Jan S. Hesthaven
+41 21 693 03 51
EPFL
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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
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Web site: Site web: https://mcss.epfl.ch/
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.
Biographie
Prof. Hesthaven received an M.Sc. in computational physics from the Technical University of Denmark (DTU) in August 1991. During the studies, the last 6 months of 1989 was spend at JET, the european fusion laboratory in Culham, UK. Following graduation, he was awarded a 3 year fellowship to begin work towards a Ph.D. at Riso National Laboratory in the Department of Optics and Fluid Dynamics.
During the 3 years of study, the academic year of 1993-1994 was spend in the Division of Applied Mathematics at Brown University and three 3 months during the summer of 1994 in Department of Mathematics and Statistics at University of New Mexico. In August 1995, he recieved a Ph.D. in Numerical Analysis from the Institute of Mathematical Modelling (DTU).
Following graduation in August 1995, he was awarded an NSF Postdoctoral Fellowship in Advanced Scientific Computing and was approinted Visiting Assistant Professor in the Division of Applied Mathematics at Brown University. In December of 1996, he was appointed consultant to the Institute of Computer Applications in Science and Engineering(ICASE) at NASA Langley Research Center (NASA LaRC).
As of July 1999, he was appointed Assistant Professor of Applied Mathematics, in September 2000 he was awarded an Alfred P. Sloan Fellowship, as of July 2001 he was awarded a Manning Assistant Professorship, and in March 2002, he was awarded an NSF Career Award.
In January 2003, he was promoted to Associate Professor of Applied Mathematics with tenure and in May 2004 he was awarded Philip J. Bray Award for Excellence in Teaching in the Sciences (the highest award given for teaching excellence in all sciences at Brown University).
He was promoted to Professor of Applied Mathematics as of July 2005.
From October 2006 to June 2013, he was the Founding Director of the Center for Computation and Visualization (CCV) at Brown University.
As of October 2007, he holds the (honorary) title of Professor (Adjunct) at the Technical University of Denmark.
In November 2009, he successfully defended his dr.techn thesis at the Technical University of Denmark and was rewarded the degree of Doctor Technices -- the highest academic distinction awarded based on ... substantial and lasting contributions that has helped to move the research area forward and penetrated into applications.
As grant Co-PI he served from Aug 2010 to June 2013 as Deputy Director of the Institute of Computational and Experimental Research in Mathematics (ICERM), the newest NSF Mathematical Sciences Research Institute.
After having spend his entire academic career at Brown University, Prof Hesthaven decided to pursue new challenges and joined the Mathematics Institute of Computational Science and Engineering (MATHICSE) at Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland in July 2013.
In March 2014 he was elected SIAM Fellow for contributions to high-order methods for partial differential equations.
During the 3 years of study, the academic year of 1993-1994 was spend in the Division of Applied Mathematics at Brown University and three 3 months during the summer of 1994 in Department of Mathematics and Statistics at University of New Mexico. In August 1995, he recieved a Ph.D. in Numerical Analysis from the Institute of Mathematical Modelling (DTU).
Following graduation in August 1995, he was awarded an NSF Postdoctoral Fellowship in Advanced Scientific Computing and was approinted Visiting Assistant Professor in the Division of Applied Mathematics at Brown University. In December of 1996, he was appointed consultant to the Institute of Computer Applications in Science and Engineering(ICASE) at NASA Langley Research Center (NASA LaRC).
As of July 1999, he was appointed Assistant Professor of Applied Mathematics, in September 2000 he was awarded an Alfred P. Sloan Fellowship, as of July 2001 he was awarded a Manning Assistant Professorship, and in March 2002, he was awarded an NSF Career Award.
In January 2003, he was promoted to Associate Professor of Applied Mathematics with tenure and in May 2004 he was awarded Philip J. Bray Award for Excellence in Teaching in the Sciences (the highest award given for teaching excellence in all sciences at Brown University).
He was promoted to Professor of Applied Mathematics as of July 2005.
