) - [j77]Jan S. Hesthaven, Cecilia Pagliantini, Nicolò Ripamonti:
Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equation. Math. Comput. 93(347): 1153-1202 (2023) - 2022
- [j76]Jan S. Hesthaven, Cecilia Pagliantini, Gianluigi Rozza:
Reduced basis methods for time-dependent problems. Acta Numer. 31: 265-345 (2022) - [j75]Jian Yu, Jan S. Hesthaven:
Model order reduction for compressible flows solved using the discontinuous Galerkin methods. J. Comput. Phys. 468: 111452 (2022) - [j74]Oscar P. Bruno, Jan S. Hesthaven, Daniel V. Leibovici:
FC-based shock-dynamics solver with neural-network localized artificial-viscosity assignment. J. Comput. Phys. X 15: 100110 (2022) - [j73]Przemyslaw Zielinski, Jan S. Hesthaven:
Discovery of Slow Variables in a Class Of Multiscale Stochastic Systems Via Neural Networks. J. Nonlinear Sci. 32(4): 51 (2022) - [j72]Boris Bonev, Jan S. Hesthaven:
A Hierarchical Preconditioner for Wave Problems in Quasilinear Complexity. SIAM J. Sci. Comput. 44(1): 198- (2022) - 2021
- [j71]Qifeng Zhang, Jan S. Hesthaven, Zhi-Zhong Sun, Yunzhu Ren:
Pointwise error estimate in difference setting for the two-dimensional nonlinear fractional complex Ginzburg-Landau equation. Adv. Comput. Math. 47(3): 35 (2021) - [j70]Lukas Schwander, Deep Ray, Jan S. Hesthaven:
Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks. J. Comput. Phys. 431: 110144 (2021) - [j69]Wenqian Chen, Qian Wang, Jan S. Hesthaven, Chuhua Zhang:
Physics-informed machine learning for reduced-order modeling of nonlinear problems. J. Comput. Phys. 446: 110666 (2021) - [j68]Jan S. Hesthaven, Fabian Mönkeberg:
Hybrid high-resolution RBF-ENO method. J. Comput. Phys. X 12: 100089 (2021) - [j67]Jan S. Hesthaven, Cecilia Pagliantini:
Structure-preserving reduced basis methods for Poisson systems. Math. Comput. 90(330): 1701-1740 (2021) - 2020
- [j66]Niccolò Discacciati, Jan S. Hesthaven, Deep Ray:
Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks. J. Comput. Phys. 409: 109304 (2020) - [j65]Jim Magiera, Deep Ray, Jan S. Hesthaven, Christian Rohde:
Constraint-aware neural networks for Riemann problems. J. Comput. Phys. 409: 109345 (2020) - [j64]Qian Wang, Nicolò Ripamonti, Jan S. Hesthaven:
Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism. J. Comput. Phys. 410: 109402 (2020) - [j63]Wenrui Hao, Jan S. Hesthaven, Guang Lin, Bin Zheng:
A Homotopy Method with Adaptive Basis Selection for Computing Multiple Solutions of Differential Equations. J. Sci. Comput. 82(1): 19 (2020) - [j62]Jan S. Hesthaven, Fabian Mönkeberg:
Two-Dimensional RBF-ENO Method on Unstructured Grids. J. Sci. Comput. 82(3): 76 (2020) - [j61]Xiao Wen, Wai Sun Don, Zhen Gao, Jan S. Hesthaven:
An Edge Detector Based on Artificial Neural Network with Application to Hybrid Compact-WENO Finite Difference Scheme. J. Sci. Comput. 83(3): 49 (2020) - 2019
- [j60]Paul Cazeaux, Jan S. Hesthaven:
Projective multiscale time-integration for electrostatic particle-in-cell methods. Comput. Phys. Commun. 236: 34-50 (2019) - [j59]Qian Wang, Jan S. Hesthaven, Deep Ray:
Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem. J. Comput. Phys. 384: 289-307 (2019) - [j58]Ali Karakus, Noel Chalmers, Jan S. Hesthaven, Tim Warburton:
Discontinuous Galerkin discretizations of the Boltzmann-BGK equations for nearly incompressible flows: Semi-analytic time stepping and absorbing boundary layers. J. Comput. Phys. 390: 175-202 (2019) - [j57]Deep Ray, Jan S. Hesthaven:
Detecting troubled-cells on two-dimensional unstructured grids using a neural network. J. Comput. Phys. 397 (2019) - [j56]Babak Maboudi Afkham, Jan S. Hesthaven:
Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems. J. Sci. Comput. 81(1): 3-21 (2019) - 2018
- [j55]Boris Bonev, Jan S. Hesthaven, Francis X. Giraldo, Michal A. Kopera:
Discontinuous Galerkin scheme for the spherical shallow water equations with applications to tsunami modeling and prediction. J. Comput. Phys. 362: 425-448 (2018) - [j54]Jan S. Hesthaven, Stefano Ubbiali:
Non-intrusive reduced order modeling of nonlinear problems using neural networks. J. Comput. Phys. 363: 55-78 (2018) - [j53]Deep Ray, Jan S. Hesthaven:
An artificial neural network as a troubled-cell indicator. J. Comput. Phys. 367: 166-191 (2018) - [j52]Allan S. Nielsen, Gilles Brunner, Jan S. Hesthaven:
Communication-aware adaptive Parareal with application to a nonlinear hyperbolic system of partial differential equations. J. Comput. Phys. 371: 483-505 (2018) - 2017
- [j51]Daniel Baffet, Jan S. Hesthaven:
High-Order Accurate Local Schemes for Fractional Differential Equations. J. Sci. Comput. 70(1): 355-385 (2017) - [j50]Paolo Gatto, Jan S. Hesthaven:
Efficient Preconditioning of hp-FEM Matrices by Hierarchical Low-Rank Approximations. J. Sci. Comput. 72(1): 49-80 (2017) - [j49]Caterina Bigoni, Jan S. Hesthaven:
Adaptive WENO Methods Based on Radial Basis Function Reconstruction. J. Sci. Comput. 72(3): 986-1020 (2017) - [j48]Daniel Baffet, Jan S. Hesthaven:
High-Order Accurate Adaptive Kernel Compression Time-Stepping Schemes for Fractional Differential Equations. J. Sci. Comput. 72(3): 1169-1195 (2017) - [j47]Daniel Baffet, Jan S. Hesthaven:
A Kernel Compression Scheme for Fractional Differential Equations. SIAM J. Numer. Anal. 55(2): 496-520 (2017) - [j46]Babak Maboudi Afkham, Jan S. Hesthaven:
Structure Preserving Model Reduction of Parametric Hamiltonian Systems. SIAM J. Sci. Comput. 39(6) (2017) - 2016
- [j45]Jan S. Hesthaven, Shun Zhang:
On the Use of ANOVA Expansions in Reduced Basis Methods for Parametric Partial Differential Equations. J. Sci. Comput. 69(1): 292-313 (2016) - 2015
- [j44]George E. Karniadakis, Jan S. Hesthaven, Igor Podlubny:
Special Issue on "Fractional PDEs: Theory, Numerics, and Applications". J. Comput. Phys. 293: 1-3 (2015) - [j43]Feng Chen, Qinwu Xu, Jan S. Hesthaven:
A multi-domain spectral method for time-fractional differential equations. J. Comput. Phys. 293: 157-172 (2015) - [j42]Qinwu Xu, Jan S. Hesthaven, Feng Chen:
A parareal method for time-fractional differential equations. J. Comput. Phys. 293: 173-183 (2015) - [j41]Liangliang Qiu, Weihua Deng, Jan S. Hesthaven:
Nodal discontinuous Galerkin methods for fractional diffusion equations on 2D domain with triangular meshes. J. Comput. Phys. 298: 678-694 (2015) - [j40]Jan S. Hesthaven, Jae-Hun Jung, Allen M. Tesdall:
