Data underlying chapter 3 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 3 of the dissertation "Multi-Fidelity Probabilistic Design Framework for Early-Stage Design of Novel Vessels" and the paper "Multi-fidelity design framework integrating compositional kernels to facilitate early-stage design exploration of complex systems". The research explores the integration of compositional kernels into the autoregressive scheme (AR1) of Multi-Fidelity Gaussian Processes, aiming to enhance the predictive accuracy and reduce uncertainty in design space estimation. The effectiveness of this method is assessed by applying it to 5 benchmark problems and a simplified design scenario of a cantilever beam.<br>The data include: (1) the Ansys model of the cantilever beam, (2) the simulation data, (3) the data associated with the analyzed cases, and (4)the Python scripts<strong> </strong>can be found in this gitlab repository.

本仓库包含支撑学位论文《面向新型船舶早期设计的多保真度概率设计框架》第3章以及学术论文《融合组合核函数以助力复杂系统早期设计探索的多保真度设计框架》中所呈现研究结果的代码与数据集。
本研究探索了将组合核函数集成至多保真度高斯过程（Multi-Fidelity Gaussian Processes）的自回归格式（AR1）中，旨在提升预测精度并降低设计空间估计过程中的不确定性。
本研究通过将该方法应用于5个基准测试问题与一个简化的悬臂梁设计场景，对其有效性进行了验证。
本数据集包含：(1) 悬臂梁的Ansys模型、(2) 仿真数据、(3) 与所分析案例相关的数据集，以及(4) Python脚本，所有相关资源均可在本GitLab仓库中获取。