DrivAerNet is a large-scale, high-fidelity CFD dataset of 3D industry-standard car shapes designed for data-driven aerodynamic design. It comprises 4000 high-quality 3D car meshes and their corresponding aerodynamic performance coefficients, alongside full 3D flow field information.
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Given the lack of consensus on a standard setof benchmarks for machine learning of PDEs, we propose a new suite of benchmarks here. Our aims in this regard are to ensure i) sufficient diversity among the types of PDE considered ii) access to training and test data is readily available for rapid prototyping and reproducibility iii) intrinsic computational complexity of problem to make sure that it is worthwhile to design fast surrogates to classical PDE solvers for a particular problem
We present a structured benchmark dataset for a representative vibroacoustic problem: Predicting the frequency response for vibrating plates. The vibrating plates benchmark dataset consists of in total 12,000 varied plate designs and accompanying vibration patterns, when the plates are excited by a harmonic force. These vibration platterns give the vibration velocity at every location of the plate orthogonal to its surface. The plate designs incorporate randomly placed beadings, indentations in the plate surface. The beadings stiffen the plates and completely change the resulting vibration patterns. Additionally, the size, thickness and damping loss factor of the plates are varied.