335 citations · 1.8k across the 47 of their papers we have counts for
7 papers · 1 filter
FC-based shock-dynamics solver with neural-network localized artificial-viscosity assignment
Oscar P. Bruno, Jan S. Hesthaven, Daniel V. Leibovici
This paper presents a spectral scheme for the numerical solution of nonlinear conservation laws in non-periodic domains under arbitrary boundary conditions. The approach relies on…
Structure-preserving model order reduction of Hamiltonian systems
J. S. Hesthaven, C. Pagliantini, N. Ripamonti
We discuss the recent developments of projection-based model order reduction (MOR) techniques targeting Hamiltonian problems. Hamilton's principle completely characterizes many hig…
An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
Federico Pichi, Francesco Ballarin, Gianluigi Rozza +1
This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial neural networks. We discuss the POD-NN approach de…
A hierarchical preconditioner for wave problems in quasilinear complexity
Boris Bonev, Jan S. Hesthaven
The paper introduces a novel, hierarchical preconditioner based on nested dissection and hierarchical matrix compression. The preconditioner is intended for continuous and disconti…
Discovery of slow variables in a class of multiscale stochastic systems via neural networks
Przemyslaw Zielinski, Jan S. Hesthaven
Finding a reduction of complex, high-dimensional dynamics to its essential, low-dimensional "heart" remains a challenging yet necessary prerequisite for designing efficient numeric…
Reduced basis methods for numerical room acoustic simulations with parametrized boundaries
Hermes Sampedro Llopis, Allan P. Engsig-Karup, Cheol-Ho Jeong +2
The use of model-based numerical simulation of wave propagation in rooms for engineering applications requires that acoustic conditions for multiple parameters are evaluated iterat…
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
Mengwu Guo, Andrea Manzoni, Maurice Amendt +2
Highly accurate numerical or physical experiments are often time-consuming or expensive to obtain. When time or budget restrictions prohibit the generation of additional data, the…