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Home All issues Volume 684 (April 2024) A&A, 684 (2024) A203 References
Open Access
Issue
A&A
Volume 684, April 2024
Article Number A203
Number of page(s) 12
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202449193
Published online 26 April 2024
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org/ [Google Scholar]
  2. Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481 [NASA ADS] [CrossRef] [Google Scholar]
  3. Bakes, E. L. O., & Tielens, A. G. G. M. 1994, ApJ, 427, 822 [NASA ADS] [CrossRef] [Google Scholar]
  4. Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B. 2017, SIAM Rev., 59, 65 [Google Scholar]
  5. Bovino, S., Grassi, T., Capelo, P. R., Schleicher, D. R. G., & Banerjee, R. 2016, A&A, 590, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Branca, L., & Pallottini, A. 2023, MNRAS, 518, 5718 [Google Scholar]
  7. Caselli, P., & Ceccarelli, C. 2012, A&A Rev., 20, 56 [NASA ADS] [CrossRef] [Google Scholar]
  8. Cen, R. 1992, ApJS, 78, 341 [NASA ADS] [CrossRef] [Google Scholar]
  9. Chen, T., & Chen, H. 1995, IEEE Transactions on Neural Networks, 6, 911 [CrossRef] [Google Scholar]
  10. Chen, T., & Guestrin, C. 2016, KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785 [CrossRef] [Google Scholar]
  11. Ciesla, L., Ilbert, O., Buat, V., et al. 2024, A&A, in press, https://doi.org/10.1051/0004-6361/202348091 [Google Scholar]
  12. Cybenko, G. 1989, Math. Control Signals Syst., 2, 303 [Google Scholar]
  13. Danehkar, A., Oey, M. S., & Gray, W. J. 2022, ApJ, 937, 68 [NASA ADS] [CrossRef] [Google Scholar]
  14. Decataldo, D., Pallottini, A., Ferrara, A., Vallini, L., & Gallerani, S. 2019, MNRAS, 487, 3377 [Google Scholar]
  15. Decataldo, D., Lupi, A., Ferrara, A., Pallottini, A., & Fumagalli, M. 2020, MNRAS, 497, 4718 [NASA ADS] [CrossRef] [Google Scholar]
  16. Ferland, G. J., Chatzikos, M., Guzmán, F., et al. 2017, Rev. Mex. Astron. Astrofis., 53, 385 [NASA ADS] [Google Scholar]
  17. Galli, D., & Palla, F. 1998, A&A, 335, 403 [NASA ADS] [Google Scholar]
  18. Galligan, T. P., Katz, H., Kimm, T., et al. 2019, arXiv e-prints [arXiv: 1981.81264] [Google Scholar]
  19. Glorot, X., & Bengio, Y. 2010, in Proceedings of Machine Learning Research, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, eds. Y. W. Teh & M. Titterington, Chia Laguna Resort, Sardinia, Italy, 9, 249 [Google Scholar]
  20. Glover, S. C. O., & Abel, T. 2008, MNRAS, 388, 1627 [NASA ADS] [CrossRef] [Google Scholar]
  21. Glover, S. C. O., Federrath, C., Mac Low, M. M., & Klessen, R. S. 2010, MNRAS, 404, 2 [Google Scholar]
  22. Gnedin, N. Y., & Hollon, N. 2012, ApJS, 202, 13 [Google Scholar]
  23. Grassi, T., Bovino, S., Schleicher, D. R. G., et al. 2014, MNRAS, 439, 2386 [Google Scholar]
  24. Grassi, T., Nauman, F., Ramsey, J. P., et al. 2022, A&A, 668, A139 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Gray, W. J., Oey, M. S., Silich, S., & Scannapieco, E. 2019, ApJ, 887, 161 [NASA ADS] [CrossRef] [Google Scholar]
  26. Habing, H. J. 1968, Bull. Astron. Inst. Netherlands, 19, 421 [Google Scholar]
  27. Hennigh, O., Narasimhan, S., Nabian, M. A., et al. 2020, arXiv e-prints [arXiv:2012.07938] [Google Scholar]
  28. Heyl, J., Butterworth, J., & Viti, S. 2023, MNRAS, 526, 404 [NASA ADS] [CrossRef] [Google Scholar]
  29. Hindmarsh, A. C. 2019, Astrophysics Source Code Library [record ascl:1905.021] [Google Scholar]
  30. Hirashita, H., & Ferrara, A. 2002, MNRAS, 337, 921 [NASA ADS] [CrossRef] [Google Scholar]
