PDOPT: A Python library for Probabilistic Design space exploration and OPTimisation
| dc.contributor.author | Spinelli, Andrea | |
| dc.contributor.author | Kipouros, Timoleon | |
| dc.date.accessioned | 2024-03-19T14:36:46Z | |
| dc.date.available | 2024-03-19T14:36:46Z | |
| dc.date.issued | 2024-03-05 | |
| dc.description.abstract | Contemporary engineering design is characterised by products and systems with increasing complexity coupled with tighter requirements and tolerances. This leads to high epistemic uncertainty due to numerous possible configurations and a high number of design parameters. Set-Based Design is a methodology capable of handling these design problems, by exploring and evaluating as many alternatives as possible, before committing to a specific solution. The Python package PDOPT aims to provide this capability without the high computational cost associated with the factorial-based design of experiments methods. Additionally, PDOPT performs the requirement mapping without explicit rule definition. Instead, it utilizes a probabilistic machine learning model to identify the areas of the design space most promising for user-provided requirements. This yields a plethora of feasible design points, assisting designers in understanding the system behaviour and selecting the desired configurations for further development. | en_UK |
| dc.identifier.citation | Spinelli A, Kipouros T. (2024) PDOPT: A Python library for Probabilistic Design space exploration and OPTimisation. Journal of Open Source Software, Volume 9, Issue 95, Article number 6110 | en_UK |
| dc.identifier.issn | 2475-9066 | |
| dc.identifier.uri | http://doi.org/10.21105/joss.06110 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/21044 | |
| dc.language.iso | en_UK | en_UK |
| dc.publisher | The Open Journal | en_UK |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.title | PDOPT: A Python library for Probabilistic Design space exploration and OPTimisation | en_UK |
| dc.type | Article | en_UK |
| dcterms.dateAccepted | 2024-03-05 |