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Machine-Learning and Data Science Techniques in String and Gauge Theories

Hirst, E. (2023). Machine-Learning and Data Science Techniques in String and Gauge Theories. (Unpublished Doctoral thesis, City, University of London)


Techniques from supervised and unsupervised machine learning, along with those from data and network science, are applied to generated datasets of mathematical objects relevant to string and gauge theories. Investigations show success in identifying and learning new structure associated to these objects. Datasets considered in the research work completed for this thesis include: dessins d’enfants, quivers, Hilbert series, amoebae, polytopes, Calabi-Yau manifolds, brane webs, and cluster algebras.

Publication Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology
Departments: School of Science & Technology > Mathematics
School of Science & Technology > School of Science & Technology Doctoral Theses
Doctoral Theses
[thumbnail of Hirst Thesis 2023 PDF-A.pdf]
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