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Name: Ed Hirst
Type: User
Bio: Postdoctoral Researcher at Queen Mary, University of London
Twitter: edghirst
Location: London, United Kingdom
Name: Ed Hirst
Type: User
Bio: Postdoctoral Researcher at Queen Mary, University of London
Twitter: edghirst
Location: London, United Kingdom
Supervised machine learning techniques and general network analysis methods are applied to Cluster Algebras and their exchange graphs (arXiv: 2203.13847).
Generation of Grassmannian cluster variables via Young tableaux and their ML (arXiv: 2212.09771).
Machine learning techniques are applied to generated datasets of Hilbert Series, to learn a variety of properties (arXiv: 2103.13436).
ML Clifford invariants of Coxeter elements (arXiv: 2310.00041)
Application of Siamese Neural Networks to classification of Type IIB (p,q)-brane webs under SL(2,Z) duality and Hanany-Witten transitions (arXiv: 2202.05845).
Generation of Calabi-Yau links from wp4 spaces, computation of their topological properties (Sasakian Hodge numbers, CN invariant), and their ML (arXiv: 2310.03064).
A sagemath notebook for tutorial 2 of the String Theory Compactification course of the Oxford ML conference 2023.
Supervised & unsupervised machine-learning techniques are applied to the database of weighted P4s which admit Calabi-Yau hypersurfaces (arXiv: 2112.06350).
Weight systems of 6 weights, defining weighted P5s and CY4s, are studied with ML. An approximation of Hodge computation is presented and used to generate transverse weight systems for CY5s and CY6s (arXiv: 2311.17146).
Machine learning techniques are applied to datasets of polygons & polyhedra, examining how properties such as volume, dual volume, reflexivity can be learnt from Plucker coordinate representation using neural networks (arXiv: 2109.09602).
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