335 citations · 1.8k across the 47 of their papers we have counts for
5 papers · 1 filter
Perfectly matched layers for the Boltzmann equation: stability and sensitivity analysis
Marco Sutti, Jan S. Hesthaven
We study the stability and sensitivity of an absorbing layer for the Boltzmann equation by examining the Bhatnagar-Gross-Krook (BGK) approximation and using the perfectly matched l…
Positional Embeddings for Solving PDEs with Evolutional Deep Neural Networks
Mariella Kast, Jan S Hesthaven
This work extends the paradigm of evolutional deep neural networks (EDNNs) to solving parametric time-dependent partial differential equations (PDEs) on domains with geometric stru…
Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs
Junming Duan, Qian Wang, Jan S. Hesthaven
Accurate prediction of aerodynamic forces in real-time is crucial for autonomous navigation of unmanned aerial vehicles (UAVs). This paper presents a data-driven aerodynamic force…
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
Federico Pichi, Beatriz Moya, Jan S. Hesthaven
The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is…
Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems
Junming Duan, Jan S. Hesthaven
Reduced-order models are indispensable for multi-query or real-time problems. However, there are still many challenges to constructing efficient ROMs for time-dependent parametrize…