Jan S. Hesthaven

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

+41 21 693 03 51
EPFL › VPA › VPA-AVP-DLE › AVP-DLE-EDOC › EDOC-ENS

Expertise

His research interests focus on the development, analysis, and application of high-order accurate computational methods for the solution of complex time-dependent problems, often requiring high-performance computing. A particular focus of his research has been on the development of computational methods for problems of linear and non-linear wave problems with applications to optics, radar applications, gravity waves, and geophysical flows, eg for tsunami prediction. He has also made considerable contributions to the development of reduced order models to allow the accurate real-time modeling of complex systems. A recent emphasis has been on combining traditional methods with machine learning and neural networks with broad applications, including structural health monitoring. Throughout his career, he has worked collaboratively with engineers and applied scientists in the public or private sector. He has co-authored more than 175 papers and 4 monographs, several of which have become standard references.

Expertise

Biographie Prix et distinctions Publications Enseignement et PhD

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.
During 2017-2020 he served as Dean of the School of Basic Sciences, comprising the Institutes of Chemistry, Mathematics, and Physics, and since 2021 he holds the position as Provost and Vice-President of Academic Affairs at EPFL.

Formation

2009 – 2009 Technical University of Denmark

Prix et distinctions

US National Science Foundation

1995

Alfred P Sloan Foundation, US

2000

Brown University, US

2001

Society of Industrial and Applied Mathematics, US

2014

US National Science Foundation

2002

American Mathematical Society

2022

Royal Danish Academy of Sciences and Letters

2022

2025

Neural Empirical Interpolation Method for Nonlinear Model Reduction

M. Hirsch; F. Pichi; J. S. Hesthaven

SIAM Journal on Scientific Computing. 2025. DOI : 10.1137/24m1681434.

Jan S. Hesthaven

Expertise

Expertise

Formation

Prix et distinctions

2025

Neural Empirical Interpolation Method for Nonlinear Model Reduction

A coordinate transformation-based physics-informed neural networks for hyperbolic conservation laws

Galerkin neural network-POD for acoustic and electromagnetic wave propagation in parametric domains

Leveraging Contrast Agent Kinetics for Robust Reflectance Mode Fluorescence Tomography

2024

GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications

Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study

Perfectly matched layers for the Boltzmann equation: Stability and sensitivity analysis

Machine-Learning-Enhanced Real-Time Aerodynamic Forces Prediction Based on Sparse Pressure Sensor Inputs

Positional Embeddings for Solving PDEs with Evolutional Deep Neural Networks

Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

A Fast Direct Solver for Higher Order Discretizations of Integral Equations

Uncertainty quantification in groundwater volume predictions from seismic data using neural networks

2023

Massively parallel nodal discontinous Galerkin finite element method simulator for room acoustics

Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems

Adaptive Symplectic Model Order Reduction Of Parametric Particle-Based Vlasov-Poisson Equation

Localized model order reduction and domain decomposition methods for coupled heterogeneous systems

A new variable shape parameter strategy for RBF approximation using neural networks

An artificial neural network approach to bifurcating phenomena in computational fluid dynamics

Monitoring of water table level and volume of water in a porous storage by seismic data

Reduced order models for coupled systems

2022

Multi-fidelity surrogate modeling using long short-term memory networks

Model order reduction for compressible flows solved using the discontinuous Galerkin methods

A data-driven shock capturing approach for discontinuous Galekin methods

Fourier Collocation and Reduced Basis Methods for Fast Modeling of Compressible Flows

Discovery of Slow Variables in a Class Of Multiscale Stochastic Systems Via Neural Networks

Reduced basis methods for numerical room acoustic simulations with parametrized boundaries

Preface to the Focused Issue on WENO Schemes

Reduced basis methods for time-dependent problems

Rank-adaptive structure-preserving model order reduction of Hamiltonian systems

Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities

A hierarchical preconditioner for wave problems in quasilinear complexity

Population pharmacokinetic model selection assisted by machine learning

Structure-preserving approaches and data-driven closure modeling for model order reduction

Preface to Focused Issue on Discontinuous Galerkin Methods PREFACE

2021

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

Construction of optimal spectral methods in phase retrieval

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

Structure-Preserving Reduced Basis Methods For Poisson Systems

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

Modeling synchronization in globally coupled oscillatory systems using model order reduction

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

Efficient algorithms for wave problems

2020

A Local Discontinuous Galerkin Method for Two-Dimensional Time Fractional Diffusion Equations

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

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

Conservative Model Order Reduction for Fluid Flow

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

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

Constraint-aware neural networks for Riemann problems

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

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

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

High-order essentially nonoscillatory methods based on radial basis functions

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

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