Jan S. Hesthaven
Doyen de la faculté SB
jan.hesthaven@epfl.ch +41 21 693 33 02 https://www.epfl.ch/labs/mcss/
EPFL SB-DO
PH A2 364 (Bâtiment PH)
Station 3
CH-1015 Lausanne
+41 21 693 33 02
+41 21 693 03 51
Local: PH A2 364
EPFL > SB > SB-DEC > SB-DO
Web site: Site web: https://www.epfl.ch/schools/sb/about/deans-office/
EPFL SB MATH MCSS
MA C2 652 (Bâtiment MA)
Station 8
CH-1015 Lausanne
+41 21 693 03 51
+41 21 693 58 42
Local: MA C2 652
EPFL > SB > MATH > MCSS
Web site: Site web: https://mcss.epfl.ch/
+41 21 693 03 51
EPFL > ENT-R > SCITAS > SCITAS-CD
Domaines de compétences
The research activities focuses on the development, analysis and application of high-order accurate computational methods for time-dependent partial differential equations with a particular emphasis on linear and nonlinear wave problems. This has and continues to included research activities in discontinuous Galerkin and spectral methods, certificed reduced basis methods, methods for uncertainty quantification, methods for multiscale problems in time and space, efficient multilevel solvers and fractional differential equations. A major recent activity is the use of machine learning techniques in computational science with an emphasis on methods that preserve physical characteristics or the use of local neural networks to accelerate existing methods rather than replacing them.
While the emphasis in on the development and analysis of new methods and algorithms, the research is application driven and we generally maintain a strong focus on tying the theoretical developments to real applications, ranging from electromagnetics and plasma physics to geoscience and combustion. There is also a sustained interest in the development of methods and algorithms for parallel computing, GPU accelerated computing and the development of resilient algorithms, to support the development and use of large scale computational tool to enable predictive similation science.
Publications
Publications Infoscience
Jan S Hesthaven publications
2020
W. Chen; Q. Wang; J. S. Hesthaven; C. Zhang : Physics-informed machine learning for reduced-order modeling of nonlinear problems. 2020-07-23.
Z. Zhang; J. S. Hesthaven : Rare event simulation for large-scale structures with local nonlinearities; Computer Methods In Applied Mechanics And Engineering. 2020-07-01. DOI : 10.1016/j.cma.2020.113051.
X. Wan; W.-S. Don; Z. Gao; J. S. Hesthaven : An edge detector based on artificial neural network with application to hybrid Compact-WENO finite difference schemes; Journal Of Scientific Computing. 2020-06-03. DOI : 10.1007/s10915-020-01237-6.
M. Kast; M. Guo; J. S. Hesthaven : A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems; Computer Methods In Applied Mechanics And Engineering. 2020-06-01. DOI : 10.1016/j.cma.2020.112947.
J. Magiera; D. Ray; J. S. Hesthaven; C. Rohde : Constraint-aware neural networks for Riemann problems; Journal Of Computational Physics. 2020-05-15. DOI : 10.1016/j.jcp.2020.109345.
N. Discacciati; J. S. Hesthaven; D. Ray : Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks; Journal Of Computational Physics. 2020-05-15. DOI : 10.1016/j.jcp.2020.109304.
C. Bigoni; Z. Zhang; J. S. Hesthaven : Systematic sensor placement for structural anomaly detection in the absence of damaged states. 2020-05-07.
C. Bigoni; J. S. Hesthaven : Simulation-based Anomaly Detection and Damage Localization: An application to Structural Health Monitoring; Computer Methods In Applied Mechanics And Engineering. 2020-05-01. DOI : 10.1016/j.cma.2020.112896.
J. Yu; J. S. Hesthaven : A Study of Several Artificial Viscosity Models within the Discontinuous Galerkin Framework; Communications In Computational Physics. 2020-05-01. DOI : 10.4208/cicp.OA-2019-0118.
L. Schwander; D. Ray; J. S. Hesthaven : Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks. 2020-04-09.
J. S. Hesthaven; F. Monkeberg : Two-Dimensional RBF-ENO Method on Unstructured Grids; Journal Of Scientific Computing. 2020-03-11. DOI : 10.1007/s10915-020-01176-2.
W. Hao; J. Hesthaven; G. Lin; B. Zheng : A Homotopy Method with Adaptive Basis Selection for Computing Multiple Solutions of Differential Equations; Journal Of Scientific Computing. 2020-01-13. DOI : 10.1007/s10915-020-01123-1.
