发表机构
University of Peradeniya（佩拉德尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究审计气道流动神经算子的零样本组合，发现局部正则化虽改善局部诊断，但全局场误差和残差增大，局部一致性不保证全局守恒。
AI 中文摘要
神经算子在一族几何内逼近偏微分方程解，但独立学习的局部算子未必能形成一致的全局模拟器。我们研究了理想化二维气道树中稳态不可压缩流的冻结、单次传递组合。分别训练的管、分叉和三叉DeepONet在4,872个原始CFD案例上使用场监督和辅助散度、端口通量、分量平衡和端口压力惩罚进行训练。验证选择的部署在检查整个树CFD场之前被冻结，并在没有树训练、迭代耦合、通量校正或CFD知情调整的情况下组装。它保留了主要流动模式和受控病理反应，CPU推理时间为0.204-0.215秒，但具有22.68%的预设入口归一化外部残差。事后敏感性协议在冻结后用于新训练和评估，重复Data、Div和Full模型在三个种子下，Tube固定。相对于Data，Full将Y2和Y3的原始复合分数分别降低26.7%和26.4%，并将组装的分量残差和界面失配RMS分别降低7.2%和16.6%。然而，平均树速度误差增加7.5%，而外部残差从17.49±4.38%增加到26.77±3.65%。因此，局部正则化可以改善原始和组装的局部诊断，而不保证准确的全局场或守恒。
英文摘要
Neural operators approximate PDE solutions within a geometry family, but independently learned local operators need not form a consistent global simulator. We study frozen, single-pass composition for steady incompressible flow in idealized two-dimensional airway trees. Separate Tube, bifurcation, and trifurcation DeepONets are trained on 4,872 primitive CFD cases using field supervision and auxiliary divergence, port-flux, component-balance, and port-pressure penalties. The validation-selected deployment is frozen before whole-tree CFD fields are inspected and assembled without tree training, iterative coupling, flux correction, or CFD-informed adjustment. It retains major flow patterns and controlled pathology responses with 0.204-0.215 s CPU inference, but has a 22.68% prescribed-inlet-normalized external residual. A post-hoc sensitivity protocol, frozen before new training and evaluation, repeats Data, Div, and Full models across three seeds with Tube fixed. Relative to Data, Full reduces primitive composite scores by 26.7% for Y2 and 26.4% for Y3 and reduces assembled component-residual and interface-mismatch RMS by 7.2% and 16.6%, respectively. Nevertheless, mean tree velocity error increases by 7.5%, while external residual increases from 17.49 +/- 4.38% to 26.77 +/- 3.65%. Local regularization can therefore improve primitive and assembled local diagnostics without ensuring accurate global fields or conservation.