From October 2006 to June 2013, he was the Founding Director of the Center for Computation and Visualization (CCV) at Brown University.
As of October 2007, he holds the (honorary) title of Professor (Adjunct) at the Technical University of Denmark.
In November 2009, he successfully defended his dr.techn thesis at the Technical University of Denmark and was rewarded the degree of Doctor Technices -- the highest academic distinction awarded based on ... substantial and lasting contributions that has helped to move the research area forward and penetrated into applications.
As grant Co-PI he served from Aug 2010 to June 2013 as Deputy Director of the Institute of Computational and Experimental Research in Mathematics (ICERM), the newest NSF Mathematical Sciences Research Institute.
After having spend his entire academic career at Brown University, Prof Hesthaven decided to pursue new challenges and joined the Mathematics Institute of Computational Science and Engineering (MATHICSE) at Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland in July 2013.
In March 2014 he was elected SIAM Fellow for contributions to high-order methods for partial differential equations.
Formation
Master of Science
Computational Physics
Technical University of Denmark
01/91-08/91
Doctor of Philosophy (PhD)
Numerical analysis
Technical University of Denmark
08/92-08/95
Doctor Technices (dr.techn)
Computational Mathematics
Technical University of Denmark
11/2009
Récompenses
NSF Postdoctoral Fellowship
US National Science Foundation
1995
Alfred P. Sloan Research Fellowship
Alfred P Sloan Foundation, US
2000
Manning Assistant Professorship
Brown University, US
2001
NSF Career Award
US National Science Foundation
2002
Gutenberg Chair
Gutenberg Foundation, France
2012
Elected SIAM Fellow
Society of Industrial and Applied Mathematics, US
2014
Publications
Publications Infoscience
Jan S Hesthaven publications
2021
Physics-informed machine learning for reduced-order modeling of nonlinear problems
Journal of Computational Physics. 2021-08-27. DOI : 10.1016/j.jcp.2021.110666.Structure-Preserving Reduced Basis Methods For Poisson Systems
Mathematics Of Computation. 2021-07-01. DOI : 10.1090/mcom/3618.Non-Intrusive Reduced Order Modeling of Convection Dominated Flows Using Artificial Neural Networks with Application to Rayleigh-Taylor Instability
Communications In Computational Physics. 2021-07-01. DOI : 10.4208/cicp.OA-2020-0064.Pointwise error estimate in difference setting for the two-dimensional nonlinear fractional complex Ginzburg-Landau equation
Advances In Computational Mathematics. 2021-06-01. DOI : 10.1007/s10444-021-09862-x.Fast screening of covariates in population models empowered by machine learning
Journal Of Pharmacokinetics And Pharmacodynamics. 2021-05-21. DOI : 10.1007/s10928-021-09757-w.Discovery of slow variables in a class of multiscale stochastic systems via neural networks
ArXiv. 2021-04-28.Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks
Journal Of Computational Physics. 2021-04-15. DOI : 10.1016/j.jcp.2021.110144.A phenomenological extended-reaction boundary model for time-domain wave-based acoustic simulations under sparse reflection conditions using a wave splitting method
Applied Acoustics. 2021-01-15. DOI : 10.1016/j.apacoust.2020.107596.Efficient algorithms for wave problems
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-8641.Hybrid high-resolution RBF-ENO method
Journal of Computational Physics: X. 2021. DOI : 10.1016/j.jcpx.2021.100089.2020
Characterization of image spaces of Riemann-Liouville fractional integral operators on Sobolev spaces W-m,W-p (omega)
Science China-Mathematics. 2020-11-18. DOI : 10.1007/s11425-019-1720-1.Time-domain room acoustic simulations with extended-reacting porous absorbers using the discontinuous Galerkin method
Journal Of The Acoustical Society Of America. 2020-11-01. DOI : 10.1121/10.0002448.Controlling oscillations in spectral schemes using Artificial Neural Networks
2020-09-14A hierarchical preconditioner for wave problems in quasilinear complexity
SIAM Journal on Scientific Computing. 2020-09-09.Massive parallel nodal discontinuous Galerkin finite element method simulator for room acoustics
International Journal of High Performance Computing Applications. 2020-09-06.Apparent diffusion coefficient measured by diffusion MRI of moving and deforming domains
Journal Of Magnetic Resonance. 2020-09-01. DOI : 10.1016/j.jmr.2020.106809.Systematic sensor placement for structural anomaly detection in the absence of damaged states
