Jan S. Hesthaven

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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-AVP-PGE > AVP-PGE-EDOC > EDOC-ENS

+41 21 693 03 51
EPFL > SB > SB-DEC > PH-SB

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Fields of expertise

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.

Biography

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.

Education

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

Publications

Infoscience publications

Journal Publications

2024

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

M. Sutti; J. S. Hesthaven

Journal Of Computational Physics. 2024-07-15. DOI : 10.1016/j.jcp.2024.113047.

Jan S. Hesthaven

Fields of expertise

Biography

Education

Publications

Infoscience publications

Journal Publications

2024

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

* 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

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

2022

* 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

* Reduced basis methods for numerical room acoustic simulations with parametrized boundaries

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

* Preface to the Focused Issue on WENO Schemes

* 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

* Preface to Focused Issue on Discontinuous Galerkin Methods PREFACE

2021

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

* 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

* 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

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

* 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

* 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

* 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

* Time domain room acoustic simulations using the spectral element method

* Entropy stable essentially non-oscillatory methods based on RBF reconstructions

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

* 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

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

* Data-driven reduced order modeling for time-dependent problems

* Greedy Non-Intrusive Reduced Order Model for Fluid Dynamics

* 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

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