Hyperbolic Problems: Theory and Computation. J. Sci. Comput. 64(3): 587-590 (2015) - [j39]Paolo Gatto, Jan S. Hesthaven:
Numerical Approximation of the Fractional Laplacian via hp-finite Elements, with an Application to Image Denoising. J. Sci. Comput. 65(1): 249-270 (2015) - [j38]Jan S. Hesthaven, Shun Zhang, Xueyu Zhu:
Reduced Basis Multiscale Finite Element Methods for Elliptic Problems. Multiscale Model. Simul. 13(1): 316-337 (2015) - [j37]Jens Zudrop, Jan S. Hesthaven:
Accuracy of High Order and Spectral Methods for Hyperbolic Conservation Laws with Discontinuous Solutions. SIAM J. Numer. Anal. 53(4): 1857-1875 (2015) - 2014
- [j36]Qinwu Xu, Jan S. Hesthaven:
Stable multi-domain spectral penalty methods for fractional partial differential equations. J. Comput. Phys. 257: 241-258 (2014) - [j35]Jichun Li, Jan S. Hesthaven:
Analysis and application of the nodal discontinuous Galerkin method for wave propagation in metamaterials. J. Comput. Phys. 258: 915-930 (2014) - [j34]Jan S. Hesthaven, Shun Zhang, Xueyu Zhu:
High-Order Multiscale Finite Element Method for Elliptic Problems. Multiscale Model. Simul. 12(2): 650-666 (2014) - [j33]Qinwu Xu, Jan S. Hesthaven:
Discontinuous Galerkin Method for Fractional Convection-Diffusion Equations. SIAM J. Numer. Anal. 52(1): 405-423 (2014) - 2013
- [j32]Akil C. Narayan, Jan S. Hesthaven:
A generalization of the Wiener rational basis functions on infinite intervals, Part II - Numerical investigation. J. Comput. Appl. Math. 237(1): 18-34 (2013) - [j31]Khosro Shahbazi, Jan S. Hesthaven, Xueyu Zhu:
Multi-dimensional hybrid Fourier continuation-WENO solvers for conservation laws. J. Comput. Phys. 253: 209-225 (2013) - 2012
- [j30]Mahadevan Ganesh, Jan S. Hesthaven, Benjamin Stamm:
A reduced basis method for electromagnetic scattering by multiple particles in three dimensions. J. Comput. Phys. 231(23): 7756-7779 (2012) - [j29]Jan S. Hesthaven, Benjamin Stamm, S. Zhang:
Certified Reduced Basis Method for the Electric Field Integral Equation. SIAM J. Sci. Comput. 34(3) (2012) - 2011
- [j28]M. Fares, Jan S. Hesthaven, Yvon Maday, Benjamin Stamm:
The reduced basis method for the electric field integral equation. J. Comput. Phys. 230(14): 5532-5555 (2011) - [j27]Khosro Shahbazi, Nathan Albin, Oscar P. Bruno, Jan S. Hesthaven:
Multi-domain Fourier-continuation/WENO hybrid solver for conservation laws. J. Comput. Phys. 230(24): 8779-8796 (2011) - [j26]Akil Narayan, Jan S. Hesthaven:
A generalization of the Wiener rational basis functions on infinite intervals: Part I-derivation and properties. Math. Comput. 80(275): 1557-1583 (2011) - 2010
- [j25]Zhen Gao, Jan S. Hesthaven:
On ANOVA expansions and strategies for choosing the anchor point. Appl. Math. Comput. 217(7): 3274-3285 (2010) - [j24]Sehun Chun, Houssem Haddar, Jan S. Hesthaven:
High-order accurate thin layer approximations for time-domain electromagnetics, Part II: Transmission layers. J. Comput. Appl. Math. 234(8): 2587-2608 (2010) - [j23]Yanlai Chen, Jan S. Hesthaven, Yvon Maday, Jerónimo Rodríguez:
Certified Reduced Basis Methods and Output Bounds for the Harmonic Maxwell's Equations. SIAM J. Sci. Comput. 32(2): 970-996 (2010) - 2009
