Fetching the paper…
Reading the bibliography…
Classification of cluster variables in cluster algebras (in particular, Grassmannian cluster algebras) is an important problem, which has direct application to computations of scattering amplitudes in physics.
V. Chari, A. Pressley, et al., A guide to quantum groups, Cambridge University Press, 1995
1995
Earlier work this paper cites.
V. Chari, A. Pressley, Factorization of representations of quantum affine algebras, Modular interfaces,(Riverside CA 1995), AMS/IP Stud. Adv. Math 4 (1997) 33–40
1997
Earlier work this paper cites.
S. Fomin, A. Zelevinsky, Cluster algebras I: foundations, Journal of the American Mathematical Society 15 (2) (2002) 497–529
2002
Earlier work this paper cites.
arXiv:math/0202148
B. Leclerc, Imaginary vectors in the dual canonical basis of U q ( n ) {U}_{q}(n) (2002) · 2002
Earlier work this paper cites.
arXiv:hep-th/0306092
S. Franco, A. Hanany, Y.-H. He, P. Kazakopoulos, Duality walls, duality trees and fractional branes (6 2003) · 2003
Earlier work this paper cites.
Y.-H. He, E. Hirst, T. Peterken, Machine-learning dessins d’enfants: explorations via modular and Seiberg–Witten curves, J. Phys. A 54 (7) (2021) 075401 · 2004
Earlier work this paper cites.
J. S. Scott, Grassmannians and cluster algebras, Proceedings of the London Mathematical Society 92 (2) (2006) 345–380
2006
Earlier work this paper cites.
J. Bao, S. Franco, Y.-H. He, E. Hirst, G. Musiker, Y. Xiao, Quiver Mutations, Seiberg Duality and Machine Learning, Phys. Rev. D 102 (8) (2020) 086013 · 2006
Earlier work this paper cites.
D. Hernandez, B. Leclerc, Cluster algebras and quantum affine algebras, Duke Mathematical Journal 154 (2) (2010) 265–341
2010
Earlier work this paper cites.
K. Baur, D. Bogdanic, A. G. Elsener, J.-R. Li, Rigid indecomposable modules in grassmannian cluster categories (2020) · 2011
Earlier work this paper cites.
G. Musiker, C. Stump, A compendium on the cluster algebra and quiver package in Sage (2011) · 2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830
2011
Earlier work this paper cites.
V. Jejjala, D. K. Mayorga Pena, C. Mishra, Neural Network Approximations for Calabi-Yau Metrics (12 2020) · 2012
Earlier work this paper cites.
doi:https://doi.org/10.1007/JHEP01(2014)091
J. Golden, A. B. Goncharov, M. Spradlin, C. Vergu, A. Volovich, Motivic amplitudes and cluster coordinates, Journal of High Energy Physics 2014 (1) (2014) 91 · 2014
Earlier work this paper cites.
S. Franco, D. Galloni, A. Mariotti, Bipartite Field Theories, Cluster Algebras and the Grassmannian, J. Phys. A 47 (47) (2014) 474004 · 2014
Cited alongside, same era.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems , software available from tensorflow.org (2015). URL https://www.tensorflow.org/
2015
Cited alongside, same era.
B. T. Jensen, A. D. King, X. Su, A categorification of Grassmannian cluster algebras, Proceedings of the London Mathematical Society 113 (2) (2016) 185–212
2016
Cited alongside, same era.
doi:https://doi.org/10.1007/JHEP03(2021)065
N. Arkani-Hamed, T. Lam, M. Spradlin, Non-perturbative geometries for planar N = 4 N=4 SYM amplitudes, J. High Energ. Phys. (03) (2021) 65 · 2021
Later among the works it cites.
N. Henke, G. Papathanasiou, Singularities of eight- and nine-particle amplitudes from cluster algebras and tropical geometry, Journal of High Energy Physics 2021 (7) (2021) 1–60
2021
Later among the works it cites.