Computer Methods in Applied Mechanics and Engineering. 2020-08-18. DOI : 10.1016/j.cma.2020.113315.Physics-informed machine learning for reduced-order modeling of nonlinear problems
2020-07-23.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.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.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.Constraint-aware neural networks for Riemann problems
Journal Of Computational Physics. 2020-05-15. DOI : 10.1016/j.jcp.2020.109345.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.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.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.Two-Dimensional RBF-ENO Method on Unstructured Grids
Journal Of Scientific Computing. 2020-03-11. DOI : 10.1007/s10915-020-01176-2.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.Numerical methods for structural anomaly detection using model order reduction and data-driven techniques
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7734.High-order essentially nonoscillatory methods based on radial basis functions
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-8411.Effective diffusion tensor measured by diffusion MRI of moving and deforming domains
Journal of Magnetic Resonance. 2020.Waves at a fluid-solid interface: Explicit versus implicit formulation of boundary conditions using a discontinuous Galerkin method
The Journal of the Acoustical Society of America. 2020. DOI : 10.1121/10.0001170.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
Modeling extended-reaction boundary conditions in time-domain wave-based simulations of room acoustics
2019-10-31.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.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.Entropy Stable Essentially Nonoscillatory Methods Based On Rbf Reconstruction
ESAIM: Mathematical Modelling and Numerical Analysis. 2019-06-21. DOI : 10.1051/m2an/2019011.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.MATHICSE Technical Report: Constraint-Aware Neural Networks for Riemann Problems
2019-04-28MATHICSE Technical Report: Time domain room acoustic simulations using a spectral element method
2019-04-28MATHICSE Technical Report: Simulation-Based Anomaly Detection and Damage Localization: an Application to Structural Health Monitoring
2019-04-10MATHICSE Technical Report: A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems
2019-04-10MATHICSE Technical Report: Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks
2019-01-28Entropy stable essentially non-oscillatory methods based on RBF reconstructions
Mathematical Modeling and Numerical Analysis. 2019. DOI : 10.1051/m2an/2019011.A nodal discontinuous Galerkin finite element method for the poroelastic wave equation
Computational Geoscience. 2019. DOI : 10.1007/s10596-019-9809-1.Estimation of groundwater storage from seismic data using deep learning
Geophysics Research Letters. 2019. DOI : 10.1111/1365-2478.12831.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.Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems
Journal of Scientific Computing. 2019. DOI : 10.1007/s10915-018-0653-6.Projective multiscale time-integration for electrostatic particle-in-cell methods
Computer Physics Communications. 2019. DOI : 10.1016/j.cpc.2018.10.012.2018
RBF Based CWENO Method
2018-11-09. ICOSAHOM 2018, London, UK.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.Data-driven reduced order modeling for time-dependent problems
Computer Methods in Applied Mechanics and Engineering. 2018-10-01. DOI : 10.1016/j.cma.2018.10.029.Conservative Model Order Reduction for Fluid Flow
Advances in reduced order modeling; Springer Verlag, 2018-08-06.Greedy Non-Intrusive Reduced Order Model for Fluid Dynamics
AIAA Journal. 2018-06-24. DOI : 10.2514/1.J056161.A data-driven shock capturing approach for discontinuous Galekin methods
2018-06-18.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.POD-Kriging reduced method’s application in Tandem Cylinders’ flow
Journal of Northwestern Polytechnical University. 2018-06-01. DOI : 10.1051/jnwpu/20183620220.Controlling oscillations in high-order schemes using neural networks
2018Scaling and Resilience in Numerical Algorithms for Exascale Computing
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8926.Geometric Model Order Reduction
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8923.Fast hierarchical solvers for symmetric eigenvalue problems
