• [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