P. Berglund, B. Campbell, V. Jejjala, Machine Learning Kreuzer-Skarke Calabi-Yau Threefolds (12 2021) · 2021
Later among the works it cites.
A. Cole, S. Krippendorf, A. Schachner, G. Shiu, Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning, in: 35th Conference on Neural Information Processing Systems, 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Arkani-Hamed, J. Bourjaily, F. Cachazo, A. Goncharov, J. Trnka, A. Postnikov, Grassmannian geometry of scattering amplitudes, Cambridge University Press, 2016
2016
Cited alongside, same era.
doi:https://doi.org/10.1007/JHEP02(2017)137
L. J. Dixon, J. Drummond, T. Harrington, A. J. McLeod, G. Papathanasiou, S. Marcus, Heptagons from the Steinmann cluster bootstrap, J. High Energ. Phys. (02) (2017) 137 · 2017
Cited alongside, same era.
Y.-H. He, Deep-Learning the Landscape (6 2017) · 2017
Cited alongside, same era.
J. Carifio, J. Halverson, D. Krioukov, B. D. Nelson, Machine Learning in the String Landscape, JHEP 09 (2017) 157 · 2017
Cited alongside, same era.
D. Krefl, R.-K. Seong, Machine Learning of Calabi-Yau Volumes, Phys. Rev. D 96 (6) (2017) 066014 · 2017
Cited alongside, same era.
F. Ruehle, Evolving neural networks with genetic algorithms to study the String Landscape, JHEP 2017 (08) (2017) 038 · 2017
Cited alongside, same era.
S. Franco, G. Musiker, Higher Cluster Categories and QFT Dualities, Phys. Rev. D 98 (4) (2018) 046021 · 2018
Cited alongside, same era.
doi:https://doi.org/10.1007/JHEP04(2020)146
J. Drummond, J. Foster, Ö. Gürdoğan, C. Kalousios, Tropical Grassmannians, cluster algebras and scattering amplitudes, Journal of High Energy Physics 2020 (4) (2020) 146 · 2020
Cited alongside, same era.
N. Henke, G. Papathanasiou, How tropical are seven-and eight-particle amplitudes, Journal of High Energy Physics 2020 (8) (2020) 1–50
2020
Cited alongside, same era.
J. Bao, Y.-H. He, E. Hirst, J. Hofscheier, A. Kasprzyk, S. Majumder, Polytopes and Machine Learning (9 2021) · 2021
Later among the works it cites.
G. Arias-Tamargo, Y.-H. He, E. Heyes, E. Hirst, D. Rodriguez-Gomez, Brain webs for brane webs, Phys. Lett. B 833 (2022) 137376 · 2022
Closest in time.
D. S. Berman, Y.-H. He, E. Hirst, Machine learning Calabi-Yau hypersurfaces, Phys. Rev. D 105 (6) (2022) 066002 · 2022
Closest in time.
J. Bao, Y.-H. He, E. Hirst, Neurons on Amoebae, J. Symb. Comput. 116 (2022) 1–38 · 2022
Closest in time.
J. Bao, Y.-H. He, E. Hirst, J. Hofscheier, A. Kasprzyk, S. Majumder, Hilbert series, machine learning, and applications to physics, Phys. Lett. B 827 (2022) 136966 · 2022
Closest in time.
E. Hirst, Machine Learning for Hilbert Series, in: Nankai Symposium on Mathematical Dialogues: In celebration of S.S.Chern’s 110th anniversary, 2022 · 2022
Closest in time.
M. Manko, An Upper Bound on the Critical Volume in a Class of Toric Sasaki-Einstein Manifolds (9 2022) · 2022
Closest in time.
S. Chen, Y.-H. He, E. Hirst, A. Nestor, A. Zahabi, Mahler Measuring the Genetic Code of Amoebae (12 2022) · 2022
Closest in time.
P.-P. Dechant, Y.-H. He, E. Heyes, E. Hirst, Cluster Algebras: Network Science and Machine Learning (3 2022) · 2022
Closest in time.