Jan S. Hesthaven - Selected Publications#

Google scholar (11/05/2023): h-index 67, 20491 citations
Web of science (11/05/2023): h-index 49, 9573 citations

Books (out of 5)

J. S. Hesthaven and T. Warburton, 2008, Nodal Discontinuous Galerkin Methods: Algorithms, Analysis, and Applications, Springer Texts in Applied Mathematics 54, Springer Verlag, New York, USA, XIV+500 pages.

J. S. Hesthaven, G. Rozza, and B. Stamm, 2016, Certified Reduced Basis Methods for Parametrized Partial Differential Equations, Springer Brief in Mathematics, Springer Verlag, Berlin, Germany, 131 pages.

J. S. Hesthaven, 2018, Numerical Methods for Conservation Laws: From Analysis to Algorithms, Computational Science and Engineering 18, SIAM Publications, Philadelphia, USA, XVI+570 pages.

Articles (out of 175+)

J. S. Hesthaven and T. Warburton, 2002, High-Order Nodal Methods on Unstructured Grids. I. Time-Domain Solution of Maxwell’s Equations, J. Comput. Phys. 181(1), 186-221.

D. Xiu and J.S. Hesthaven, 2005, High-Order Collocation Methods for Differential Equations with Random Inputs, SIAM J. Sci. Comput. 27, 1118-1139.

W. Deng and J. S. Hesthaven, 2013, Local discontinuous Galerkin methods for fractional diffusion equations, Math. Model. Numer, Anal. 47(6), 1845-1864.

J.S. Hesthaven, 2016, Spectral Methods for Hyperbolic Problems, In Handbook of Numerical Methods for Hyperbolic Problems Basic and Fundamental Issues. Handbook of Numerical Analysis 17, 441-466.

J.S. Hesthaven and S. Ubbiali, 2018, Non-intrusive reduced order modeling of nonlinear problems using neural networks, J. Comput. Phys. 363, 55-78.

Q. Wang, J. S. Hesthaven, D. Ray, 2019, Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem, J. Comput. Phys. 384, 289-307.

J.S. Hesthaven, C. Pagliantini, and G. Rozza, 2022, Reduced Basic Methods for Time-Dependent Problems, In Acta Numerica 31, 265-345