Summary of Generative Ai-based Prompt Evolution Engineering Design Optimization with Vision-language Model, by Melvin Wong et al.
Generative AI-based Prompt Evolution Engineering Design Optimization With Vision-Language Model
by Melvin Wong, Thiago Rios, Stefan Menzel, Yew Soon Ong
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 PEDO framework is an innovative approach to engineering design optimization that combines 3D shape representation, optimization algorithms, and design performance evaluation methods. It leverages a vision-language model to penalize impractical car designs synthesized by a generative model. The backbone of the framework is an evolutionary strategy coupled with an optimization objective function that comprises a physics-based solver and a vision-language model for practical or functional guidance in the generated car designs. The optimizer iteratively generates text prompts that embed user specifications on aerodynamic performance and visual preferences, fostering the evolution of viable designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Engineering design optimization is a complex problem that requires balancing multiple factors. Researchers have developed a new framework called PEDO that uses artificial intelligence to generate innovative designs while meeting specific requirements. The framework starts with a generative model that creates many possible car designs. Then, an evolutionary strategy is used to improve the designs based on how well they meet certain criteria, such as aerodynamics and appearance. |
Keywords
» Artificial intelligence » Generative model » Language model » Objective function » Optimization
