Data for thesis 'Predicting aerodynamics with hierarchical geometric deep learning: the GCN-MM-AE framework'
Data for thesis 'Predicting aerodynamics with hierarchical geometric deep learning: the GCN-MM-AE framework'
This dataset contains all the results obtained for the various results chapters of the PhD thesis, particularly Chapters 4 to 8.
These results were then used to generate the various results figures illustrated in the thesis. The multiple results files are classified according to the various folders.
As reported in the thesis, results were obtained either with SU2 CFD solver for the reference (ground-truth) and the implemented python/pytorch library developed during the research.
Feel free to contact the author if further information is required.
Dataset is available 'on request' only to bone fide researchers. Please complete the attached request form and we will seek approval for the request from the author.
geometric deep learning, aerodynamics
University of Southampton
Massegur Sampietro, David
d5bc71e8-f1b8-4c9f-9537-7ff63ad19426
Massegur Sampietro, David
d5bc71e8-f1b8-4c9f-9537-7ff63ad19426
Massegur Sampietro, David
(2025)
Data for thesis 'Predicting aerodynamics with hierarchical geometric deep learning: the GCN-MM-AE framework'.
University of Southampton
doi:10.5258/SOTON/D3514
[Dataset]
Abstract
This dataset contains all the results obtained for the various results chapters of the PhD thesis, particularly Chapters 4 to 8.
These results were then used to generate the various results figures illustrated in the thesis. The multiple results files are classified according to the various folders.
As reported in the thesis, results were obtained either with SU2 CFD solver for the reference (ground-truth) and the implemented python/pytorch library developed during the research.
Feel free to contact the author if further information is required.
Dataset is available 'on request' only to bone fide researchers. Please complete the attached request form and we will seek approval for the request from the author.
Text
D3514_README.txt
- Dataset
Text
Request_Form_Access_D3514.docx
- Dataset
More information
Published date: May 2025
Keywords:
geometric deep learning, aerodynamics
Identifiers
Local EPrints ID: 501734
URI: http://eprints.soton.ac.uk/id/eprint/501734
PURE UUID: 3a8c9565-99b0-470f-b1d7-a233251973e9
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Date deposited: 09 Jun 2025 17:30
Last modified: 10 Jun 2025 02:05
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Contributors
Creator:
David Massegur Sampietro
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