TY - DATA T1 - Data underlying chapter 3 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels PY - 2025/02/20 AU - Nikoleta Dimitra Charisi AU - Hans Hopman AU - Austin Kana UR - DO - 10.4121/1dcda9bd-4ce6-4e0c-9b84-9292d4e101d0.v2 KW - conceptual design KW - data-driven design KW - design space exploration KW - multi-fidelity Gaussian processes KW - compositional kernels N2 - <p>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.</p><p><br></p><p>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<a href="https://gitlab.tudelft.nl/ndcharisi/mf-daf-for-novel-vessels.git" target="_blank"> gitlab repository</a>.</p> ER -