Jan S. Hesthaven

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Vice-président académique

jan.hesthaven@epfl.ch 41 21 693 03 51 https://www.epfl.ch/labs/mcss/

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https://orcid.org/0000-0001-8074-1586

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41 21 693 03 51
EPFL > VPA > VPA-VP > VPA-VP-GE

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/

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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.

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

Structure-Preserving Reduced Basis Methods For Poisson Systems

J. S. Hesthaven; C. Pagliantini

Mathematics Of Computation. 2021-07-01. DOI : 10.1090/mcom/3618.

Jan S. Hesthaven

Vice-président académique

Domaines de compétences

Biographie

Formation

Récompenses

Publications

Publications Infoscience

Jan S Hesthaven publications

2021

Structure-Preserving Reduced Basis Methods For Poisson Systems

Non-Intrusive Reduced Order Modeling of Convection Dominated Flows Using Artificial Neural Networks with Application to Rayleigh-Taylor Instability

Pointwise error estimate in difference setting for the two-dimensional nonlinear fractional complex Ginzburg-Landau equation

Fast screening of covariates in population models empowered by machine learning

Discovery of slow variables in a class of multiscale stochastic systems via neural networks

Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks

A phenomenological extended-reaction boundary model for time-domain wave-based acoustic simulations under sparse reflection conditions using a wave splitting method

Hybrid high-resolution RBF-ENO method

2020

Characterization of image spaces of Riemann-Liouville fractional integral operators on Sobolev spaces W-m,W-p (omega)

Time-domain room acoustic simulations with extended-reacting porous absorbers using the discontinuous Galerkin method

Controlling oscillations in spectral schemes using Artificial Neural Networks

A hierarchical preconditioner for wave problems in quasilinear complexity

Massive parallel nodal discontinuous Galerkin finite element method simulator for room acoustics

Apparent diffusion coefficient measured by diffusion MRI of moving and deforming domains

Systematic sensor placement for structural anomaly detection in the absence of damaged states

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Rare event simulation for large-scale structures with local nonlinearities

An edge detector based on artificial neural network with application to hybrid Compact-WENO finite difference schemes

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Constraint-aware neural networks for Riemann problems

Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks

Simulation-based Anomaly Detection and Damage Localization: An application to Structural Health Monitoring

A Study of Several Artificial Viscosity Models within the Discontinuous Galerkin Framework

Two-Dimensional RBF-ENO Method on Unstructured Grids

A Homotopy Method with Adaptive Basis Selection for Computing Multiple Solutions of Differential Equations

Numerical methods for structural anomaly detection using model order reduction and data-driven techniques

High-order essentially nonoscillatory methods based on radial basis functions

Effective diffusion tensor measured by diffusion MRI of moving and deforming domains

Waves at a fluid-solid interface: Explicit versus implicit formulation of boundary conditions using a discontinuous Galerkin method

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

2019

Modeling extended-reaction boundary conditions in time-domain wave-based simulations of room acoustics

Model order reduction for large-scale structures with local nonlinearities

A comparative study of earthquake source models in high- order accurate tsunami simulations

Entropy Stable Essentially Nonoscillatory Methods Based On Rbf Reconstruction

Time domain room acoustic simulations using the spectral element method

MATHICSE Technical Report: Constraint-Aware Neural Networks for Riemann Problems

MATHICSE Technical Report: Time domain room acoustic simulations using a spectral element method

MATHICSE Technical Report: Simulation-Based Anomaly Detection and Damage Localization: an Application to Structural Health Monitoring

MATHICSE Technical Report: A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

MATHICSE Technical Report: Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks

Entropy stable essentially non-oscillatory methods based on RBF reconstructions

Estimation of groundwater storage from seismic data using deep learning

Discontinuous Galerkin Discretizations of the Boltzmann Equations in 2D: semi-analytic time stepping and absorbing boundary layers

Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems

Projective multiscale time-integration for electrostatic particle-in-cell methods

2018

RBF Based CWENO Method

Detecting troubled-cells on two-dimensional unstructured grids using a neural network

Data-driven reduced order modeling for time-dependent problems

Conservative Model Order Reduction for Fluid Flow

Greedy Non-Intrusive Reduced Order Model for Fluid Dynamics

A data-driven shock capturing approach for discontinuous Galekin methods

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

POD-Kriging reduced method’s application in Tandem Cylinders’ flow

Controlling oscillations in high-order schemes using neural networks

Scaling and Resilience in Numerical Algorithms for Exascale Computing

Geometric Model Order Reduction

Fast hierarchical solvers for symmetric eigenvalue problems

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure

A nodal discontinuous Galerkin finite element method for the poroelastic wave equation

Multi space reduced basis preconditioners for parametrized partial differential equations

Symplectic Model-Reduction with a Weighted Inner Product

Flowfield Reconstruction Method Using Artificial Neural Network

Reduced order modeling for nonlinear structural analysis using Gaussian process regression

Discontinuous Galerkin scheme for the spherical shallow water equations with applications to tsunami modeling and prediction

An artificial neural network as a troubled-cell indicator

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography