Scripts and data underlying the publication that defines and applies the new Adaptive Screening method, for extreme value prediction of non-linear wave-induced responses

This set of scripts and data files can be used to re-generate the Adaptive Screening method and the three applications described in the paper "Designing for dangerous waves – a new ‘Adaptive Screening’ method to predict extreme values of non-linear marine and coastal structure responses to waves".<br>Predicting extreme values of strongly non-linear hydrodynamic responses (such as wave impact loads) is crucial for ensuring the safety and reliability of marine and coastal structures. However, this task is challenging due to the complexity and rarity of these responses. Existing methods are often limited to weakly non-linear responses or are very computationally expensive. This paper presents a new multi-fidelity method called ‘Adaptive Screening’, designed to efficiently predict extreme values of strongly non-linear wave-induced responses. Predicting these values is a critical element of structural design and reliability analysis. Adaptive Screening combines elements of screening, multi-fidelity Gaussian Process Regression, and adaptive sampling. We validate its effectiveness through three applications: predicting the most probable maxima of second-orderwave crests, vertical bending moments on a ferry, and greenwater impact loads on a containership. Our results demonstrate that Adaptive Screening outperforms conventional brute force methods, achieving comparable accuracy in predicting extreme values while significantlyreducing high-fidelity simulation times (especially for the most non-linear cases). Like many alternative methods, Adaptive Screening relies on a response-dependent low-fidelity indicator variable. We also show that the method performs well with realistic indicators for a range of applications. The test cases indicate that Adaptive Screening is very promising for the strongly non-linear responses it was designed for.

本脚本集与数据文件可用于复现发表于论文《面向危险波浪：一种用于预测波浪作用下非线性海洋与海岸结构响应极值的新型“自适应筛选（Adaptive Screening）”方法》中所述的自适应筛选方法及三项应用案例。
精准预测强非线性水动力响应（如波浪冲击荷载）的极值，是保障海洋与海岸结构安全可靠的核心环节。但此类响应兼具复杂性与稀有性，使得该任务极具挑战。现有方法往往仅适用于弱非线性响应场景，或存在计算成本过高的问题。
本论文提出了一种名为“自适应筛选”的新型多保真度方法，旨在高效预测强非线性波浪诱导响应的极值。此类极值的预测是结构设计与可靠性分析的关键环节。自适应筛选整合了筛选分析、多保真度高斯过程回归（Gaussian Process Regression）与自适应采样等技术思路。
我们通过三项应用案例验证了该方法的有效性：分别为二阶波峰最概然极大值预测、渡轮垂向弯矩预测，以及集装箱船的上浪冲击荷载预测。实验结果表明，自适应筛选的性能优于传统蛮力法：在保证极值预测精度相当的前提下，可大幅缩短高保真仿真的耗时（针对强非线性场景尤为显著）。
与诸多替代方法类似，自适应筛选依赖于与响应相关的低保真度指示变量。同时我们还证实，针对一系列应用场景，采用符合实际的指示变量时，该方法仍可保持优异性能。测试案例表明，自适应筛选针对其设计目标的强非线性响应场景，展现出极高的应用潜力。