This repository can be used to re-generate the results in the paper referenced below. Abstract of the paper: "The Adaptive Screening extreme value prediction method defined in \cite{VS2025} was applied to three cases, predicting most probable maximum values in a given duration. We used either Gaussian Process Regression (GPR) or its multi-fidelity form (MF-GPR) for the regression, and compared seven acquisition functions for the adaptive sampling. The results were judged based on several performance metrics, of which the required number of HF samples for convergence is the most important one. The results of this exercise show that the best option is to use Adaptive Screening with MF-GPR and an acquisition function that balances obtaining new samples around the probability of interest and the tail of the distribution. If the use of MF-GPR is not feasible for any reason, it should be replaced with GPR (or Generalised Pareto fitting) combined with an acquisition function that selects new samples based on the largest probability gap from the existing samples."
Van Essen, S.M. and Seyffert, H.C. (2025). Designing ships for extreme non-linear responses - the role of the acquisition function in the Adaptive Screening method. 16th Practical Design of Ships and other Floating Structures (PRADS) Conference, 19-23 Oct, Ann Arbor, USA.
" authors: - family-names: van Essen given-names: Sanne orcid: "https://orcid.org/0000-0001-8239-0724" - family-names: Seyffert given-names: Harleigh orcid: "https://orcid.org/0000-0003-0323-2096" title: "Scripts and data for PRADS publication that varies the acquisition function of the Adaptive Screening method" keywords: version: 1 identifiers: - type: doi value: 10.4121/12777259-c2f6-4b44-b71f-eec5557824d1.v1 license: CC BY 4.0 date-released: 2025-05-14