Data underlying chapter 3 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels
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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. 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 can be found in this gitlab repository.
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