Summary of Physics-informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models For Engineering Design, by Shahroz Khan et al.
Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
by Shahroz Khan, Zahid Masood, Muhammad Usama, Konstantinos Kostas, Panagiotis Kaklis, Chen
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 physics-informed geometric operators (GOs) aim to enrich the geometric data for training surrogate/discriminative models, dimension reduction, and generative models by infusing high-level intrinsic geometric information and physics into the feature vector. This is achieved through Fourier descriptors, curvature integrals, geometric moments, and their invariants, which capture shape characteristics essential for performance analyses. The GOs regularise the model training, reducing over-fitting and enhancing generalisation to new designs. In dimension reduction and generative models, incorporating GOs enhances the quality of the resulting latent space, facilitating the generation of valid and diverse designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to make machine learning models better at understanding shapes by adding physical properties like geometry and physics to the data they learn from. This helps the models become more accurate and less prone to overfitting, making them better at predicting performance and creating new designs that are realistic and varied. |
Keywords
* Artificial intelligence * Latent space * Machine learning * Overfitting
