A. Bzdak, S. Esumi, V. Koch, J. Liao, M. Stephanov et al., Mapping the Phases of Quantum Chromodynamics with Beam Energy Scan, Phys. Rept, vol.853, pp.1-87, 2020.

P. De-forcrand, Simulating QCD at finite density, PoS, vol.2009, p.10, 2009.

M. , High-Temperature QCD: theory overview, Nucl. Phys. A, vol.982, pp.99-105, 2019.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

P. Mehta, M. Bukov, C. Wang, A. Day, C. Richardson et al., A high-bias, low-variance introduction to Machine Learning for physicists, Phys. Rep, vol.810, p.1, 2019.

G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld et al., Machine Learning and the Physical Sciences, Rev. Mod. Phys, vol.91, p.45002, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02101667

S. J. Wetzel and M. Scherzer, Machine Learning of Explicit Order Parameters: From the Ising Model to

. Lattice-gauge-theory, Phys. Rev. B, vol.96, p.184410, 2017.

K. Zhou, G. Endrodi, L. Pang, and H. Stöcker, Regressive and generative neural networks for scalar field theory, Phys. Rev. D, vol.100, issue.1, p.11501, 2019.

A. Tanaka and A. Tomiya, Detection of phase transition via convolutional neural network, J. Phys. Soc. Jap, vol.86, issue.6, p.63001, 2017.

B. Yoon, T. Bhattacharya, and R. Gupta, Machine Learning Estimators for Lattice QCD Observables, Phys. Rev. D, vol.100, issue.1, p.14504, 2019.

D. Bachtis, G. Aarts, and B. Lucini, Extending Machine Learning Classification Capabilities with Histogram Reweighting, Phys. Rev. E, vol.102, p.33303, 2020.

T. Matsumoto, M. Kitazawa, and Y. Kohno, Classifying Topological Charge in SU(3) Yang-Mills Theory with Machine Learning

M. N. Chernodub, H. Erbin, V. A. Goy, and A. V. Molochkov, Topological defects and confinement with machine learning: the case of monopoles in compact electrodynamics, Phys. Rev. D, vol.102, p.54501, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02874540

. Critical, [. Mc-|re, and . |im,

. Ml-|l|,

, The same results as in Fig. 5, but for the temporal extension Nt = 4 of SU(3) gauge theory

H. M. Yau and N. Su, On the generalizability of artificial neural networks in spin models

P. E. Shanahan, D. Trewartha, and W. Detmold, Machine learning action parameters in lattice quantum chromodynamics, Phys. Rev. D, vol.97, issue.9, p.94506, 2018.

D. Levy, M. D. Hoffman, and J. Sohl-dickstein, Generalizing Hamiltonian Monte Carlo with Neural Networks

J. M. Pawlowski and J. M. Urban, Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks

M. S. Albergo, G. Kanwar, and P. E. Shanahan, Flowbased generative models for Markov chain Monte Carlo in lattice field theory, Phys. Rev. D, vol.100, issue.3, p.34515, 2019.

D. Boyda, Sampling using SU (N ) gauge equivariant flows

. Gurtej-kanwar, Equivariant flow-based sampling for lattice gauge theory, Phys. Rev. Lett, vol.125, p.121601, 2020.

E. P. Van-nieuwenburg, Y. Liu, and S. D. Huber, Learning phase transitions by confusion, Nature Physics, vol.13, p.435, 2017.

K. Albertsson, Machine Learning in High Energy Physics Community White Paper, J. Phys. Conf. Ser, vol.1085, issue.2, p.22008, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01846718

S. Blücher, L. Kades, J. M. Pawlowski, N. Strodthoff, and J. M. Urban, Towards novel insights in lattice field theory with explainable machine learning, Phys. Rev. D, vol.101, issue.9, p.94507, 2020.

J. Fingberg, U. M. Heller, and F. Karsch, Scaling and asymptotic scaling in the SU(2) gauge theory, Nucl. Phys. B, vol.392, pp.493-517, 1993.

G. Boyd, J. Engels, F. Karsch, E. Laermann, C. Legeland et al., Thermodynamics of SU(3) lattice gauge theory, Nucl. Phys. B, vol.469, pp.419-444, 1996.

K. Binder, Finite size scaling analysis of Ising model block distribution functions, Z. Phys. B, vol.43, p.119, 1981.

P. Broecker, J. Carrasquilla, R. G. Melko, and S. Trebst, Machine learning quantum phases of matter beyond the fermion sign problem, Sci. Rep, vol.7, p.8823, 2017.