NL2Hull 框架用自然语言驱动船舶船体设计决策
NL2Hull: A Natural Language-Driven Constrained Ship Design Decision Framework
研究者把自然语言接进船体设计：Chip 模型在 SDDBench 上答对 95.90% 的设计问题，NURBS 加 FFD 直接生成几何，代码数据集全开源，搞 CAD 或几何方向可以看看。
NL2Hull 框架把船舶船型编辑建模为类型化离散决策问题，通过 CFFD Engine 用 NURBS 表示水线并施加自由变形（FFD）后重建船体、校验几何约束。团队构建了含 134,558 条清洗记录的 SDD Dataset，并在其 5,000 条记录、43,496 个类型化问题的子集 SDDBench 上评估模型。其提出的 Chip 模型达到 95.90% 问题准确率和 99.32% FFD 精确匹配，负对数似然 0.0951，期望校准误差 0.0032，Brier 分数 0.0551。代码与数据集已在 GitHub 开源。
NL2Hull: A Natural Language-Driven Constrained Ship Design Decision Framework
Ship-form design combines smooth geometric representation, local shape editing, and constraints on the resulting hull. We present the Natural-Language-to-Hull Framework (NL2Hull Framework), which formulates ship-form editing as a typed discrete decision problem and connects language decisions to numerical geometry. Its Constrained Free-Form Deformation Engine (CFFD Engine) represents hull waterlines with non-uniform rational B-splines (NURBS), applies free-form deformation (FFD) to their control points, reconstructs the hull, and checks geometric constraints. We construct the Ship Design Decision Dataset (SDD Dataset) with 134,558 cleaned records and evaluate compared models on its subset Ship Design Decision Benchmark (SDDBench), containing 5,000 records and 43,496 typed questions. We propose Chip, a constrained ship-design decision model for processing natural-language requests. Chip reaches 95.90\% question accuracy and 99.32\% FFD exact match, with a negative log-likelihood of 0.0951, an expected calibration error of 0.0032, and a Brier score of 0.0551. The NL2Hull Framework provides a reproducible interface for evaluating language-based ship-form decisions while identifying the geometry and continuous-control components that require further development. Our code and dataset is available at https://github.com/wenhuahuo/NL2Hull.