Data underlying chapter 6 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels

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.<br>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.

本仓库包含支撑学位论文《面向新型船舶前期设计的多保真度概率设计框架》第6章以及论文《利用信息论熵概念改进面向复杂船舶前期设计探索的多保真度设计框架》中所呈现结果的代码与数据。本研究探索了利用信息论熵改进基于高斯过程（Gaussian Processes）的多保真度设计框架。熵可量化设计空间预测过程中伴随的不确定性，本研究重点在于将该不确定性指标同时用作两项判定准则：一是确定是否需要采样额外设计以构建可靠的设计空间近似模型，二是确定在哪一个优化步骤中执行多保真度高斯过程的协方差矩阵优化。本仓库包含的数据包括：(1) 用于生成船体网格的Rhino与Grasshopper参数化模型；(2) 仿真数据集；(3) 与所分析的解析案例相关的数据；以及(4) 用于开发多保真度模型的Python脚本，相关内容均可在本仓库中获取。需注意，用于计算垂直弯矩的分析求解器未包含在本仓库中。