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.
" 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 3 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels" keywords: version: 1 identifiers: - type: doi value: 10.4121/1dcda9bd-4ce6-4e0c-9b84-9292d4e101d0.v1 license: CC BY 4.0 date-released: 2024-11-25