Jan S. Hesthaven | DeepAI

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∙ 05/16/2023

Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs

Accurate prediction of aerodynamic forces in real-time is crucial for au...

0 Junming Duan, et al. ∙

∙ 05/15/2023

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

The present work proposes a framework for nonlinear model order reductio...

0 Federico Pichi, et al. ∙

∙ 03/06/2023

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

Reduced-order models are indispensable for multi-query or real-time prob...

0 Junming Duan, et al. ∙

∙ 10/30/2022

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

The choice of the shape parameter highly effects the behaviour of radial...

0 Fatemeh Nassajian Mojarrad, et al. ∙

∙ 08/05/2022

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

When evaluating quantities of interest that depend on the solutions to d...

1 Paolo Conti, et al. ∙

∙ 01/14/2022

Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equation

High-resolution simulations of particle-based kinetic plasma models typi...

0 Jan S. Hesthaven, et al. ∙

∙ 11/02/2021

FC-based shock-dynamics solver with neural-network localized artificial-viscosity assignment

This paper presents a spectral scheme for the numerical solution of nonl...

0 Oscar P. Bruno, et al. ∙

∙ 09/22/2021

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

This work deals with the investigation of bifurcating fluid phenomena us...

4 Federico Pichi, et al. ∙

∙ 05/17/2021

A hierarchical preconditioner for wave problems in quasilinear complexity

The paper introduces a novel, hierarchical preconditioner based on neste...

0 Boris Bonev, et al. ∙

∙ 04/28/2021

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

Finding a reduction of complex, high-dimensional dynamics to its essenti...

0 Przemyslaw Zielinski, et al. ∙

∙ 03/22/2021

Efficient numerical room acoustic simulations with parametrized boundaries using the spectral element and reduced basis method

Numerical methods can be used to simulate wave propagation in rooms, wit...

0 Hermes Sampedro Llopis, et al. ∙

∙ 02/26/2021

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

Highly accurate numerical or physical experiments are often time-consumi...

0 Mengwu Guo, et al. ∙

∙ 07/26/2020

Rank-adaptive structure-preserving reduced basis methods for Hamiltonian systems

This work proposes an adaptive structure-preserving model order reductio...

0 Jan S. Hesthaven, et al. ∙

∙ 04/29/2019

Constraint-Aware Neural Networks for Riemann Problems

Neural networks are increasingly used in complex (data-driven) simulatio...

0 Jim Magiera, et al. ∙