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8808.Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure
Mathematics of computation. 2018.Multi space reduced basis preconditioners for parametrized partial differential equations
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8553.Symplectic Model-Reduction with a Weighted Inner Product
SIAM Journal of Scientific Computing. 2018.Flowfield Reconstruction Method Using Artificial Neural Network
AIAA Journal. 2018. DOI : 10.2514/1.J057108.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.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.An artificial neural network as a troubled-cell indicator
Journal of computational physics. 2018. DOI : 10.1016/j.jcp.2018.04.029.Non-intrusive reduced order modeling of nonlinear problems using neural networks
Journal of Computational Physics. 2018. DOI : 10.1016/j.jcp.2018.02.037.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.Research and Education in Computational Science and Engineering
Siam Review. 2018. DOI : 10.1137/16M1096840.2017
Numerical methods for conservation laws: From analysis to algorithms
Philadelphia: SIAM Publishing.A comparative study of shock capturing models for the discontinuous Galerkin method
Journal of Computational Physics. 2017.Efficient Preconditioning of hp-FEM Matrices by Hierarchical Low-Rank Approximations
Journal Of Scientific Computing. 2017. DOI : 10.1007/s10915-016-0347-x.Dynamical Low Rank approximation of PDEs with random parameters
Lausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-7813.Large-Scale Tsunami Simulations using the Discontinuous Galerkin Method
27th Biennial Conference on Numerical Analysis, Glasgow, UK, June 27-30, 2017.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.A greedy non-intrusive reduced order model for fluid dynamics
Journal of Northwestern Polytechnical University. 2017.High-Order Accurate Local Schemes for Fractional Differential Equations
Journal Of Scientific Computing. 2017. DOI : 10.1007/s10915-015-0089-1.Identification of a Predator-Prey System from Simulation Data of a Convection Model
Physics of Plasmas. 2017. DOI : 10.1063/1.4977057.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.Adaptive WENO methods based on radial basis functions reconstruction
Journal of Scientific Computing. 2017. DOI : 10.1007/s10915-017-0383-1.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.Structure preserving model reduction of parametric Hamiltonian systems
Siam Journal on Scientific Computing. 2017. DOI : 10.1137/17M1111991.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.A kernel compression scheme for fractional differential equations
Siam Journal on Numerical Analysis. 2017. DOI : 10.1137/15M1043960.2016
A posteriori error estimation for partial differential equations with random input data
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7260.Multiscale methods for Stokes flow in heterogeneous media
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7135.Spectral Methods for Hyperbolic Problems
Handbook of Numerical Methods for Hyperbolic Problems Basic and Fundamental Issues; Elsevier Publishing, 2016. p. 441-466.Low-rank methods for parameter-dependent eigenvalue problems and matrix equations
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7137.An octree-based adaptive semi-Lagrangian free surface flow solver
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7011.Parallel Algorithms for the Solution of Large-Scale Fluid-Structure Interaction Problems in Hemodynamics
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6983.Spectral methods for tempered fractional differential equations
Mathematics of Computation. 2016.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.Efficient Absorbing Layers for Weakly Compressible Flows
2016On 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
Certified Reduced Basis Methods for Parametrized Partial Differential Equations
Springer Verlag.Efficient Reduction Techniques for the Simulation and Optimization of Parametrized Systems
Lausanne, EPFL, 2015. DOI : 10.5075/epfl-thesis-6810.Hyperbolic Problems: Theory and Computation
Journal Of Scientific Computing. 2015. DOI : 10.1007/s10915-015-0065-9.An Adjoint Approach for Stabilizing the Parareal Method
Comptes rendus des séances de l'Académie des Sciences. Série A, Sciences mathématiques**. 2015.Special Issue on "Fractional PDEs: Theory, Numerics, and Applications"
Journal Of Computational Physics. 2015. DOI : 10.1016/j.jcp.2015.04.007.Enseignement & Phd
Enseignement
Mathematics