I. Mekkaoui; J. Pousin; J. S. Hesthaven; J.-R. Li : Effective diffusion tensor measured by diffusion MRI of moving and deforming domains; Journal of Magnetic Resonance. 2020.
Q. Wang; N. Ripamonti; J. S. Hesthaven : Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism; Journal of Computational Physics. 2020. DOI : 10.1016/j.jcp.2020.109402.
2019
K. Shukla; J. M. Carcione; J. S. Hesthaven; E. L'heureux : Waves at a fluid-solid interface: Explicit versus implicit formulation of boundary conditions using a discontinuous Galerkin method. 2019-12-23.
F. Pind; C.-H. Jeong; J. S. Hesthaven; A. Engsig-Karup; J. Stromann-Andersen : Modeling extended-reaction boundary conditions in time-domain wave-based simulations of room acoustics. 2019-10-31.
Z. Zhang; M. Guo; J. S. Hesthaven : Model order reduction for large-scale structures with local nonlinearities; Computer Methods In Applied Mechanics and Engineering. 2019-08-15. DOI : 10.1016/j.cma.2019.04.042.
M. Hajihassanpour; B. Bonev; J. S. Hesthaven : A comparative study of earthquake source models in high- order accurate tsunami simulations; Ocean Modelling. 2019-08-14. DOI : 10.1016/j.ocemod.2019.101429.
J. S. Hesthaven; F. Monkeberg : Entropy Stable Essentially Nonoscillatory Methods Based On Rbf Reconstruction; ESAIM: Mathematical Modelling and Numerical Analysis. 2019-06-21. DOI : 10.1051/m2an/2019011.
F. Pind; A. P. Engsig-Karup; C.-H. Jeong; J. S. Hesthaven; M. S. Mejling et al. : Time domain room acoustic simulations using the spectral element method; Journal Of The Acoustical Society Of America. 2019-06-01. DOI : 10.1121/1.5109396.
J. Magiera; D. Ray; J. S. Hesthaven; C. Rohde : MATHICSE Technical Report: Constraint-Aware Neural Networks for Riemann Problems. 2019-04-28.
F. Pind; A. P. Engsig-Karup; C.-H. Jeong; J. S. Hesthaven; M. S. Meiling et al. : MATHICSE Technical Report: Time domain room acoustic simulations using a spectral element method. 2019-04-28.
C. Bigoni; J. S. Hesthaven : MATHICSE Technical Report: Simulation-Based Anomaly Detection and Damage Localization: an Application to Structural Health Monitoring. 2019-04-10.
M. Kast; M. Guo; J. S. Hesthaven : MATHICSE Technical Report: A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems. 2019-04-10.
N. Discacciati; J. S. Hesthaven; D. Ray : MATHICSE Technical Report: Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial\ viscosity tuned by neural networks. 2019-01-28.
Q. Wang; N. Ripamonti; J. S. Hesthaven : Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism. 2019.
T. Lahivaara; A. Pasanen; L. Karkkainen; J. M. Huttunen; J. S. Hesthaven et al. : Estimation of groundwater storage from seismic data using deep learning; Geophysics Research Letters. 2019. DOI : 10.1111/1365-2478.12831.
A. Karakus; N. Chalmers; J. S. Hesthaven; T. Warburton : Discontinuous Galerkin Discretizations of the Boltzmann Equations in 2D: semi-analytic time stepping and absorbing boundary layers; Journal of Computational Physics. 2019. DOI : 10.1016/j.jcp.2019.03.050.
P. Cazeaux; J. S. Hesthaven : Projective multiscale time-integration for electrostatic particle-in-cell methods; Computer Physics Communications. 2019. DOI : 10.1016/j.cpc.2018.10.012.
2018
J. S. Hesthaven; F. Mönkeberg; S. Zaninelli : RBF Based CWENO Method. 2018-11-09. ICOSAHOM 2018, London, UK.
D. Ray; J. S. Hesthaven : Detecting troubled-cells on two-dimensional unstructured grids using a neural network; Journal of Computational Physics. 2018-11-01. DOI : 10.1016/j.jcp.2019.07.043.
M. Guo; J. S. Hesthaven : Data-driven reduced order modeling for time-dependent problems; Computer Methods in Applied Mechanics and Engineering. 2018-10. DOI : 10.1016/j.cma.2018.10.029.
J. S. Hesthaven; F. Mönkeberg : Entropy stable essentially non-oscillatory methods based on RBF reconstructions; Mathematical Modeling and Numerical Analysis. 2018-08-14.
B. Maboudi Afkham; N. Ripamonti; Q. Wang; J. S. Hesthaven : Conservative Model Order Reduction for Fluid Flow; Advances in reduced order modeling; Springer Verlag, 2018-08-06.