- [j22]Gustaaf B. Jacobs, Jan S. Hesthaven:
Implicit-explicit time integration of a high-order particle-in-cell method with hyperbolic divergence cleaning. Comput. Phys. Commun. 180(10): 1760-1767 (2009) - [j21]Sehun Chun, Jan S. Hesthaven:
High-order accurate thin layer approximations for time-domain electromagnetics. Part I: General metal backed coatings. J. Comput. Appl. Math. 231(2): 598-611 (2009) - [j20]Gregor Gassner, Frieder Lörcher, Claus-Dieter Munz, Jan S. Hesthaven:
Polymorphic nodal elements and their application in discontinuous Galerkin methods. J. Comput. Phys. 228(5): 1573-1590 (2009) - [j19]Andreas Klöckner, Tim Warburton, Jeff Bridge, Jan S. Hesthaven:
Nodal discontinuous Galerkin methods on graphics processors. J. Comput. Phys. 228(21): 7863-7882 (2009) - 2008
- [j18]Jan S. Hesthaven, Robert M. Kirby:
Filtering in Legendre spectral methods. Math. Comput. 77(263): 1425-1452 (2008) - 2007
- [j17]Alex Kanevsky, Mark H. Carpenter, David I. Gottlieb, Jan S. Hesthaven:
Application of implicit-explicit high order Runge-Kutta methods to discontinuous-Galerkin schemes. J. Comput. Phys. 225(2): 1753-1781 (2007) - 2006
- [j16]Gustaaf B. Jacobs, Jan S. Hesthaven:
High-order nodal discontinuous Galerkin particle-in-cell method on unstructured grids. J. Comput. Phys. 214(1): 96-121 (2006) - [j15]Alex Kanevsky, Mark H. Carpenter, Jan S. Hesthaven:
Idempotent filtering in spectral and spectral element methods. J. Comput. Phys. 220(1): 41-58 (2006) - [j14]David I. Gottlieb, Jan S. Hesthaven, George E. Karniadakis, Chi-Wang Shu:
Foreword. J. Sci. Comput. 27(1-3): 1-3 (2006) - [j13]Saul Abarbanel, David I. Gottlieb, Jan S. Hesthaven:
Non-Linear PML Equations for Time Dependent Electromagnetics in Three Dimensions. J. Sci. Comput. 28(2-3): 125-137 (2006) - [j12]Jan S. Hesthaven, Sidi Mahmoud Kaber, Laura B. Lurati:
Padé-Legendre Interpolants for Gibbs Reconstruction. J. Sci. Comput. 28(2-3): 337-359 (2006) - [j11]Cédric Chauvière, Jan S. Hesthaven, Laura B. Lurati:
Computational Modeling of Uncertainty in Time-Domain Electromagnetics. SIAM J. Sci. Comput. 28(2): 751-775 (2006) - 2005
- [j10]Mark H. Carpenter, David I. Gottlieb, Jan S. Hesthaven, Chi-Wang Shu:
Foreword. J. Sci. Comput. 25(1-2): 1-2 (2005) - [j9]Q. Y. Chen, David I. Gottlieb, Jan S. Hesthaven:
Spectral Methods Based on Prolate Spheroidal Wave Functions for Hyperbolic PDEs. SIAM J. Numer. Anal. 43(5): 1912-1933 (2005) - [j8]Dongbin Xiu, Jan S. Hesthaven:
High-Order Collocation Methods for Differential Equations with Random Inputs. SIAM J. Sci. Comput. 27(3): 1118-1139 (2005) - 2002
- [j7]Saul Abarbanel, David I. Gottlieb, Jan S. Hesthaven:
Long Time Behavior of the Perfectly Matched Layer Equations in Computational Electromagnetics. J. Sci. Comput. 17(1-4): 405-422 (2002) - [j6]Guo-Xin Fan, Qing Huo Liu, Jan S. Hesthaven:
Multidomain pseudospectral time-domain simulations of scattering by objects buried in lossy media. IEEE Trans. Geosci. Remote. Sens. 40(6): 1366-1373 (2002) - 2001
- [j5]I. Fatkullin, Jan S. Hesthaven:
