H. B. Casimir, On the attraction between two perfectly conducting plates, Proc. Kon. Ned. Akad. Wetensch, vol.51, p.793, 1948.

H. B. Casimir and D. Polder, The Influence of Retardation on the London-van der Waals Forces, Physical Review, vol.73, issue.4, pp.360-372, 1948.

M. Bordag, G. L. Klimchitskaya, U. Mohideen, and V. M. Mostepanenko, Introduction, Advances in the Casimir Effect, pp.1-14, 2009.

K. A. Milton, The Casimir Effect, 2001.

A. W. Rodriguez, F. Capasso, and S. G. Johnson, The Casimir effect in microstructured geometries, Nature Photonics, vol.5, issue.4, pp.211-221, 2011.

B. V. Derjaguin, I. I. Abrikosova, and E. M. Lifshitz, Direct measurement of molecular attraction between solids separated by a narrow gap, Quarterly Reviews, Chemical Society, vol.10, issue.3, p.295, 1956.

J. B?ocki, J. Randrup, W. J. ?wia?tecki, and C. F. Tsang, Proximity forces, Annals of Physics, vol.105, issue.2, pp.427-462, 1977.

S. G. Johnson, Numerical Methods for Computing Casimir Interactions, Casimir Physics, vol.834, pp.175-218, 2011.

T. Emig, N. Graham, R. L. Jaffe, and M. Kardar, Casimir Forces between Arbitrary Compact Objects, Physical Review Letters, vol.99, issue.17, p.170403, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00186489

A. Lambrecht, P. A. Neto, and S. Reynaud, The Casimir effect within scattering theory, New Journal of Physics, vol.8, issue.10, pp.243-243, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00112885

S. Pasquali and A. C. Maggs, Fluctuation-induced interactions between dielectrics in general geometries, The Journal of Chemical Physics, vol.129, issue.1, p.014703, 2008.

M. T. Homer-reid, A. W. Rodriguez, J. White, and S. G. Johnson, Efficient Computation of Casimir Interactions between Arbitrary 3D Objects, Phys. Rev. Lett, vol.103, p.40401, 2009.

H. Gies, K. Langfeld, and L. Moyaerts, Casimir effect on the worldline, Journal of High Energy Physics, vol.2003, issue.06, pp.018-018, 2003.

O. Pavlovsky and M. Ulybyshev, CASIMIR ENERGY CALCULATIONS WITHIN THE FORMALISM OF NONCOMPACT LATTICE QED, International Journal of Modern Physics A, vol.25, issue.12, pp.2457-2473, 2010.

O. Pavlovsky and M. Ulybyshev, MONTE CARLO CALCULATION OF THE LATERAL CASIMIR FORCES BETWEEN RECTANGULAR GRATINGS WITHIN THE FORMALISM OF LATTICE QUANTUM FIELD THEORY, International Journal of Modern Physics A, vol.26, issue.16, pp.2743-2756, 2011.

M. N. Chernodub, V. A. Goy, and A. V. Molochkov, Casimir effect on the lattice: U(1) gauge theory in two spatial dimensions, Physical Review D, vol.94, issue.9, p.94504, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01362563

M. N. Chernodub, V. A. Goy, and A. V. Molochkov, Nonperturbative Casimir effect and monopoles: Compact Abelian gauge theory in two spatial dimensions, Physical Review D, vol.95, issue.7, p.74511, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01487703

S. K. Lamoreaux, Erratum: Demonstration of the Casimir Force in the 0.6 to 6?mRange [Phys. Rev. Lett. 78, 5 (1997)], Physical Review Letters, vol.81, issue.24, pp.5475-5476, 1998.

U. Mohideen and A. Roy, Precision Measurement of the Casimir Force from 0.1 to0.9?m, Physical Review Letters, vol.81, issue.21, pp.4549-4552, 1998.

T. Ederth, Template-stripped gold surfaces with 0.4-nm rms roughness suitable for force measurements: Application to the Casimir force in the 20?100-nm range, Physical Review A, vol.62, issue.6, p.62104, 2000.

G. Bressi, G. Carugno, R. Onofrio, and G. Ruoso, Measurement of the Casimir Force between Parallel Metallic Surfaces, Physical Review Letters, vol.88, issue.4, p.41804, 2002.

H. B. Chan, Y. Bao, J. Zou, R. A. Cirelli, F. Klemens et al., Erratum: Measurement of the Casimir Force between a Gold Sphere and a Silicon Surface with Nanoscale Trench Arrays [Phys. Rev. Lett.101, 030401 (2008)], Physical Review Letters, vol.107, issue.1, p.30401, 2011.

P. Mehta, M. Bukov, C. Wang, A. G. Day, C. Richardson et al., A high-bias, low-variance introduction to Machine Learning for physicists, Physics Reports, vol.810, pp.1-124, 2019.

G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld et al., Machine learning and the physical sciences, Reviews of Modern Physics, vol.91, issue.4, p.45002, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02101667

B. Joó, C. Jung, N. H. Christ, W. Detmold, R. G. Edwards et al., Status and future perspectives for lattice gauge theory calculations to the exascale and beyond, The European Physical Journal A, vol.55, issue.11, p.199, 2019.

A. Nagy and V. Savona, Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems, Physical Review Letters, vol.122, issue.25, p.250501, 2019.

M. J. Hartmann and G. Carleo, Neural-Network Approach to Dissipative Quantum Many-Body Dynamics, Physical Review Letters, vol.122, issue.25, p.250502, 2019.

