利用操作网络预测时间依赖性流动过复杂几何结构
Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
AI总结：
本文提出了一种时间依赖的深度操作网络，用于预测复杂几何结构周围流动的速度场，通过几何感知和历史流动编码，实现高精度和高效模拟。
摘要：
快速、通用化的非稳态流动代理仍然具有挑战性。我们提出了一种时间依赖的、几何感知的深度操作网络，用于预测参数化和非参数化形状周围中等雷诺数流动的速度场。该模型通过符号距离场（SDF）主干编码几何，并通过CNN分支编码流动历史，训练于841个高保真模拟上。在测试形状上，其达到约5%的相对L2单步误差，并在CFD上实现高达1000倍的速度提升。我们提供以物理为中心的滚动诊断，包括探测器处的相位误差和发散范数，以量化长时间范围内的保真度。这些结果揭示了在短期瞬态中准确，但在细尺度涡流中误差累积，尤其在尖锐角落几何中最为显著。我们分析了失败模式并概述了实际的缓解措施。代码、划分和脚本已公开发布在：https://github.com/baskargroup/TimeDependent-DeepONet以支持可重复性和基准测试。
英文摘要：
Developing neural surrogates for unsteady flow that generalize across complex geometries and maintain accuracy over extended rollouts remains a critical challenge for accelerating flow simulations. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for moderate-Re flows around parametric and non-parametric shapes. The model encodes geometry via a signed distance field (SDF) trunk and flow history via a CNN branch, and is trained using 841 high-fidelity simulations from the FlowBench dataset (https://baskargroup.bitbucket.io/). The model attains $\sim 5\%$ relative L2 single-step error and up to 1000x speedups over CFD on 262 test geometries not used for training. We provide physics-centric rollout diagnostics, including probe-based phase lag and Strouhal frequency analysis, to quantify long-horizon fidelity. These reveal accurate near-term transients but systematic error accumulation in fine-scale wakes, most pronounced for sharp-cornered geometries. We analyze failure modes and outline practical mitigation strategies including physics-informed regularization and diffusion-based refinement. Code, splits, and scripts are openly released at: https://github.com/baskargroup/TimeDependent-DeepONet to support reproducibility and benchmarking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
