Summary of A Multi-fidelity Methodology For Reduced Order Models with High-dimensional Inputs, by Bilal Mufti et al.
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs
by Bilal Mufti, Christian Perron, Dimitri N. Mavris
First submitted to arxiv on: 26 Feb 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 proposed multi-fidelity, parametric, and non-intrusive Reduced Order Model (ROM) framework addresses the complexities of high-dimensional aerospace design spaces. By integrating machine learning techniques for manifold alignment and dimension reduction using Proper Orthogonal Decomposition (POD) and Model-based Active Subspace with multi-fidelity regression, the approach achieves improved cost-accuracy benefits and better predictive accuracy with reduced computational demands. The framework is validated through two test cases: the 2D RAE~2822 airfoil and the 3D NASA CRM wing, demonstrating its efficacy in handling scenarios with large input dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to design airplanes using special math models. These models help reduce the amount of computer time needed for designing new planes by combining different levels of detail. The approach uses machine learning techniques to simplify complex designs and make them easier to predict. Two examples are shown: an airfoil and a wing. This method is better than previous ones because it takes less time and gives more accurate results. |
Keywords
* Artificial intelligence * Alignment * Machine learning * Regression
