Localized Generative Design of Ship Hulls with Diffusion Models and High-Fidelity Assessment
Practical ship design requires localized refinement and performance evaluation.
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Key points
- This paper presents a reward-directed diffusion framework for localized hull optimization.
- Parametric masking during dataset generation restricts geometric changes to selected variables, including the bulbous bow and forebody, while preserving full-hull continuity.
- Surrogate-assisted sequential Monte Carlo sampling targets reduced predicted calm-water resistance, with stability retained through training data and displacement matched during screening.
- An automated viscous free-surface CFD pipeline, validated against measurements for two benchmark container ships, evaluates a generated variant and GenHull E, a hull refined by a naval architect within the same representation.
Sources (1)
- [1]Localized Generative Design of Ship Hulls with Diffusion Models and High-Fidelity AssessmentarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 01:56 AM
Practical ship design requires localized refinement and performance evaluation.
This paper presents a reward-directed diffusion framework for localized hull optimization.
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