33 citations · 89 across the 5 of their papers we have counts for
6 papers
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
Francesco Romor, Marco Tezzele, Markus Mrosek +2
Multi-fidelity models are of great importance due to their capability of fusing information coming from different numerical simulations, surrogates, and sensors. We focus on the ap…
Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes
Sam Jacob Jacob, Markus Mrosek, Carsten Othmer +1
The aerodynamic optimization process of cars requires multiple iterations between aerodynamicists and stylists. Response Surface Modeling and Reduced-Order Modeling are commonly us…
Hybrid neural network reduced order modelling for turbulent flows with geometric parameters
Matteo Zancanaro, Markus Mrosek, Giovanni Stabile +2
Geometrically parametrized Partial Differential Equations are nowadays widely used in many different fields as, for example, shape optimization processes or patient specific surger…
Parametric solutions of turbulent incompressible flows in OpenFOAM via the proper generalised decomposition
Vasileios Tsiolakis, Matteo Giacomini, Ruben Sevilla +2
An a priori reduced order method based on the proper generalised decomposition (PGD) is proposed to compute parametric solutions involving turbulent incompressible flows of interes…
Quantum-assisted finite-element design optimization
Dyon van Vreumingen, Florian Neukart, David Von Dollen +4
Quantum annealing devices such as the ones produced by D-Wave systems are typically used for solving optimization and sampling tasks, and in both academia and industry the characte…
Nonintrusive proper generalised decomposition for parametrised incompressible flow problems in OpenFOAM
Vasileios Tsiolakis, Matteo Giacomini, Ruben Sevilla +2
The computational cost of parametric studies currently represents the major limitation to the application of simulation-based engineering techniques in a daily industrial environme…