research
∙
01/24/2023
A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems
Parametric time-dependent systems are of a crucial importance in modelin...
share
research
∙
02/18/2022
Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics
We investigate various data-driven methods to enhance projection-based m...
research
∙
01/26/2022
A Data-Driven Surrogate Modeling Approach for Time-Dependent Incompressible Navier-Stokes Equations with Dynamic Mode Decomposition and Manifold Interpolation
This work introduces a novel approach for data-driven model reduction of...
research
∙
01/14/2022
Reduced order modeling for spectral element methods: current developments in Nektar++ and further perspectives
In this paper, we present recent efforts to develop reduced order modeli...
research
∙
01/10/2022
Model Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations
This work investigates the use of sparse polynomial interpolation as a m...
research
∙
10/14/2020
A comparison of reduced-order modeling approaches for PDEs with bifurcating solutions
This paper focuses on reduced-order models (ROMs) built for the efficien...
research
∙
06/24/2020
