Machine learning line bundle cohomologies of hypersurfaces in toric varieties

DOI10.1016/j.physletb.2019.01.002zbMath1406.14001arXiv1809.02547OpenAlexW2891834385MaRDI QIDQ1716730

Daniel Klaewer, Lorenz Schlechter

Publication date: 5 February 2019

Published in: Physics Letters. B (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1809.02547

zbMATH Keywords

toric varietiesneural networkcohomologymachine learningline bundles

Mathematics Subject Classification ID

Vector bundles on surfaces and higher-dimensional varieties, and their moduli (14J60) Software, source code, etc. for problems pertaining to algebraic geometry (14-04)

Related Items (19)

Flops, Gromov-Witten invariants and symmetries of line bundle cohomology on Calabi-Yau three-folds ⋮ Data science applications to string theory ⋮ Neural network approximations for Calabi-Yau metrics ⋮ Line Bundle Cohomologies on CICYs with Picard Number Two ⋮ Formulae for Line Bundle Cohomology on Calabi‐Yau Threefolds ⋮ Topological data analysis for the string landscape ⋮ Estimating Calabi-Yau hypersurface and triangulation counts with equation learners ⋮ Deep learning in the heterotic orbifold landscape ⋮ Contrast data mining for the MSSM from strings ⋮ Machine learning Calabi-Yau four-folds ⋮ Towards the “Shape” of Cosmological Observables and the String Theory Landscape with Topological Data Analysis ⋮ Getting CICY high ⋮ Searching the landscape of flux vacua with genetic algorithms ⋮ Heterotic line bundle models on generalized complete intersection Calabi Yau manifolds ⋮ Topological formulae for the zeroth cohomology of line bundles on del Pezzo and Hirzebruch surfaces ⋮ Algorithmically solving the tadpole problem ⋮ Branes with brains: exploring string vacua with deep reinforcement learning ⋮ Geodesics in the extended Kähler cone of Calabi-Yau threefolds ⋮ Accessibility measure for eternal inflation: dynamical criticality and higgs metastability

Uses Software

• cohomCalg
• GitHub

Cites Work

• GANs for generating EFT models
• Evolving neural networks with genetic algorithms to study the string landscape
• Machine learning in the string landscape
• Phases of \(N=2\) theories in two dimensions
• Cohomology of line bundles: Proof of the algorithm
• Cohomology of line bundles: A computational algorithm
• Formulae for Line Bundle Cohomology on Calabi‐Yau Threefolds

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