31 citations · 56 across the 4 of their papers we have counts for
5 papers
PMRT: A Training Recipe for Fast, 3D High-Resolution Aerodynamic Prediction
Sam Jacob Jacob, Markus Mrosek, Carsten Othmer +1
The aerodynamic optimization of cars requires close collaboration between aerodynamicists and stylists, while slow, expensive simulations remain a bottleneck. Surrogate models have…
Benchmarking Convolutional Neural Network and Graph Neural Network based Surrogate Models on a Real-World Car External Aerodynamics Dataset
Sam Jacob Jacob, Markus Mrosek, Carsten Othmer +1
Aerodynamic optimization is crucial for developing eco-friendly, aerodynamic, and stylish cars, which requires close collaboration between aerodynamicists and stylists, a collabora…
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…