Summary of Generative Ai in Ship Design, by Sahil Thakur et al.
Generative AI in Ship Design
by Sahil Thakur, Navneet V Saxena, Prof Sitikantha Roy
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 generative AI model utilizes machine learning and artificial intelligence algorithms to optimize ship hull design. By creating a novel approach that replaces traditional human-driven iterative processes, the report outlines the systematic creation of this generative AI for ship design. The model architecture selection involves Gaussian Mixture Model (GMM), which offers a statistical framework to analyze data distribution crucial for generating innovative ship designs efficiently. The GMM is trained and validated using the “SHIP-D” dataset consisting of 30,000 hull forms. This approach has the potential to revolutionize ship design by exploring a broader design space and integrating multidisciplinary optimization objectives effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses artificial intelligence to make designing ships more efficient. It creates an AI model that can optimize ship hull designs without needing human input. The model is trained on data from 30,000 different ship hulls. This new approach has the potential to change how ships are designed by letting the AI explore many different options and choose the best one. |
Keywords
» Artificial intelligence » Machine learning » Mixture model » Optimization