N. Yoshioka and R. Hamazaki, Constructing neural stationary states for open quantum many-body systems, Physical Review B, vol.99, issue.21, p.214306, 2019.

P. Zhang, H. Shen, and H. Zhai, Machine Learning Topological Invariants with Neural Networks, Physical Review Letters, vol.120, issue.6, p.66401, 2018.

K. Zhou, G. Endr?di, L. Pang, and H. Stöcker, Regressive and generative neural networks for scalar field theory, Physical Review D, vol.100, issue.1, p.11501, 2019.

T. Matsumoto, M. Kitazawa, and Y. Kohno, Classifying Topological Charge in SU(3) Yang-Mills Theory with Machine Learning, Progress of Theoretical and Experimental Physics, 2020.

K. Ch?ng, J. Carrasquilla, R. G. Melko, and E. Khatami, Machine Learning Phases of Strongly Correlated Fermions, Physical Review X, vol.7, issue.3, p.31038, 2017.

J. Carrasquilla and R. G. Melko, Machine learning phases of matter, Nature Physics, vol.13, issue.5, pp.431-434, 2017.

E. P. Van-nieuwenburg, Y. Liu, and S. D. Huber, Learning phase transitions by confusion, Nature Physics, vol.13, issue.5, pp.435-439, 2017.

J. Venderley, V. Khemani, and E. Kim, Machine Learning Out-of-Equilibrium Phases of Matter, Physical Review Letters, vol.120, issue.25, p.257204, 2018.

Y. Zhang and E. Kim, Quantum Loop Topography for Machine Learning, Physical Review Letters, vol.118, issue.21, p.21, 2017.

Y. Liu and E. P. Van-nieuwenburg, Discriminative Cooperative Networks for Detecting Phase Transitions, Physical Review Letters, vol.120, issue.17, p.17, 2018.

R. A. Vargas-hernández, J. Sous, M. Berciu, and R. V. Krems, Extrapolating Quantum Observables with Machine Learning: Inferring Multiple Phase Transitions from Properties of a Single Phase, Physical Review Letters, vol.121, issue.25, p.25, 2018.

B. S. Rem, N. Käming, M. Tarnowski, L. Asteria, N. Fläschner et al., Identifying quantum phase transitions using artificial neural networks on experimental data, Nature Physics, vol.15, issue.9, pp.917-920, 2019.

P. Broecker, J. Carrasquilla, R. G. Melko, and S. Trebst, Machine learning quantum phases of matter beyond the fermion sign problem, Scientific Reports, vol.7, issue.1, p.1, 2017.

Y. Abe, K. Fukushima, Y. Hidaka, H. Matsueda, K. Murase et al., Image-processing the topological charge density in the $\mathbb{C}P^{N-1}$ model, Progress of Theoretical and Experimental Physics, vol.2020, issue.1, 2020.

S. J. Wetzel, Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders, Physical Review E, vol.96, issue.2, 2017.

S. J. Wetzel and M. Scherzer, Machine learning of explicit order parameters: From the Ising model to SU(2) lattice gauge theory, Physical Review B, vol.96, issue.18, p.18, 2017.

M. N. Chernodub, V. A. Goy, and A. V. Molochkov, Casimir effect and deconfinement phase transition, Physical Review D, vol.96, issue.9, p.94507, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01584866

M. N. Chernodub, V. A. Goy, A. V. Molochkov, and H. H. Nguyen, Casimir Effect in Yang-Mills Theory in D=2+1, Physical Review Letters, vol.121, issue.19, p.191601, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01815174

M. Bordag, D. Robaschik, and E. Wieczorek, Quantum field theoretic treatment of the casimir effect, Annals of Physics, vol.165, issue.1, pp.192-213, 1985.

J. Ambjørn and S. Wolfram, Properties of the vacuum. 2. Electrodynamic, Annals of Physics, vol.147, issue.1, pp.33-56, 1983.

N. Graham, R. L. Jaffe, V. Khemani, M. Quandt, M. Scandurra et al., Calculating vacuum energies in renormalizable quantum field theories:, Nuclear Physics B, vol.645, issue.1-2, pp.49-84, 2002.

N. Graham, R. L. Jaffe, V. Khemani, M. Quandt, M. Scandurra et al., Casimir energies in light of quantum field theory, Physics Letters B, vol.572, issue.3-4, pp.196-201, 2003.

N. Graham, R. L. Jaffe, V. Khemani, M. Quandt, O. Schröder et al., The Dirichlet Casimir problem, Nuclear Physics B, vol.677, issue.1-2, pp.379-404, 2004.

C. Gattringer and C. B. Lang, More about lattice fermions, Quantum Chromodynamics on the Lattice, pp.243-266, 2009.

I. P. Omelyan, I. M. Mryglod, and R. Folk, Optimized Verlet-like algorithms for molecular dynamics simulations, Physical Review E, vol.65, issue.5, p.56706, 2002.

, Signing Off, Physics Education, vol.38, issue.3, pp.272-272, 2003.

J. C. Sexton, D. H. Weingarten, ;. Urbach, K. Jansen, A. Shindler et al., Comput. Phys. Commun, vol.380, p.87, 1992.

C. Peterson, T. H. Hansson, and K. Johnson, Loop diagrams in boxes, Physical Review D, vol.26, issue.2, pp.415-428, 1982.

F. Chollet and . Keras, Keras

F. Chollet, Deep Learning with Python, 2017.