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58. Hawking, S.: The development of irregularities in a single bubble inflationary universe. Phys.
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59. He, Y.H.: Deep-Learning the Landscape (2017)
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61. Hikage, C., Komatsu, E., Matsubara, T.: Primordial Non-Gaussianity and Analytical Formula
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A. Cole and G. Shiu
38. Dine, M., O’Neil, D., Sun, Z.: Branches of the landscape. JHEP 07, 014 (2005). https://doi.
org/10.1088/1126-6708/2005/07/014
39. Dine, M., Gorbatov, E., Thomas, S.D.: Low energy supersymmetry from the landscape. JHEP
08, 098 (2008). https://doi.org/10.1088/1126-6708/2008/08/098
40. Douglas, M.R.: The Statistics of string / M theory vacua. JHEP 05, 046 (2003). https://doi.
org/10.1088/1126-6708/2003/05/046
41. Douglas, M.R.: Statistical analysis of the supersymmetry breaking scale (2004)
42. Douglas, M.R., Shiffman, B., Zelditch, S.: Critical points and supersymmetric vacua. Commun. Math. Phys. 252, 325–358 (2004). https://doi.org/10.1007/s00220-004-1228-y
43. Douglas, M.R., Taylor, W.: The Landscape of intersecting brane models. JHEP 01, 031 (2007).
https://doi.org/10.1088/1126-6708/2007/01/031
44. Edelsbrunner, H., Harer, J.: Computational topology: an introduction. Am. Math. Soc. (2010)
45. Elbers, W., van de Weygaert, R.: Persistent topology of the reionization bubble network –
i. formalism and phenomenology. Mon. Not. Roy. Astron. Soc. 486(2), 1523–1538 (2019).
https://doi.org/10.1093/mnras/stz908
46. Elsner, F., Wandelt, B.D.: Improved simulation of non-gaussian temperature and polarization
cosmic microwave background maps. Astrophys. J. 184(2), 264 (2009), http://stacks.iop.org/
0067-0049/184/i=2/a=264
47. Feldbrugge, J., van Engelen, M., van de Weygaert, R., Pranav, P., Vegter, G.: Stochastic
homology of gaussian vs. non-gaussian random fields: graphs towards betti numbers and
persistence diagrams. JCAP 1909(09), 052 (2019). https://doi.org/10.1088/1475-7516/2019/
09/052
48. Gay, C., Pichon, C., Pogosyan, D.: Non-Gaussian statistics of critical sets in 2 and 3D: Peaks,
voids, saddles, genus and skeleton. Phys. Rev. D 85, 023011 (2012). https://doi.org/10.1103/
PhysRevD.85.023011
49. Gay, C., Pichon, C., Pogosyan, D.: Non-gaussian statistics of critical sets in 2d and 3d: Peaks,
voids, saddles, genus, and skeleton. Phys. Rev. D 85(2), 023011 (2012)
50. Gmeiner, F., Blumenhagen, R., Honecker, G., Lust, D., Weigand, T.: One in a billion: MSSMlike D-brane statistics. JHEP 01, 004 (2006). https://doi.org/10.1088/1126-6708/2006/01/
004
51. Gukov, S., Vafa, C., Witten, E.: CFT’s from Calabi-Yau four folds. Nucl. Phys. B 584,
69–108 (2000). https://doi.org/10.1016/S0550-3213(01)00289-9, https://doi.org/10.1016/
S0550-3213(00)00373-4, [Erratum: Nucl. Phys. B608,477(2001)]
52. Guth, A.H.: Inflationary universe: A possible solution to the horizon and flatness problems.
Phys. Rev. D 23, 347–356 (Jan 1981). 10.1103/PhysRevD.23.347, https://link.aps.org/doi/
10.1103/PhysRevD.23.347
53. Guth, A.H., Pi, S.Y.: Fluctuations in the new inflationary universe. Phys. Rev. Lett. 49(15),
1110 (1982)
54. Halverson, J., Long, C.: Statistical Predictions in String Theory and Deep Generative Models
(2020)
55. Halverson, J., Long, C., Sung, B.: Algorithmic universality in F-theory compactifications.
Phys. Rev. D 96(12), 126006 (2017). https://doi.org/10.1103/PhysRevD.96.126006
56. Halverson, J., Nelson, B., Ruehle, F.: Branes with Brains: Exploring String Vacua with Deep
Reinforcement Learning. JHEP 06, 003 (2019). https://doi.org/10.1007/JHEP06(2019)003
57. Hatcher, A.: Algebraic Topology. Cambridge University Press (2002)
58. Hawking, S.: The development of irregularities in a single bubble inflationary universe. Phys.
Lett. B 115(4), 295–297 (1982)
59. He, Y.H.: Deep-Learning the Landscape (2017)
60. He, Y.H.: The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning (2018)
61. Hikage, C., Komatsu, E., Matsubara, T.: Primordial Non-Gaussianity and Analytical Formula
for Minkowski Functionals of the Cosmic Microwave Background and Large-scale Structure.
Astrophys. J. 653, 11–26 (2006). https://doi.org/10.1086/508653
62. Hofer, C., Kwitt, R., Dixit, M., Niethammer, M.: Connectivity-optimized representation learning via persistent homology. arXiv preprint arXiv:1906.09003 (2019)