Machine-Learning and Data Science Techniques in String and Gauge Theories
Lead Research Organisation:
City, University of London
Department Name: Sch of Engineering and Mathematical Sci
Abstract
Machine-Learning and Data Science Techniques in String and Gauge Theories
Organisations
People |
ORCID iD |
| Yang-Hui He (Primary Supervisor) | |
| Edward Hirst (Student) |
Publications
Bao J
(2021)
Dessins d'enfants, Seiberg-Witten curves and conformal blocks
in Journal of High Energy Physics
He Y
(2021)
Machine-learning dessins d'enfants: explorations via modular and Seiberg-Witten curves
in Journal of Physics A: Mathematical and Theoretical
Bao J
(2023)
Neurons on amoebae
in Journal of Symbolic Computation
Bao J
(2020)
Quiver mutations, Seiberg duality, and machine learning
in Physical Review D
Berman D
(2022)
Machine learning Calabi-Yau hypersurfaces
in Physical Review D
Bao J
(2022)
Hilbert series, machine learning, and applications to physics
in Physics Letters B
Arias-Tamargo G
(2022)
Brain webs for brane webs
in Physics Letters B
Bao J
(2022)
Some open questions in quiver gauge theory
in Proyecciones (Antofagasta)
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/S505341/1 | 01/10/2019 | 30/09/2023 | |||
| 2283750 | Studentship | ST/S505341/1 | 01/10/2019 | 30/03/2023 | Edward Hirst |
| Description | Data Science and Machine-Learning techniques have been tested ona variety of mathematical objects used in various subfields of geometry and theoretical physics. The successes of certain techniques have been documented, identifying a new computational avenue for research beyond techniques currently used. |
| Exploitation Route | More advanced techniques would hope to help identify new phenomena and hence uncover further interconnections within the mathematics that can aid interpretation and stimulate further research. |
| Sectors | Other |