用神经算子预测游动生物的流场，速度快精度高。
Neural Operators for Immersed-Boundary Soft Swimmers Locomotion
- 用神经算子建模变形体与流体的耦合运动，输入为几何和雷诺数。
- 平面模型在高雷诺数下全局相对误差仅3.51%，体积模型误差3.44%~19.2%。
- 适合需要快速仿真设计的流体-结构耦合研究者使用。
高保真浸入边界模拟可精确解析变形游泳体与其周围流场的耦合运动，但计算成本高，限制了工程设计、参数分析和控制中的重复评估。本文开发了神经算子代理模型，用于平面和立体鳗鱼游动体的时序流场预测。代理模型在自适应流固耦合模拟输出的规则网格数据上训练，条件输入为游泳体几何形状和雷诺数。平面模型联合预测两个速度分量、标量涡量和压力；在五个高雷诺数留出轨迹上，其全域全局相对 L^2 误差为 3.51%。立体模型采用三个针对不同目标的子模型，共享多通道输入：一个预测三维速度（误差 3.44%），一个预测涡量（误差 5.58%），一个预测压力（误差 19.2%）。结果表明，场分辨神经代理模型可用于移动边界游泳体流场预测，同时指出压力精度与物理一致性是未来改进的重点。
原文摘要 · Abstract (English)
High-fidelity immersed-boundary simulation resolves the coupled motion of a deforming swimmer and its surrounding flow, but the resulting cost limits repeated evaluations for engineering design, parameter studies, and control. We develop neural-operator surrogates for temporal prediction of the hydrodynamic fields generated by planar and volumetric eel swimmers. The surrogates are trained on regular-grid fields exported from adaptive fluid--structure simulations and are conditioned on swimmer geometry and Reynolds number. The planar model jointly predicts two velocity components, scalar vorticity, and pressure. On five held-out high-Reynolds-number trajectories, its full-domain global relative L^2 error is 3.51 %. The volumetric formulation uses three target-specific models with a common multichannel input: one model predicts three-dimensional velocity, one predicts vorticity, and one predicts pressure. Their full-domain global relative L^2 errors on five held-out within-range trajectories are 3.44 %, 5.58 %, and 19.2 %. Together, the results demonstrate the feasibility of field-resolved neural surrogates for moving-boundary swimmer flows while identifying pressure accuracy and physical consistency as priorities for further development.
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