发表机构
College of Shipbuilding Engineering, Harbin Engineering University（哈尔滨工程大学船舶工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对船舶水动力学中大型非均匀点集注意力模型成本高的问题，提出基于自适应面片划分的APPSolver框架，验证其可降低MAE并实现精度-效率权衡，代码公开。
AI 中文摘要
大型非均匀点集使得基于直接注意力的替代模型在船舶水动力学中计算成本高昂。我们提出APPSolver，这是一种基于自适应面片划分（APP）的逐点流场预测框架，APP是从船舶CFD模拟中提取的固定二维水平切片的确定性四叉树表示。APP在船体附近分配更精细的面片，在远处分配更粗糙的面片，对面片内容进行下采样，并将预测恢复到完整参考点集。在修正协议下（该协议在拆分前构建自然(t,t+1)对、复用训练集归一化统计量并报告三个模型种子），学习到的分词器比APP-Transformer更准确，而持久性基线在所有三个ShipBench船体上的一步MAE更低。因此，APP的有益之处在于计算效率而非普遍预测优势：在代表性DTC输入上，APP-Transformer每次模型前向传播需要1.815 GFLOPs和1.309 ms，而匹配的消融实验显示，与采用学习切片的均匀划分相比，自适应划分将MAE降低了16.4%-24.9%。条件编码器在留一船体评估中提供了依赖于设置的增益，但当前的绝对下一状态目标无法建立准确的长时动力学。这些结果表明APP是一种紧凑的空间表示，具有明确的精度-效率权衡。代码可在该https URL获取。
英文摘要
Large non-uniform point sets make direct attention-based surrogate modeling costly for ship hydrodynamics. We introduce APPSolver, a point-wise flow-prediction framework built around Adaptive Patch Partitioning (APP), a deterministic quadtree representation for fixed two-dimensional horizontal slices extracted from ship CFD simulations. APP assigns finer patches near the hull and coarser patches farther away, downsamples patch contents, and recovers predictions to the full reference point set. Under a corrected protocol that constructs natural $(t,t+1)$ pairs before splitting, reuses training-set normalization statistics, and reports three model seeds, learned tokenizers are more accurate than APP-Transformer, and a persistence baseline has lower one-step MAE on all three ShipBench hulls. The supported benefit of APP is therefore computational rather than universal predictive superiority: on a representative DTC input, APP-Transformer requires 1.815 GFLOPs and 1.309 ms per model forward, while a matched ablation shows that adaptive partitioning reduces MAE by 16.4-24.9\% relative to a uniform partition augmented with learned slicing. Condition encoders provide setting-dependent gains in leave-one-hull-out evaluation, but the current absolute next-state objective does not establish accurate long-horizon dynamics. These results characterize APP as a compact spatial representation with an explicit accuracy--efficiency trade-off. Code is available at https://github.com/wenhuahuo/APPSolver .
Comments14 pages, 2 figures. Code: https://github.com/wenhuahuo/APPSolver
