发表机构
Harbin Engineering University（哈尔滨工程大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出NL2Hull框架，将船型编辑形式化为类型化离散决策问题，结合NURBS和FFD引擎，构建含134,558条记录的SDD数据集，提出Chip模型达到95.90%准确率，为语言驱动的船型设计提供可复现评估接口。
AI 中文摘要
船型设计结合了光滑的几何表示、局部形状编辑以及对所得船体的约束。我们提出了自然语言到船体框架（NL2Hull框架），该框架将船型编辑形式化为一个类型化的离散决策问题，并将语言决策与数值几何联系起来。其约束自由变形引擎（CFFD引擎）使用非均匀有理B样条（NURBS）表示船体水线，对其控制点应用自由变形（FFD），重建船体，并检查几何约束。我们构建了船舶设计决策数据集（SDD数据集），包含134,558条清洗后的记录，并在其子集船舶设计决策基准（SDDBench）上评估比较模型，该基准包含5,000条记录和43,496个类型化问题。我们提出了Chip，一个用于处理自然语言请求的约束船舶设计决策模型。Chip达到了95.90%的问题准确率和99.32%的FFD精确匹配，负对数似然为0.0951，期望校准误差为0.0032，Brier得分为0.0551。NL2Hull框架为评估基于语言的船型决策提供了一个可复现的接口，同时识别了需要进一步发展的几何和连续控制组件。我们的代码和数据集可在该https URL获取。
英文摘要
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.
Comments23 pages, 8 figures, 10 tables
