arXiv cs.AI· Hadi Keramati, Mohammed A. Hannan, Patrick Kirchen, Sina Tousi, Joonas Riekkinen, Jim Pyra, Rajeev K. Jaiman·· 6 天前AI 评分20
用扩散模型与高保真评估实现船体局部生成式设计
Localized Generative Design of Ship Hulls with Diffusion Models and High-Fidelity Assessment
AI 导读
一篇论文提出奖励导向的扩散框架，用于船体局部优化：数据集生成时用参数化掩码把几何变化限制在球鼻艏和前体等选定变量上，同时保持全船连续性，并以代理模型辅助的序贯蒙特卡洛采样降低预测静水阻力。
正文 · 原文
Abstract:Practical ship design requires localized refinement and performance evaluation. 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. At matched KRISO Container Ship (KCS) displacement, the generated variant has comparable total resistance at $F_n=0.195$ but 45-50% higher resistance at $F_n=0.250$ and $0.260$, missed by empirical screening. GenHull E achieves 6-8% lower total resistance up to $F_n=0.240$ but higher resistance above $F_n=0.250$ as opposing friction and pressure trends reverse the gains. Aggregating resistance across this crossover can conceal tradeoffs that emerge when friction and pressure components are evaluated separately. The assessment reveals an inability to represent a faired bulbous bow and a reward lacking viscous pressure and stability terms. These findings show how high-fidelity assessment informs improved representations and objectives for physically realizable generative designs.
| Subjects: | Analysis of PDEs (math.AP); Artificial Intelligence (cs.AI); Mathematical Physics (math-ph) |
| Cite as: | arXiv:2610.12870 [math.AP] |
| (or arXiv:2610.12870v1 [math.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12870 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hadi Keramati [view email]
[v1]
Fri, 9 Oct 2026 01:56:07 UTC (17,162 KB)
来源：arXiv cs.AI · arxiv.org