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87. Pranav, P., van de Weygaert, R., Vegter, G., Jones, B.J.T., Adler, R.J., Feldbrugge, J., Park, C.,
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88. Ruehle, F.: Evolving neural networks with genetic algorithms to study the String Landscape.
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89. Ruehle, F.: Data science applications to string theory. Phys. Rep. (2019)
90. Schmalzing, J., Gorski, K.M.: Minkowski functionals used in the morphological analysis of
cosmic microwave background anisotropy maps. Mod. Not. R Astron. Soc. 297, 355–365
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91. Schmalzing, J., Buchert, T.: Beyond genus statistics: a unifying approach to the morphology
of cosmic structure. Astrophys. J. Lett. 482(1), L1 (1997), http://stacks.iop.org/1538-4357/
482/i=1/a=L1
92. Schneider, A., Teyssier, R., Potter, D., Stadel, J., Onions, J., Reed, D.S., Smith, R.E., Springel,
V., Pearce, F.R., Scoccimarro, R.: Matter power spectrum and the challenge of percent accuracy. JCAP 1604(04), 047 (2016). https://doi.org/10.1088/1475-7516/2016/04/047
93. Sousbie, T.: DisPerSE: robust structure identification in 2D and 3D (2013)
94. Starobinsky, A.A.: A new type of isotropic cosmological models without singularity. Phys.
Lett. 91B, 99–102 (1980). https://doi.org/10.1016/0370-2693(80)90670-X
95. Starobinsky, A.A.: Dynamics of phase transition in the new inflationary universe scenario and
generation of perturbations. Phys. Lett. B 117(3–4), 175–178 (1982)
96. Susskind, L.: Supersymmetry breaking in the anthropic landscape 1745–1749, (2004)
97. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.:
Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
98. Taylor, W., Wang, Y.N.: The F-theory geometry with most flux vacua. JHEP 12, 164 (2015).
https://doi.org/10.1007/JHEP12(2015)164
99. Vafa, C.: The String landscape and the swampland (2005)
100. Wang, Y.N., Zhang, Z.: Learning non-Higgsable gauge groups in 4D F-theory (2018)
101. van de Weygaert, R., Pranav, P., Jones, B.J., Bos, E., Vegter, G., Edelsbrunner, H., Teillaud,
M., Hellwing, W.A., Park, C., Hidding, J., et al.: Probing dark energy with alpha shapes and
betti numbers. arXiv preprint arXiv:1110.5528 (2011)
102. Winitzki, S., Kosowsky, A.: Minkowski functional description of microwave background gaussianity. New Astron. 3(2), 75–99 (1998). https://doi.org/10.1016/S13841m076(97)00046-8, http://www.sciencedirect.com/science/article/pii/S1384107697000468
103. Zomorodian, A.J.: Topology for computing, vol. 16. Cambridge University Press (2005)
A. Cole and G. Shiu
87. Pranav, P., van de Weygaert, R., Vegter, G., Jones, B.J.T., Adler, R.J., Feldbrugge, J., Park, C.,
Buchert, T., Kerber, M.: Topology and geometry of gaussian random fields i: on betti numbers,
euler characteristic and minkowski functionals. Mon. Not. Roy. Astron. Soc. 485(3), 4167–
4208 (2019). https://doi.org/10.1093/mnras/stz541
88. Ruehle, F.: Evolving neural networks with genetic algorithms to study the String Landscape.
JHEP 08, 038 (2017). https://doi.org/10.1007/JHEP08(2017)038
89. Ruehle, F.: Data science applications to string theory. Phys. Rep. (2019)
90. Schmalzing, J., Gorski, K.M.: Minkowski functionals used in the morphological analysis of
cosmic microwave background anisotropy maps. Mod. Not. R Astron. Soc. 297, 355–365
(1998). https://doi.org/10.1046/j.1365-8711.1998.01467.x
91. Schmalzing, J., Buchert, T.: Beyond genus statistics: a unifying approach to the morphology
of cosmic structure. Astrophys. J. Lett. 482(1), L1 (1997), http://stacks.iop.org/1538-4357/
482/i=1/a=L1
92. Schneider, A., Teyssier, R., Potter, D., Stadel, J., Onions, J., Reed, D.S., Smith, R.E., Springel,
V., Pearce, F.R., Scoccimarro, R.: Matter power spectrum and the challenge of percent accuracy. JCAP 1604(04), 047 (2016). https://doi.org/10.1088/1475-7516/2016/04/047
93. Sousbie, T.: DisPerSE: robust structure identification in 2D and 3D (2013)
94. Starobinsky, A.A.: A new type of isotropic cosmological models without singularity. Phys.
Lett. 91B, 99–102 (1980). https://doi.org/10.1016/0370-2693(80)90670-X
95. Starobinsky, A.A.: Dynamics of phase transition in the new inflationary universe scenario and
generation of perturbations. Phys. Lett. B 117(3–4), 175–178 (1982)
96. Susskind, L.: Supersymmetry breaking in the anthropic landscape 1745–1749, (2004)
97. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.:
Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
98. Taylor, W., Wang, Y.N.: The F-theory geometry with most flux vacua. JHEP 12, 164 (2015).
https://doi.org/10.1007/JHEP12(2015)164
99. Vafa, C.: The String landscape and the swampland (2005)
100. Wang, Y.N., Zhang, Z.: Learning non-Higgsable gauge groups in 4D F-theory (2018)
101. van de Weygaert, R., Pranav, P., Jones, B.J., Bos, E., Vegter, G., Edelsbrunner, H., Teillaud,
M., Hellwing, W.A., Park, C., Hidding, J., et al.: Probing dark energy with alpha shapes and
betti numbers. arXiv preprint arXiv:1110.5528 (2011)
102. Winitzki, S., Kosowsky, A.: Minkowski functional description of microwave background gaussianity. New Astron. 3(2), 75–99 (1998). https://doi.org/10.1016/S13841m076(97)00046-8, http://www.sciencedirect.com/science/article/pii/S1384107697000468
103. Zomorodian, A.J.: Topology for computing, vol. 16. Cambridge University Press (2005)