-
[1]
S.-M. Udrescu, M. Tegmark, Science Advances 6(16), eaay2631 (2020)
-
[2]
S.-M. Udrescu, A. Tan, J. Feng, O. Neto, T. Wu, M. Tegmark, Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity, arXiv:2006.10782 [physics, stat] (Dec. 2020). arXiv:2006.10782.
-
[3]
Z. Liu, M. Tegmark, Phys. Rev. Lett. 126(18), 180604 (2021), arXiv:2011.04698
-
[4]
Z. Liu, M. Tegmark, Physical Review Letters 126(18), 180604 (2021), arXiv:2011.04698
-
[5]
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, L. Zdeborová, Rev. Mod. Phys. 91(4), 045002 (2019), arXiv:1903.10563
-
[6]
P. E. Shanahan, A. Trewartha, W. Detmold, Phys. Rev. D 97(9), 094506 (2018), arXiv:1801.05784
-
[7]
S. Y. Chen, H. T. Ding, F. Y. Liu, G. Papp, C. B. Yang, Machine learning spectral functions in lattice QCD (10 2021). arXiv:2110.13521.
-
[8]
D. L. B. Sombillo, Y. Ikeda, T. Sato, Phys. Rev. D 104(3), 036001 (2021), arXiv:2105.04898
-
[9]
D. Liu, C. Sun, J. Gao, JHEP 08, 088 (2022), arXiv:2201.06586
-
[10]
Z. Zhang, R. Ma, J. Hu, Q. Wang, Discover the GellMann-Okubo formula with machine learning (8 2022). arXiv:2208.03165.
-
[11]
H. Chen, W.-Q. Niu, H.-Q. Zheng, Identify Hadronic Molecule States by Neural Network (5 2022). arXiv: 2205.03572.
-
[12]
J. Liu, Z. Zhang, J. Hu, Q. Wang, Phys. Rev. D 105(7), 076013 (2022), arXiv:2202.04929
-
[13]
M. Raissi, P. Perdikaris, G. Karniadakis, Journal of Computational Physics 378, 686 (2019)
-
[14]
M. Luscher, Commun. Math. Phys. 104, 177 (1986)
-
[15]
M. Luscher, Commun. Math. Phys. 105, 153 (1986)
-
[16]
M. Luscher, Nucl. Phys. B 354, 531 (1991)
-
[17]
M. T. Hansen, S. R. Sharpe, Phys. Rev. D 90(11), 116003 (2014), arXiv:1408.5933
-
[18]
A. W. Jackura, S. M. Dawid, C. Fernández-Ramírez, V. Mathieu, M. Mikhasenko, A. Pilloni, S. R. Sharpe, A. P. Szczepaniak, Phys. Rev. D 100(3), 034508 (2019), arXiv:1905.12007
-
[19]
M. T. Hansen, S. R. Sharpe, Ann. Rev. Nucl. Part. Sci. 69, 65 (2019), arXiv:1901.00483
-
[20]
T. D. Blanton, S. R. Sharpe, Phys. Rev. D 103(5), 054503 (2021), arXiv:2011.05520
-
[21]
H.-W. Hammer, J.-Y. Pang, JHEP 09, 109 (2017), arXiv:1706.07700
-
[22]
F. Müller, A. Rusetsky, JHEP 03, 152 (2021), arXiv:2012.13957
-
[23]
F. Müller, J.-Y. Pang, A. Rusetsky, J.-J. Wu, JHEP 02, 158 (2022), arXiv:2110.09351
-
[24]
M. Mai, M. Döring, Eur. Phys. J. A 53(12), 240 (2017), arXiv:1709.08222
-
[25]
M. Mai, M. Doring, Phys. Rev. Lett. 122(6), 062503 (2019), arXiv:1807.04746
-
[26]
R. Brett, C. Culver, M. Mai, A. Alexandru, M. Döring, F. X. Lee, Phys. Rev. D 104(1), 014501 (2021), arXiv:2101.06144
-
[27]
M. Mai, M. Döring, A. Rusetsky, Eur. Phys. J. ST 230(6), 1623 (2021), arXiv:2103.00577
-
[28]
K. Hornik, M. Stinchcombe, H. White, Neural Networks 2(5), 359 (1989)
-
[29]
K. Hornik, Neural Networks 4(2), 251 (1991)
-
[30]
M. Leshno, V. Y. Lin, A. Pinkus, S. Schocken, Neural Networks 6(6), 861 (1993)
-
[31]
J.-J. Wu, T. S. H. Lee, A. W. Thomas, R. D. Young, Phys. Rev. C 90(5), 055206 (2014), arXiv:1402.4868
-
[32]
A. Matsuyama, T. Sato, T. S. H. Lee, Phys. Rept. 439, 193 (2007), arXiv:nucl-th/0608051
-
[33]
H. Kamano, S. X. Nakamura, T. S. H. Lee, T. Sato, Phys. Rev. D 84, 114019 (2011), arXiv:1106.4523
-
[34]
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Neural Computation 1(4), 541 (1989)
-
[35]
C. Zhang, S. Bengio, M. Hardt, B. Recht, O. Vinyals, Understanding deep learning requires rethinking generalization, CoRR abs/1611.03530 (2016). arXiv:1611.03530. URL http://arxiv.org/abs/1611.03530