用合成数据训练的神经网络，快速准确监测海上风电桩基结构状态。
A Synthetic Reliability-Aware PINN Benchmark for Offshore Wind Turbine Support-Structure Monitoring with Bayesian Inverse Identification
- 将梁模型与土壤基础嵌入神经网络损失函数，融合物理规律。
- 结合贝叶斯反演与可靠性方法，在少量数据下实现高精度状态估计。
- 适合做风电结构健康监测的算法验证，尤其关注可靠性评估。
海上风电桩基结构的可靠健康监测需从稀疏测量中快速估计结构状态。直接使用高保真有限元或气动弹性分析难以用于在线监测，而纯数据驱动的代理模型又需要大量训练数据。本文提出 Digi Turbine，一个面向海上风电单桩结构监测的合成可靠性感知物理信息神经网络（PINN）基准。该工作流程在训练目标中嵌入简化的欧拉-伯努利梁方程与温克勒地基模型，结合贝叶斯先验引导的逆向识别，并引入一阶可靠性方法（FORM）筛选。所有验证均基于合成配置，其真实值来自解析解或有限差分法，参考场景为 NREL 5MW 参考风机。
原文摘要 · Abstract (English)
Reliable structural health monitoring (SHM) of offshore wind turbine (OWT) support structures requires fast state estimation from sparse measurements. Repeated high fidelity finite element or aeroelastic analyses are difficult to use directly in online monitoring loops, while purely data-driven surrogates can require large training sets. This paper presents Digi Turbine, a synthetic reliability-aware Physics Informed Neural Network (PINN) benchmark for OWT monopile support structure monitoring. The workflow embeds a simplified Euler Bernoulli beam equation with Winkler soil foundation in the training objective, couples it with Bayesian-prior-informed inverse identification, and adds First Order Reliability Method (FORM) screening. All validation uses synthetic configurations with analytical or finite-difference ground truth motivated by the NREL 5MW reference turbine context.
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