W. Chen; J. S. Hesthaven; B. Junqiang; Y. Qiu; Y. Tihao et al. : Greedy Non-Intrusive Reduced Order Model for Fluid Dynamics; AIAA Journal. 2018-06-24. DOI : 10.2514/1.J056161.
J. Yu; J. S. Hesthaven; C. Yan : A data-driven shock capturing approach for discontinuous Galekin methods. 2018-06-18.
Q. Wang; J. S. Hesthaven; D. Ray : Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem; Journal of Computational Physics. 2018-06-18. DOI : 10.1016/j.jcp.2019.01.031.
W. Chen; B. Junqiang; J. S. Hesthaven; Q. Yasong; Q. Lei et al. : POD-Kriging reduced method’s application in Tandem Cylinders’ flow; Journal of Northwestern Polytechnical University. 2018-06-01.
N. Discacciati : Controlling oscillations in high-order schemes using neural networks ; 2018.
A. S. Nielsen / J. S. Hesthaven (Dir.) : Scaling and Resilience in Numerical Algorithms for Exascale Computing. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8926.
B. Maboudi Afkham / J. S. Hesthaven (Dir.) : Geometric Model Order Reduction. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8923.
A. Susnjara / D. Kressner (Dir.) : Fast hierarchical solvers for symmetric eigenvalue problems. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8808.
J. S. Hesthaven; C. Pagliantini : Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure. 2018.
K. Shukla; J. S. Hesthaven; J. M. Carcione; R. Ye; J. de la Puenta et al. : A nodal discontinuous Galerkin finite element method for the poroelastic wave equation; Computational Geoscience. 2018. DOI : 10.1007/s10596-019-9809-1.
N. Dal Santo / A. Quarteroni; A. Manzoni (Dir.) : Multi space reduced basis preconditioners for parametrized partial differential equations. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8553.
B. Maboudi Afkham; A. Bhatt; B. Haasdonk; J. S. Hesthaven : Symplectic Model-Reduction with a Weighted Inner Product; SIAM Journal of Scientific Computing. 2018.
J. Yu; J. S. Hesthaven : Flowfield Reconstruction Method Using Artificial Neural Network; AIAA Journal. 2018. DOI : 10.2514/1.J057108.
M. Guo; J. S. Hesthaven : Reduced order modeling for nonlinear structural analysis using Gaussian process regression; Computer Methods in Applied Mechanics and Engineering. 2018. DOI : 10.1016/j.cma.2018.07.017.
B. Bonev; J. S. Hesthaven; F. X. Giraldo; M. A. Kopera : Discontinuous Galerkin scheme for the spherical shallow water equations with applications to tsunami modeling and prediction; Journal of Computational Physics. 2018. DOI : 10.1016/j.jcp.2018.02.008.
D. Ray; J. S. Hesthaven : An artificial neural network as a troubled-cell indicator; Journal of computational physics. 2018. DOI : 10.1016/j.jcp.2018.04.029.
J. S. Hesthaven; S. Ubbiali : Non-intrusive reduced order modeling of nonlinear problems using neural networks; Journal of Computational Physics. 2018. DOI : 10.1016/j.jcp.2018.02.037.
T. Lahivaara; L. Karkkainen; J. M. Huttunen; J. S. Hesthaven : Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography; Journal of the Acoustical Society of America. 2018. DOI : 10.1121/1.5024341.
U. Ruede; K. Willcox; L. C. McInnes; H. De Sterck; G. Biros et al. : Research and Education in Computational Science and Engineering; Siam Review. 2018. DOI : 10.1137/16M1096840.
2017
J. S. Hesthaven : Numerical methods for conservation laws: From analysis to algorithms ; Philadelphia: SIAM Publishing.
J. Yu; J. S. Hesthaven : A comparative study of shock capturing models for the discontinuous Galerkin method; Journal of Computational Physics. 2017.
P. Gatto; J. S. Hesthaven : Efficient Preconditioning of hp-FEM Matrices by Hierarchical Low-Rank Approximations; Journal Of Scientific Computing. 2017. DOI : 10.1007/s10915-016-0347-x.
E. Musharbash / F. Nobile (Dir.) : Dynamical Low Rank approximation of PDEs with random parameters. Lausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-7813.
B. Bonev; F. Giraldo; J. S. Hesthaven : Large-Scale Tsunami Simulations using the Discontinuous Galerkin Method ; 27th Biennial Conference on Numerical Analysis, Glasgow, UK, June 27-30, 2017.
