ART of PIV：面向主动流动控制的智能体实时粒子图像测速
ART of PIV: Agentic Real-Time Particle Image Velocimetry for Active Flow Control
AI 导读
研究者提出 ART of PIV，用学习到的智能体为每对图像自动选择 PIV 流速估计管线配置，以匹配给定速度预算下的测量目标。在跨相关、经典光流与学习型估计器上，其在同等速度下将平均端点误差（AEPE）降低 12.2-22.4%，速度可达 1000-4000 图像对/秒；仿真速度调节任务中闭环成本较调优固定估计器降低 13.24-13.59 个百分点。
正文 · 原文
Abstract:Particle image velocimetry (PIV) measures fluid motion from images of tracer particles, but its accuracy depends on choices throughout the estimation pipeline, including image preprocessing, flow estimation, and postprocessing. These choices are typically left to expert tuning, and a fixed configuration can become less accurate as conditions change. This matters especially when PIV supplies feedback for real-time control, where measurements must be both accurate and fast, and flow conditions vary. We introduce ART of PIV -- short for Agentic Real-Time and also a nod to the art of tuning a PIV pipeline -- which automates pipeline choice for each image pair. A learned agent selects a configuration according to a chosen measurement objective from a library assembled for a specified speed budget. Across cross-correlation, classical optical-flow, and learning-based estimators, ART of PIV reduces average end-point error (AEPE) by 12.2-22.4 % relative to the best single estimator at matched speed, with gains extending to 1000-4000 image pair/s. In a simulated local velocity regulation task, it reduces mean normalized closed-loop cost by 13.24-13.59 percentage points relative to a tuned fixed estimator, with the same classical controller in both cases. In a tabletop water channel, we illustrate two ways to tune ART of PIV for a fluidic pinball of three rotating cylinders: selecting between complementary optical-flow tunings for accuracy, and selecting regions of interest with coherent rotation or shear using a common flow estimator. For the first variant, we train a DreamerV3 reinforcement learning controller directly in the physical world and demonstrate tracking of a prescribed flow field in the wake of a fluidic pinball.
| Subjects: | Fluid Dynamics (physics.flu-dyn); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.41304 [physics.flu-dyn] |
| (or arXiv:2610.41304v1 [physics.flu-dyn] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41304 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Antonio Terpin [view email]
[v1]
Fri, 30 Oct 2026 06:04:36 UTC (10,636 KB)
来源：arXiv cs.AI · arxiv.org