用深度神经算子优化海管涡激振动传感器位置，提升预测精度与效率。
DeepVIVONet: Using deep neural operators to optimize sensor locations with application to vortex-induced vibrations
- 基于深度神经算子构建动态重建与预报框架，支持稀疏时空数据
- 在不同流况下通过迁移学习实现外推，精度优于传统方法
- 可作为快速代理模型，用于传感器最优布局的闭环优化
我们提出 DeepVIVONet，一种基于现场数据的海上海管涡激振动（VIV）最优动态重构与预报新框架。该模型能利用稀疏的时空测量数据精确重建海管运动状态。通过迁移学习，模型在不同流况下表现出良好泛化能力，显著提升预测准确性并优化运维效率。训练后的 DeepVIVONet 可作为海管系统的快速高精度代理模型，嵌入外层优化算法中，自动求解传感器最优布设位置。我们还对比了基于本征正交分解（POD）的传统传感器布置方法，发现虽然 POD 可提供良好的初始布局，但 DeepVIVONet 的自适应能力可生成更精准且成本更低的配置方案。
原文摘要 · Abstract (English)
We introduce DeepVIVONet, a new framework for optimal dynamic reconstruction and forecasting of the vortex-induced vibrations (VIV) of a marine riser, using field data. We demonstrate the effectiveness of DeepVIVONet in accurately reconstructing the motion of an off--shore marine riser by using sparse spatio-temporal measurements. We also show the generalization of our model in extrapolating to other flow conditions via transfer learning, underscoring its potential to streamline operational efficiency and enhance predictive accuracy. The trained DeepVIVONet serves as a fast and accurate surrogate model for the marine riser, which we use in an outer--loop optimization algorithm to obtain the optimal locations for placing the sensors. Furthermore, we employ an existing sensor placement method based on proper orthogonal decomposition (POD) to compare with our data-driven approach. We find that that while POD offers a good approach for initial sensor placement, DeepVIVONet's adaptive capabilities yield more precise and cost-effective configurations.
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