Scripts and data underlying the paper that introduces and validates the Probabilistic Adaptive Screening method

This set of scripts and data files underlies the publication that introduces and validates the Probabilistic Adaptive Screening (PAS) method. <br>Accurately predicting the extreme (or design) values of wave impact loads is crucial to ensure the safety and reliability of marine and coastal structures. However, this task is challenging due to the complexity and rarity of these strongly non-linear loads. Existing methods are often limited to weakly non-linear loads, or are very computationally expensive. The paper connected to this dataset introduces a new multi-fidelity screening method, Probabilistic Adaptive Screening (PAS), which predicts extreme values of strongly non-linear wave-induced loads while minimising the required high-fidelity simulation duration. The method introduces a probabilistic approach to multi-fidelity screening, allowing efficient linear potential flow indicators to be used in the low-fidelity stage, even for strongly non-linear load cases. The method is validated against a range of cases, including non-linear waves, vertical bending moments, green water impact loads, and slamming loads. <br>In the paper, it is concluded that PAS accurately estimates both the distributions and extreme values in all test cases, with most probable maximum (MPM) values within 10% of the available full brute-force Monte-Carlo Simulation (MCS) results. In addition, PAS achieves this performance very efficiently, requiring less than 1.7% of the high-fidelity simulation time needed for conventional MCS. These results demonstrate that PAS can reliably reproduce the statistics of both weakly and strongly non-linear extreme load problems, while significantly reducing the associated computational cost. The study validates the statistical PAS framework; further work should focus on validating the full procedure including CFD load simulations.