Adaptive High-Order Finite-Difference Method for Nonlinear Wave Problems. J. Sci. Comput. 16(1): 47-67 (2001) - 2000
- [j4]Jan S. Hesthaven, C. H. Teng:
Stable Spectral Methods on Tetrahedral Elements. SIAM J. Sci. Comput. 21(6): 2352-2380 (2000) - 1998
- [j3]Jan S. Hesthaven:
A Stable Penalty Method for the Compressible Navier-Stokes Equations: III. Multidimensional Domain Decomposition Schemes. SIAM J. Sci. Comput. 20(1): 62-93 (1998) - 1997
- [j2]Jan S. Hesthaven:
A Stable Penalty Method for the Compressible Navier-Stokes Equations: II. One-Dimensional Domain Decomposition Schemes. SIAM J. Sci. Comput. 18(3): 658-685 (1997) - 1996
- [j1]Jan S. Hesthaven, David I. Gottlieb:
A Stable Penalty Method for the Compressible Navier-Stokes Equations: I. Open Boundary Conditions. SIAM J. Sci. Comput. 17(3): 579-612 (1996)
Conference and Workshop Papers
- 2016
- [c1]Allan S. Nielsen, Jan S. Hesthaven:
Fault Tolerance in the Parareal Method. FTXS@HPDC 2016: 1-8
Editorship
- 2021
- [e1]Joan Bruna, Jan S. Hesthaven, Lenka Zdeborová:
Mathematical and Scientific Machine Learning, 16-19 August 2021, Virtual Conference / Lausanne, Switzerland. Proceedings of Machine Learning Research 145, PMLR 2021 [contents]
Informal and Other Publications
- 2024
- [i27]Fernando Henríquez, Jan S. Hesthaven:
Fast Numerical Approximation of Parabolic Problems Using Model Order Reduction and the Laplace Transform. CoRR abs/2403.02847 (2024) - [i26]Nicola Rares Franco, Andrea Manzoni, Paolo Zunino, Jan S. Hesthaven:
Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reduction. CoRR abs/2404.18841 (2024) - [i25]Fernando Henríquez, Jan S. Hesthaven:
Fast Numerical Approximation of Linear, Second-Order Hyperbolic Problems Using Model Order Reduction and the Laplace Transform. CoRR abs/2405.19896 (2024) - [i24]Max Hirsch, Federico Pichi, Jan S. Hesthaven:
Neural empirical interpolation method for nonlinear model reduction. CoRR abs/2406.03562 (2024) - [i23]Oisín M. Morrison, Federico Pichi, Jan S. Hesthaven:
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications. CoRR abs/2406.03569 (2024) - [i22]Philipp Weder, Mariella Kast, Fernando Henríquez, Jan S. Hesthaven:
Galerkin Neural Network-POD for Acoustic and Electromagnetic Wave Propagation in Parametric Domains. CoRR abs/2406.13567 (2024) - [i21]Matteo Caldana, Jan S. Hesthaven:
Neural Ordinary Differential Equations for Model Order Reduction of Stiff Systems. CoRR abs/2408.06073 (2024) - 2023
- [i20]Junming Duan, Jan S. Hesthaven:
Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems. CoRR abs/2303.02986 (2023) - [i19]Federico Pichi, Beatriz Moya, Jan S. Hesthaven:
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs. CoRR abs/2305.08573 (2023) - [i18]Junming Duan, Qian Wang, Jan S. Hesthaven:
Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs. CoRR abs/2305.09199 (2023) - [i17]Mariella Kast, Jan S. Hesthaven:
Positional Embeddings for Solving PDEs with Evolutional Deep Neural Networks. CoRR abs/2308.03461 (2023) - [i16]Marco Sutti, Jan S. Hesthaven:
Perfectly matched layers for the Boltzmann equation: stability and sensitivity analysis. CoRR abs/2312.03273 (2023) - 2022
- [i15]Jan S. Hesthaven, Cecilia Pagliantini, Nicolò Ripamonti:
Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equation. CoRR abs/2201.05555 (2022) - [i14]Paolo Conti, Mengwu Guo, Andrea Manzoni, Jan S. Hesthaven:
Multi-fidelity surrogate modeling using long short-term memory networks. CoRR abs/2208.03115 (2022) - [i13]Fatemeh Nassajian Mojarrad, Maria Han Veiga, Jan S. Hesthaven, Philipp Öffner:
A new variable shape parameter strategy for RBF approximation using neural networks. CoRR abs/2210.16945 (2022) - 2021
- [i12]Mengwu Guo, Andrea Manzoni, Maurice Amendt, Paolo Conti, Jan S. Hesthaven:
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities. CoRR abs/2102.13403 (2021) - [i11]Hermes Sampedro Llopis, Allan P. Engsig-Karup, Cheol-Ho Jeong, Finnur Pind, Jan S. Hesthaven:
Efficient numerical room acoustic simulations with parametrized boundaries using the spectral element and reduced basis method. CoRR abs/2103.11730 (2021) - [i10]Przemyslaw Zielinski, Jan S. Hesthaven:
Discovery of slow variables in a class of multiscale stochastic systems via neural networks. CoRR abs/2104.13911 (2021) - [i9]Boris Bonev, Jan S. Hesthaven:
A hierarchical preconditioner for wave problems in quasilinear complexity. CoRR abs/2105.07791 (2021) - [i8]Federico Pichi, Francesco Ballarin, Gianluigi Rozza, Jan S. Hesthaven:
An artificial neural network approach to bifurcating phenomena in computational fluid dynamics. CoRR abs/2109.10765 (2021) - [i7]Jan S. Hesthaven, Cecilia Pagliantini, Nicolò Ripamonti:
Structure-preserving model order reduction of Hamiltonian systems. CoRR abs/2109.12367 (2021) - [i6]Oscar P. Bruno, Jan S. Hesthaven, Daniel V. Leibovici:
FC-based shock-dynamics solver with neural-network localized artificial-viscosity assignment. CoRR abs/2111.01315 (2021) - 2020
- [i5]Jan S. Hesthaven, Cecilia Pagliantini, Nicolò Ripamonti:
Rank-adaptive structure-preserving reduced basis methods for Hamiltonian systems. CoRR abs/2007.13153 (2020) - 2016
- [i4]Ulrich Rüde, Karen Willcox, Lois Curfman McInnes, Hans De Sterck, George Biros, Hans-Joachim Bungartz, James Corones, Evin Cramer, James Crowley, Omar Ghattas, Max D. Gunzburger, Michael Hanke, Robert J. Harrison, Michael A. Heroux, Jan S. Hesthaven, Peter K. Jimack, Chris Johnson, Kirk E. Jordan, David E. Keyes, Rolf H. Krause, Vipin Kumar, Stefan Mayer, Juan Meza, Knut Martin Mørken, J. Tinsley Oden, Linda R. Petzold, Padma Raghavan, Suzanne M. Shontz, Anne E. Trefethen, Peter R. Turner, Vladimir V. Voevodin, Barbara I. Wohlmuth, Carol S. Woodward:
Research and Education in Computational Science and Engineering. CoRR abs/1610.02608 (2016) - 2013
- [i3]Andreas Klöckner, Timothy Warburton, Jan S. Hesthaven:
Solving Wave Equations on Unstructured Geometries. CoRR abs/1304.5546 (2013) - [i2]Scott E. Field, Chad R. Galley, Jan S. Hesthaven, Jason Kaye, Manuel Tiglio:
Fast prediction and evaluation of gravitational waveforms using surrogate models. CoRR abs/1308.3565 (2013) - 2012
- [i1]Andreas Klöckner, Timothy Warburton, Jan S. Hesthaven:
High-Order Discontinuous Galerkin Methods by GPU Metaprogramming. CoRR abs/1211.0582 (2012)
Coauthor Index
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