Built on

K. Bull, Y.-H. He, V. Jejjala, and C. Mishra, “Getting CICY High,” Phys. Lett. B
Original
1903
Earlier work this paper cites.
Y.-H. He and M. Kim, “Learning Algebraic Structures: Preliminary Investigations,” arXiv:1905.02263 [cs.LG]
Original
1905
Earlier work this paper cites.
I. M. Comsa, M. Firsching, and T. Fischbacher, “SO(8) Supergravity and the Magic of Machine Learning,” JHEP
Original
1906
Earlier work this paper cites.
A. Cole, A. Schachner, and G. Shiu, “Searching the Landscape of Flux Vacua with Genetic Algorithms,” JHEP
Original
1907
Earlier work this paper cites.
N. Bobev, T. Fischbacher, and K. Pilch, “Properties of the new 𝒩 \mathcal{N} = 1 AdS 4
Original
1909
Earlier work this paper cites.
L. Alessandretti, A. Baronchelli, and Y.-H. He, “Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer,” arXiv:1911.02008 [math.NT]
Original
1911
Earlier work this paper cites.
P. Candelas, G. T. Horowitz, A. Strominger, and E. Witten, “Vacuum Configurations for Superstrings,” Nucl. Phys. B
1985
Earlier work this paper cites.
P. Green and T. Hubsch, “Calabi-Yau Manifolds as Complete Intersections in Products of Complex Projective Spaces,” Commun. Math. Phys
1987
Earlier work this paper cites.
P. Candelas, A. M. Dale, C. A. Lutken, and R. Schimmrigk, “Complete Intersection Calabi-Yau Manifolds,” Nucl. Phys. B
1988
Earlier work this paper cites.
C. Vafa, “String Vacua and Orbifoldized L-G Models,” Mod. Phys. Lett. A
1989
Earlier work this paper cites.
P. Candelas, M. Lynker, and R. Schimmrigk, “Calabi-yau manifolds in weighted p4,” Nuclear Physics B
1990
Earlier work this paper cites.
M. Kreuzer and H. Skarke, “No mirror symmetry in landau-ginzburg spectra!” Nuclear Physics B
1992
Earlier work this paper cites.
A. Klemm and R. Schimmrigk, “Landau-ginzburg string vacua,” Nuclear Physics B
1994
Earlier work this paper cites.
P. Candelas, X. d. l. Ossa, and S. Katz, “Mirror symmetry for calabi-yau hypersurfaces in weighted and extensions of landau-ginzburg theory,” Nuclear Physics B
1995
Earlier work this paper cites.
H. Skarke, “Weight systems for toric calabi-yau varieties and reflexivity of newton polyhedra,” Modern Physics Letters A
1996
Earlier work this paper cites.
A. Klemm, B. Lian, S.-S. Roan, and S.-T. Yau, “Calabi-yau four-folds for m- and f-theory compactifications,” Nuclear Physics B
1998
Earlier work this paper cites.
J. Bao, Y.-H. He, E. Hirst, and S. Pietromonaco, “Lectures on the Calabi-Yau Landscape,” arXiv:2001.01212 [hep-th]
Original
2001
Earlier work this paper cites.

Similar

C. Krishnan, V. Mohan, and S. Ray, “Machine Learning 𝒩 = 8 , D = 5 {\cal N}=8,D=5 Gauged Supergravity,” Fortsch. Phys
Original
2002
Cited alongside, same era.
M. Kreuzer and H. Skarke, “Complete classification of reflexive polyhedra in four-dimensions,” Adv. Theor. Math. Phys
2002
Cited alongside, same era.
N. Bobev, T. Fischbacher, F. F. Gautason, and K. Pilch, “A cornucopia of AdS 5
Original
2003
Cited alongside, same era.
S. Krippendorf and M. Syvaeri, “Detecting Symmetries with Neural Networks,” arXiv:2003.13679 [physics.comp-ph]
Original
2003
Cited alongside, same era.
Y.-H. He, E. Hirst, and T. Peterken, “Machine-learning dessins d’enfants: explorations via modular and Seiberg–Witten curves,” J. Phys. A
Original

Then

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature
2015
Later among the works it cites.
M. Cirafici, “Persistent Homology and String Vacua,” JHEP
Original
2016
Later among the works it cites.
Y.-H. He, “Machine-learning the string landscape,” Phys. Lett. B
2017
Later among the works it cites.
J. Carifio, J. Halverson, D. Krioukov, and B. D. Nelson, “Machine Learning in the String Landscape,” JHEP
Original
2017
Later among the works it cites.
D. Krefl and R.-K. Seong, “Machine Learning of Calabi-Yau Volumes,” Phys. Rev. D
Original
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2021

Machine Learning Calabi-Yau Hypersurfaces

Berman, David S., He, Yang-Hui, Hirst, Edward

Understand

We revisit the classic database of weighted-P4s which admit Calabi-Yau 3-fold hypersurfaces equipped with a diverse set of tools from the machine-learning toolbox.

Unsupervised techniques identify an unanticipated almost linear dependence of the topological data on the weights.
This then allows us to identify a previously unnoticed clustering in the Calabi-Yau data.
Supervised techniques are successful in predicting the topological parameters of the hypersurface from its weights with an accuracy of R^2 > 95%.

