Browsing Awaiting Allocation by Author "Hirst, E"

Now showing items 1-13 of 13

    • Brain webs for brane webs 

      Arias-Tamargo, G; He, YH; Heyes, E; Hirst, E; Rodriguez-Gomez, D (2022-10-10)
      We propose a new technique for classifying 5d Superconformal Field Theories arising from brane webs in Type IIB String Theory, using technology from Machine Learning to identify different webs giving rise to the same theory. ...

    • Cluster algebras: Network science and machine learning 

      Dechant, P-P; He, Y-H; Heyes, E; Hirst, E (Elsevier, 2023-12)

    • Dessins d’enfants, Seiberg-Witten curves and conformal blocks 

      Bao, J; Foda, O; He, YH; Hirst, E; Read, J; Xiao, Y; Yagi, F (2021-05-01)
      We show how to map Grothendieck’s dessins d’enfants to algebraic curves as Seiberg-Witten curves, then use the mirror map and the AGT map to obtain the corresponding 4d N = 2 supersymmetric instanton partition functions ...

    • Hilbert series, machine learning, and applications to physics 

      Bao, J; He, YH; Hirst, E; Hofscheier, J; Kasprzyk, A; Majumder, S (2022-04-10)
      We describe how simple machine learning methods successfully predict geometric properties from Hilbert series (HS). Regressors predict embedding weights in projective space to ∼1 mean absolute error, whilst classifiers ...

    • Machine learning Sasakian and $G_2$ topology on contact Calabi-Yau $7$-manifolds 

      Aggarwal, D; He, Y-H; Heyes, E; Hirst, E; Earp, HNS; Silva, TSR (2023-10-04)

    • Machine learning Sasakian and G<inf>2</inf> topology on contact Calabi-Yau 7-manifolds 

      Aggarwal, D; He, YH; Heyes, E; Hirst, E; Sá Earp, HN; Silva, TSR (2024-03-01)
      We propose a machine learning approach to study topological quantities related to the Sasakian and G2-geometries of contact Calabi-Yau 7-manifolds. Specifically, we compute datasets for certain Sasakian Hodge numbers and ...

    • Machine-learning dessins d’enfants: Explorations via modular and Seiberg–Witten curves 

      He, YH; Hirst, E; Peterken, T (2021-02-19)
      We apply machine-learning to the study of dessins d’enfants. Specifically, we investigate a class of dessins which reside at the intersection of the investigations of modular subgroups, Seiberg–Witten (SW) curves and ...

    • Neurons on amoebae 

      Bao, J; He, YH; Hirst, E (2023-05-01)
      We apply methods of machine-learning, such as neural networks, manifold learning and image processing, in order to study 2-dimensional amoebae in algebraic geometry and string theory. With the help of embedding manifold ...

    • New Calabi-Yau Manifolds from Genetic Algorithms 

      Berglund, P; He, Y-H; Heyes, E; Hirst, E; Jejjala, V; Lukas, A (2023-06-09)

    • New Calabi–Yau manifolds from genetic algorithms 

      Berglund, P; He, YH; Heyes, E; Hirst, E; Jejjala, V; Lukas, A (2024-03-01)
      Calabi–Yau manifolds can be obtained as hypersurfaces in toric varieties built from reflexive polytopes. We generate reflexive polytopes in various dimensions using a genetic algorithm. As a proof of principle, we demonstrate ...

    • Polytopes and machine learning 

      Bao, J; He, Y-H; Hirst, E; Hofscheier, J; Kasprzyk, A; Majumder, S (World Scientific Publishing, 2023-12)

    • Quiver mutations, Seiberg duality, and machine learning 

      Bao, J; Franco, S; He, YH; Hirst, E; Musiker, G; Xiao, Y (2020-10-15)
      We initiate the study of applications of machine learning to Seiberg duality, focusing on the case of quiver gauge theories, a problem also of interest in mathematics in the context of cluster algebras. Within the general ...

    • Some Open Questions in Quiver Gauge Theory 

      Bao, J; Hanany, A; He, YH; Hirst, E (2022-04-01)
      Quivers, gauge theories and singular geometries are of great interest in both mathematics and physics. In this note, we collect a few open questions which have arisen in various recent works at the intersection between ...