Summary of A Systematic Dataset Generation Technique Applied to Data-driven Automotive Aerodynamics, by Mark Benjamin et al.
A systematic dataset generation technique applied to data-driven automotive aerodynamics
by Mark Benjamin, Gianluca Iaccarino
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach for generating datasets in the context of drag prediction for automotive geometries using neural networks. The primary challenge is constructing a training database of sufficient size and diversity, which this strategy aims to address by interpolating between starting data points. A realistic automotive geometry is used to test the method, demonstrating that convolutional neural networks perform well at predicting drag coefficients and surface pressures. Extrapolation performance is also promising, suggesting potential applications in aerodynamic shape optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps create datasets for cars using artificial intelligence (AI) techniques. The problem is making a big enough training database with diverse information. This new method uses a few starting points to make more data by filling in the gaps. It works well on a real car shape, showing that AI can predict drag and air pressure accurately. The results are also good when using it for predictions outside of the original data. |
Keywords
» Artificial intelligence » Optimization
