Publications: Dr Edward Hirst

Berglund P, Butbaia G, He Y-H, Heyes E, Hirst E, Jejjala V ( 2025 ) . Generating triangulations and fibrations with reinforcement learning . Physics Letters B vol. 860 ,

Hirst E ( 2024 ) . Calabi–Yau links and machine learning . International Journal of Data Science in the Mathematical Sciences vol. 02 , ( 01 ) 3 - 14 .

Hirst E, Schettini Gherardini T ( 2024 ) . Calabi-Yau Four/Five/Six-folds as $\mathbb{P}_\textbf{w}^n$ Hypersurfaces: Machine Learning, Approximation, and Generation . Phys. Rev. D vol. 109 , 106006 - 106006 .

Chen S, Dechant P-P, He Y-H, Heyes E, Hirst E, Riabchenko D ( 2024 ) . Machine Learning Clifford Invariants of ADE Coxeter Elements . Advances in Applied Clifford Algebras vol. 34 , ( 3 )

Aggarwal D, He Y-H, Heyes E, Hirst E, Earp HNS, Silva TSR ( 2024 ) . Machine learning Sasakian and G2 topology on contact Calabi-Yau 7-manifolds . Physics Letters B vol. 850 ,

Berglund P, He Y-H, Heyes E, Hirst E, Jejjala V, Lukas A ( 2024 ) . New Calabi–Yau manifolds from genetic algorithms . Physics Letters B vol. 850 ,

Bao J, He Y-H, Hirst E, Hofscheier J, Kasprzyk A, Majumder S ( 2024 ) . Polytopes and machine learning . International Journal of Data Science in the Mathematical Sciences vol. 01 , ( 02 ) 181 - 211 .

Dechant P-P, He Y-H, Heyes E, Hirst E ( 2023 ) . Cluster algebras: Network science and machine learning . Journal of Computational Algebra vol. 8 ,

Bao J, He Y-H, Hirst E ( 2023 ) . Neurons on amoebae . Journal of Symbolic Computation vol. 116 , 1 - 38 .

Cheung M-W, Dechant P-P, He Y-H, Heyes E, Hirst E, Li J-R ( 2023 ) . Clustering cluster algebras with clusters . Advances in Theoretical and Mathematical Physics vol. 27 , ( 3 ) 797 - 828 .

He Y-H, Heyes E, Hirst E ( 2023 ) . Machine learning in physics and geometry . Artificial Intelligence , vol. 49 , Elsevier

Chen S, He Y-H, Hirst E, Nestor A, Zahabi A ( 2023 ) . Mahler measuring the genetic code of amoebae . Advances in Theoretical and Mathematical Physics vol. 27 , ( 5 ) 1405 - 1461 .

Arias-Tamargo G, He Y-H, Heyes E, Hirst E, Rodriguez-Gomez D ( 2022 ) . Brain webs for brane webs . Physics Letters B vol. 833 ,

Bao J, He Y-H, Hirst E, Hofscheier J, Kasprzyk A, Majumder S ( 2022 ) . Hilbert series, machine learning, and applications to physics . Physics Letters B vol. 827 ,

Bao J, Hanany A, He Y-H, Hirst E ( 2022 ) . Some open questions in quiver gauge theory . Proyecciones (Antofagasta) vol. 41 , ( 2 ) 355 - 386 .

Berman DS, He Y-H, Hirst E ( 2022 ) . Machine learning Calabi-Yau hypersurfaces . Physical Review D vol. 105 , ( 6 ) 066002 - 066002 .

Bao J, Foda O, He Y-H, Hirst E, Read J, Xiao Y, Yagi F ( 2021 ) . Dessins d’enfants, Seiberg-Witten curves and conformal blocks . Journal of High Energy Physics vol. 2021 , ( 5 )

He Y-H, Hirst E, Peterken T ( 2021 ) . Machine-learning dessins d’enfants: explorations via modular and Seiberg–Witten curves . Journal of Physics A: Mathematical and Theoretical vol. 54 , ( 7 )

Bao J, Franco S, He Y-H, Hirst E, Musiker G, Xiao Y ( 2020 ) . Quiver mutations, Seiberg duality, and machine learning . Physical Review D vol. 102 , ( 8 )