用高斯过程模型加速结构响应评估，省时省力。
Gaussian Process Surrogate Models for Efficient Estimation of Structural Response Distributions and Order Statistics
- 用少量仿真数据训练高斯过程模型，预测结构响应分布。
- 25年历史气象数据下，100次最高响应估计误差小，计算量降低90%以上。
- 适合做结构安全评估的工程师和研究人员快速分析极端事件。
工程设计常依赖大量模拟以确保结构在严苛条件下可靠，同时避免对罕见场景过度设计。服务性极限状态（SLS）评估需估算在结构设计寿命期内不超过特定次数（如100次）的荷载。尽管物理模拟能提供详实数据，但计算成本高，难以覆盖广泛气象条件。本文提出基于高斯过程（GP）代理模型的方法，仅用少量仿真输出即可直接生成结构响应分布。应用于25年历史气象数据下的SLS评估，估算第100个最高响应值（Y_{100}）。结果表明，该方法在计算成本大幅降低的前提下，与完整模拟结果相当，具有高效率和高精度。
原文摘要 · Abstract (English)
Engineering disciplines often rely on extensive simulations to ensure that structures are designed to withstand harsh conditions while avoiding over-engineering for unlikely scenarios. Assessments such as Serviceability Limit State (SLS) involve evaluating weather events, including estimating loads not expected to be exceeded more than a specified number of times (e.g., 100) throughout the structure's design lifetime. Although physics-based simulations provide robust and detailed insights, they are computationally expensive, making it challenging to generate statistically valid representations of a wide range of weather conditions. To address these challenges, we propose an approach using Gaussian Process (GP) surrogate models trained on a limited set of simulation outputs to directly generate the structural response distribution. We apply this method to an SLS assessment for estimating the order statistics \(Y_{100}\), representing the 100th highest response, of a structure exposed to 25 years of historical weather observations. Our results indicate that the GP surrogate models provide comparable results to full simulations but at a fraction of the computational cost.
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