This repository contains the code and data supporting the results presented in Chapter 6 of the dissertation "Multi-Fidelity Probabilistic Design Framework for Early-Stage Design of Novel Vessels" and the paper "Leveraging the concept of information-theoretic entropy to improve a multi-fidelity design framework for early-stage design exploration of complex vessels". The research explores the adoption of information-theoretic entropy to improve a multi-fidelity design framework based on Gaussian Processes. Entropy quantifies the uncertainty associated with the prediction of the design space. The research focuses on using this uncertainty metric both as a criterion to determine whether further designs should be sampled to construct a reliable approximation of the design space and as a criterion to establish in which optimization step the optimization of the covariance matrix for the multi-fidelity Gaussian Processes should be performed.
The data include: (1) the parametric model developed in Rhino and Grasshopper used to generate the hull mesh, (2) the simulation dataset, (3) the data associated with the analyzed analytical cases, and (4)the Python scripts for the development of the MF models can be found in this gitlab repository. The analysis solvers used to calculate the vertical bending moments for calculating the vertical bending moments are not included in this repository.
" authors: - family-names: Charisi given-names: Nikoleta Dimitra orcid: "https://orcid.org/0009-0006-8715-4652" - family-names: Hopman given-names: Hans orcid: "https://orcid.org/0000-0002-5404-5699" - family-names: Kana given-names: Austin orcid: "https://orcid.org/0000-0002-9600-8669" title: "Data underlying chapter 6 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels" keywords: version: 1 identifiers: - type: doi value: 10.4121/459cfd14-ab90-4fbb-9a1f-2cbd96baaa1a.v1 license: CC BY 4.0 date-released: 2024-11-25