Built on

K. Bull, Y.-H. He, V. Jejjala, and C. Mishra, “Getting CICY High,” Phys. Lett. B
Original
1903
Earlier work this paper cites.
Y.-H. He and M. Kim, “Learning Algebraic Structures: Preliminary Investigations,” arXiv:1905.02263 [cs.LG]
Original
1905
Earlier work this paper cites.
I. M. Comsa, M. Firsching, and T. Fischbacher, “SO(8) Supergravity and the Magic of Machine Learning,” JHEP
Original
1906
Earlier work this paper cites.
A. Cole, A. Schachner, and G. Shiu, “Searching the Landscape of Flux Vacua with Genetic Algorithms,” JHEP
Original
1907
Earlier work this paper cites.
N. Bobev, T. Fischbacher, and K. Pilch, “Properties of the new 𝒩 \mathcal{N} = 1 AdS 4
Original
1909
Earlier work this paper cites.
L. Alessandretti, A. Baronchelli, and Y.-H. He, “Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer,” arXiv:1911.02008 [math.NT]
Original
1911
Earlier work this paper cites.
P. Candelas, G. T. Horowitz, A. Strominger, and E. Witten, “Vacuum Configurations for Superstrings,” Nucl. Phys. B
1985
Earlier work this paper cites.
P. Green and T. Hubsch, “Calabi-Yau Manifolds as Complete Intersections in Products of Complex Projective Spaces,” Commun. Math. Phys
1987
Earlier work this paper cites.
P. Candelas, A. M. Dale, C. A. Lutken, and R. Schimmrigk, “Complete Intersection Calabi-Yau Manifolds,” Nucl. Phys. B
1988
Earlier work this paper cites.
C. Vafa, “String Vacua and Orbifoldized L-G Models,” Mod. Phys. Lett. A
1989
Earlier work this paper cites.
P. Candelas, M. Lynker, and R. Schimmrigk, “Calabi-yau manifolds in weighted p4,” Nuclear Physics B
1990
Earlier work this paper cites.
M. Kreuzer and H. Skarke, “No mirror symmetry in landau-ginzburg spectra!” Nuclear Physics B
1992
Earlier work this paper cites.
A. Klemm and R. Schimmrigk, “Landau-ginzburg string vacua,” Nuclear Physics B
1994
Earlier work this paper cites.
P. Candelas, X. d. l. Ossa, and S. Katz, “Mirror symmetry for calabi-yau hypersurfaces in weighted and extensions of landau-ginzburg theory,” Nuclear Physics B
1995
Earlier work this paper cites.
H. Skarke, “Weight systems for toric calabi-yau varieties and reflexivity of newton polyhedra,” Modern Physics Letters A
1996
Earlier work this paper cites.
A. Klemm, B. Lian, S.-S. Roan, and S.-T. Yau, “Calabi-yau four-folds for m- and f-theory compactifications,” Nuclear Physics B
1998
Earlier work this paper cites.
J. Bao, Y.-H. He, E. Hirst, and S. Pietromonaco, “Lectures on the Calabi-Yau Landscape,” arXiv:2001.01212 [hep-th]
Original
2001
Earlier work this paper cites.

Similar

C. Krishnan, V. Mohan, and S. Ray, “Machine Learning 𝒩 = 8 , D = 5 {\cal N}=8,D=5 Gauged Supergravity,” Fortsch. Phys
Original
2002
Cited alongside, same era.
M. Kreuzer and H. Skarke, “Complete classification of reflexive polyhedra in four-dimensions,” Adv. Theor. Math. Phys
2002
Cited alongside, same era.
N. Bobev, T. Fischbacher, F. F. Gautason, and K. Pilch, “A cornucopia of AdS 5
Original
2003
Cited alongside, same era.
S. Krippendorf and M. Syvaeri, “Detecting Symmetries with Neural Networks,” arXiv:2003.13679 [physics.comp-ph]
Original
2003
Cited alongside, same era.
Y.-H. He, E. Hirst, and T. Peterken, “Machine-learning dessins d’enfants: explorations via modular and Seiberg–Witten curves,” J. Phys. A
Original

Then

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature
2015
Later among the works it cites.
M. Cirafici, “Persistent Homology and String Vacua,” JHEP
Original
2016
Later among the works it cites.
Y.-H. He, “Machine-learning the string landscape,” Phys. Lett. B
2017
Later among the works it cites.
J. Carifio, J. Halverson, D. Krioukov, and B. D. Nelson, “Machine Learning in the String Landscape,” JHEP
Original
2017
Later among the works it cites.
D. Krefl and R.-K. Seong, “Machine Learning of Calabi-Yau Volumes,” Phys. Rev. D
Original
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2004

Cited alongside, same era.

Y.-H. He and S.-T. Yau, “Graph Laplacians, Riemannian Manifolds and their Machine-Learning,” arXiv:2006.16619 [math.CO]

Original

2006

Cited alongside, same era.

J. Bao, S. Franco, Y.-H. He, E. Hirst, G. Musiker, and Y. Xiao, “Quiver Mutations, Seiberg Duality and Machine Learning,” Phys. Rev. D

Original

2006

Cited alongside, same era.

V. V. Batyrev, “On the stringy Hodge numbers of mirrors of quasi-smooth Calabi-Yau hypersurfaces,” arXiv:2006.15825 [math.AG]

Original

2006

Cited alongside, same era.

H. Erbin and R. Finotello, “Machine learning for complete intersection Calabi-Yau manifolds: a methodological study,” Phys. Rev. D

Original

2007

Cited alongside, same era.

H. Erbin and R. Finotello, “Inception neural network for complete intersection Calabi–Yau 3-folds,” Mach. Learn. Sci. Tech

Original

2007

Cited alongside, same era.

Y.-H. He and A. Lukas, “Machine Learning Calabi-Yau Four-folds,” Phys. Lett. B

Original

2009

Cited alongside, same era.

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, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research

2011

Cited alongside, same era.

F. Ruehle, “Evolving neural networks with genetic algorithms to study the String Landscape,” JHEP

Original

2017