A. S. Nielsen; G. Brunner; J. S. Hesthaven : Communication-aware adaptive parareal with application to a nonlinear hyperbolic system of partial dierential equations; Journal of Computational Physics. 2017. DOI : 10.1016/j.jcp.2018.04.056.
B. Maboudi Afkham; J. S. Hesthaven : Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems; Journal of Scientific Computing. 2017.
W. Chen; J. S. Hesthaven; B. Junqiang; Z. Yang; Y. Tihao : A greedy non-intrusive reduced order model for fluid dynamics; Journal of Northwestern Polytechnical University. 2017.
D. Baffet; J. S. Hesthaven : High-Order Accurate Local Schemes for Fractional Differential Equations; Journal Of Scientific Computing. 2017. DOI : 10.1007/s10915-015-0089-1.
M. Dam; M. Brøns; J. J. Rasmussen; V. Naulin; J. S. Hesthaven : Identification of a Predator-Prey System from Simulation Data of a Convection Model; Physics of Plasmas. 2017. DOI : 10.1063/1.4977057.
P. Gatto; J. S. Hesthaven; R. Christiansen : Efficient preconditioning of hp-FEM matrices arising from time-varying problems: an application to topology optimization; Computer Methods in Applied Mechanics and Engineering. 2017. DOI : 10.1016/j.cma.2017.04.027.
C. Bigoni; J. S. Hesthaven : Adaptive WENO methods based on radial basis functions reconstruction; Journal of Scientific Computing. 2017. DOI : 10.1007/s10915-017-0383-1.
D. H. Baffet; J. S. Hesthaven : High-Order Accurate Adaptive Kernel Compression Time-Stepping Schemes for Fractional Differential Equations; Journal of Scientific Computing. 2017. DOI : 10.1007/s10915-017-0393-z.
B. Maboudi Afkham; J. S. Hesthaven : Structure preserving model reduction of parametric Hamiltonian systems; Siam Journal on Scientific Computing. 2017. DOI : 10.1137/17M1111991.
S. Yeganeh; R. Mokhtari; J. S. Hesthaven : Space-dependent source determination in a time-fractional diffusion equation using a local discontinuous Galerkin method; BIT Numerical Mathematics. 2017. DOI : 10.1007/s10543-017-0648-y.
D. H. Baffet; J. S. Hesthaven : A kernel compression scheme for fractional differential equations; Siam Journal on Numerical Analysis. 2017. DOI : 10.1137/15M1043960.
2016
D. S. Guignard / F. Nobile; M. Picasso (Dir.) : A posteriori error estimation for partial differential equations with random input data. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7260.
O. Budáč / A. Abdulle (Dir.) : Multiscale methods for Stokes flow in heterogeneous media. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7135.
J. S. Hesthaven : Spectral Methods for Hyperbolic Problems; Handbook of Numerical Methods for Hyperbolic Problems Basic and Fundamental Issues; Elsevier Publishing, 2016. p. 441-466.
P. Sirkovic / D. Kressner (Dir.) : Low-rank methods for parameter-dependent eigenvalue problems and matrix equations. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7137.
V. H. Laurmaa / M. Picasso (Dir.) : An octree-based adaptive semi-Lagrangian free surface flow solver. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7011.
D. Forti / A. Quarteroni; S. Deparis (Dir.) : Parallel Algorithms for the Solution of Large-Scale Fluid-Structure Interaction Problems in Hemodynamics. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6983.
L. Zhao; W. Deng; J. S. Hesthaven : Spectral methods for tempered fractional differential equations; Mathematics of Computation. 2016.
A. S. Nielsen; J. S. Hesthaven : Fault Tolerance in the Parareal Method. 2016. ACM Workshop on Fault-Tolerance for HPC at Extreme Scale (FTXS). p. 1-8. DOI : 10.1145/2909428.2909431.
Z. Gao; J. S. Hesthaven; T. Warburton : Efficient Absorbing Layers for Weakly Compressible Flows. 2016.
J. S. Hesthaven; S. Zhang : On the use of ANOVA expansions in reduced basis methods for high-dimensional parametric partial differential equations; Journal of Scientific Computing. 2016. DOI : 10.1007/s10915-016-0194-9.
2015
J. S. Hesthaven; G. Rozza; B. Stamm : Certified Reduced Basis Methods for Parametrized Partial Differential Equations ; Springer Verlag.
F. Negri / A. Quarteroni; G. Rozza (Dir.) : Efficient Reduction Techniques for the Simulation and Optimization of Parametrized Systems. Lausanne, EPFL, 2015. DOI : 10.5075/epfl-thesis-6810.
