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We describe how simple machine learning methods successfully predict geometric properties from Hilbert series (HS).
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2018
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K. Bull, Y.-H. He, V. Jejjala, and C. Mishra, “Machine learning CICY threefolds,” Phys. Lett. B
2018
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Anisha, S. Das Bakshi, J. Chakrabortty, and S. Prakash, “Hilbert series and plethystics: paving the path towards 2HDM- and MLRSM-EFT,” J. High Energy Phys
2019
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2013
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S. Cremonesi, A. Hanany, and A. Zaffaroni, “Monopole operators and Hilbert series of Coulomb branches of 3d 𝒩 \mathcal{N} = 4 gauge theories,” Journal of High Energy Physics
2014
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S. Cremonesi, A. Hanany, N. Mekareeya, and A. Zaffaroni, “Coulomb branch Hilbert series and Hall–Littlewood polynomials,” Journal of High Energy Physics
2014
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Y.-H. He, V. Jejjala, C. Matti, and B. D. Nelson, “Veronese geometry and the electroweak vacuum moduli space,” Phys. Lett. B
2014
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2019
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J. Halverson, B. Nelson, and F. Ruehle, “Branes with brains: exploring string vacua with deep reinforcement learning,” J. High Energy Phys
2019
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Y. Xiao, Y.-H. He, and C. Matti, “Standard model plethystics,” Phys. Rev. D
2019
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2020
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2020
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2022
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