J. S. Hesthaven; J.-H. Jung; A. Tesdall : Hyperbolic Problems: Theory and Computation; Journal Of Scientific Computing. 2015. DOI : 10.1007/s10915-015-0065-9.
F. Chen; J. S. Hesthaven; Y. Maday; A. S. Nielsen : An Adjoint Approach for Stabilizing the Parareal Method; Comptes rendus des séances de l'Académie des Sciences. Série A, Sciences mathématiques**. 2015.
G. E. Karniadakis; J. S. Hesthaven; I. Podlubny : Special Issue on "Fractional PDEs: Theory, Numerics, and Applications"; Journal Of Computational Physics. 2015. DOI : 10.1016/j.jcp.2015.04.007.
J. Zudrop; J. S. Hesthaven : Accuracy of high order and spectral methods for hyperbolic conservation laws with discontinuous solutions; Siam Journal on Numerical Analysis. 2015. DOI : 10.1137/140992758.
L. Qiu; W. Deng; J. S. Hesthaven : Nodal discontinuous Galerkin methods for fractional diffusion equations on 2D domain with triangular meshes; Journal of Computational Physics. 2015. DOI : 10.1016/j.jcp.2015.06.022.
F. Nobile; L. Tamellini; R. Tempone : Comparison of Clenshaw-Curtis and Leja Quasi-Optimal Sparse Grids for the Approximation of Random PDEs. 2015. International Conference on Spectral and High-Order Methods 2014 (ICOSAHOM'14), Salt Lake City, June 23-27, 2014. p. 475-482. DOI : 10.1007/978-3-319-19800-2_44.
P. Gatto; J. S. Hesthaven : Numerical approximation of the fractional Laplacian via hp-finite elements, with an application to image denoising; Journal of Scientific Computing. 2015. DOI : 10.1007/s10915-014-9959-1.
Q. Xu; J. S. Hesthaven; F. Chen : A Parareal Method for Time-fractional Differential Equations; Journal of Computational Physics. 2015. DOI : 10.1016/j.jcp.2014.11.034.
W. Deng; J. S. Hesthaven : Local discontinuous Galerkin methods for fractional ordinary differential equations; Bit Numerical Mathematics. 2015. DOI : 10.1007/s10543-014-0531-z.
S. Tirupathi; J. S. Hesthaven; Y. Liang : Modeling 3D Magma Dynamics Using a Discontinuous Galerkin Method; Communications in Computational Physics. 2015. DOI : 10.4208/cicp.090314.151214a.
S. Tirupathi; J. S. Hesthaven; Y. Liang; M. Parmentier : Multilevel and Local Timestepping Discontinuous Galerkin Methods for Magma Dynamics; Computational Geosciences. 2015. DOI : 10.1007/s10596-015-9514-7.
J. S. Hesthaven; S. Zhang; X. Zhu : Reduced basis multiscale finite element methods for elliptic problems; Multiscale Modeling and Simulation. 2015. DOI : 10.1137/140955070.
F. Chen; Q. Xu; J. S. Hesthaven : A Multi-domain Spectral Method for Time-fractional Differential Equations; Journal of Computational Physics. 2015. DOI : 10.1016/j.jcp.2014.10.016.
2014
F. Chen; J. S. Hesthaven; X. Zhu : On the Use of Reduced Basis Methods to Accelerate and Stabilize the Parareal Method. 2014. Workshop on Reduced Basis, POD and Reduced Order Methods for Model and Computational Reduction: towards Real-time Computing and Visualization', Lausanne, CH. p. 187-214. DOI : 10.1007/978-3-319-02090-7_7.
C. M. Colciago / A. Quarteroni; S. Deparis (Dir.) : Reduced Order Fluid-Structure Interaction Models for Haemodynamics Applications. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6285.
R. Munipalli; Z. Liu; X. Zhu; S. Menon; J. S. Hesthaven : Applications of Model Reduction Techniques in Aerospace Combustors. 2014. AIAA Conference.
P. Chen / A. Quarteroni; G. Rozza (Dir.) : Model Order Reduction Techniques for Uncertainty Quantification Problems. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6118.
J. S. Hesthaven : Time-domain finite element methods for Maxwell's equations in metamaterials; Siam Review. 2014.
J. Li; J. S. Hesthaven : Analysis and application of the nodal discontinuous Galerkin method for wave propagation in metamaterials; Journal Of Computational Physics. 2014. DOI : 10.1016/j.jcp.2013.11.018.
Enseignement & Phd
Enseignement
Mathematics