  31. Holdship, J., Viti, S., Jiménez-Serra, I., Makrymallis, A., & Priestley, F. 2017, AJ, 154, 38 [NASA ADS] [CrossRef] [Google Scholar]
  32. Holdship, J., Viti, S., Haworth, T. J., & Ilee, J. D. 2021, A&A, 653, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Jin, P., Lu, L., Tang, Y., & Karniadakis, G. E. 2020, Neural Networks, 130, 85 [CrossRef] [Google Scholar]
  34. Jura, M. 1975, ApJ, 197, 575 [NASA ADS] [CrossRef] [Google Scholar]
  35. Katz, H. 2022, MNRAS, 512, 348 [NASA ADS] [CrossRef] [Google Scholar]
  36. Katz, H., Galligan, T. P., Kimm, T., et al. 2019, MNRAS, 487, 5902 [NASA ADS] [CrossRef] [Google Scholar]
  37. Kim, J.-G., Kim, W.-T., & Ostriker, E. C. 2018, ApJ, 859, 68 [NASA ADS] [CrossRef] [Google Scholar]
  38. Kingma, D. P., & Ba, J. 2014, arXiv e-prints [arXiv: 1412.6980] [Google Scholar]
  39. Kumar, A., & Fisher, R. T. 2013, MNRAS, 431, 455 [NASA ADS] [CrossRef] [Google Scholar]
  40. Li, Z., Kovachki, N., Azizzadenesheli, K., et al. 2020, arXiv e-prints [arXiv:2010.08895] [Google Scholar]
  41. Lu, L., Jin, P., & Karniadakis, G. E. 2019a, arXiv e-prints [arXiv:1910.03193] [Google Scholar]
  42. Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. 2019b, arXiv e-prints [arXiv: 1907.04502] [Google Scholar]
  43. Lu, L., Jin, P., Pang, G., Zhang, Z., & Karniadakis, G. E. 2021, Nat. Mach. Intell., 3, 218 [CrossRef] [Google Scholar]
  44. Lu, L., Meng, X., Cai, S., et al. 2022, Comp. Methods Appl. Mech. Eng., 393, 114778 [CrossRef] [Google Scholar]
  45. Lundberg, S. M., Erion, G. G., & Lee, S.-I. 2018, arXiv e-prints [arXiv: 1802.03888] [Google Scholar]
  46. Lupi, A. 2019, MNRAS, 484, 1687 [NASA ADS] [CrossRef] [Google Scholar]
  47. Maio, U., Dolag, K., Ciardi, B., & Tornatore, L. 2007, MNRAS, 379, 963 [NASA ADS] [CrossRef] [Google Scholar]
  48. Mao, Z., Lu, L., Marxen, O., Zaki, T. A., & Karniadakis, G. E. 2021, J. Comput. Phys., 447, 110698 [NASA ADS] [CrossRef] [Google Scholar]
  49. Obreja, A., Macciò, A. V., Moster, B., et al. 2019, MNRAS, 490, 1518 [CrossRef] [Google Scholar]
  50. Obreja, A., Arrigoni Battaia, F., Macciò, A. V., & Buck, T. 2024, MNRAS, 527, 8078 [Google Scholar]
  51. Olsen, K. P., Pallottini, A., Wofford, A., et al. 2018, Galaxies, 6, 100 [NASA ADS] [CrossRef] [Google Scholar]
  52. Pallottini, A., Ferrara, A., Bovino, S., et al. 2017, MNRAS, 471, 4128 [NASA ADS] [CrossRef] [Google Scholar]
  53. Pallottini, A., Ferrara, A., Decataldo, D., et al. 2019, MNRAS, 487, 1689 [Google Scholar]
  54. Pallottini, A., Ferrara, A., Gallerani, S., et al. 2022, MNRAS, 513, 5621 [NASA ADS] [Google Scholar]
  55. Pang, G., Shen, C., Cao, L., & van den Hengel, A. 2021, ACM Comput. Surv., 54, 1 [Google Scholar]
  56. Prasthofer, M., De Ryck, T., & Mishra, S. 2022, arXiv e-prints [arXiv:2205.11404] [Google Scholar]
  57. Rackauckas, C., Innes, M., Ma, Y., et al. 2019, arXiv e-prints [arXiv:1902.02376] [Google Scholar]
  58. Robinson, D., Avestruz, C., & Gnedin, N. Y. 2024, MNRAS, 528, 255 [NASA ADS] [CrossRef] [Google Scholar]
  59. Röllig, M., Abel, N. P., Bell, T., et al. 2007, A&A, 467, 187 [Google Scholar]
  60. Rosdahl, J., Katz, H., Blaizot, J., et al. 2018, MNRAS, 479, 994 [NASA ADS] [Google Scholar]
  61. Shen, S., Madau, P., Guedes, J., et al. 2013, ApJ, 765, 89 [NASA ADS] [CrossRef] [Google Scholar]
  62. Smith, B. D., Bryan, G. L., Glover, S. C. O., et al. 2017, MNRAS, 466, 2217 [NASA ADS] [CrossRef] [Google Scholar]
  63. Springel, V., Pakmor, R., Zier, O., & Reinecke, M. 2021, MNRAS, 506, 2871 [NASA ADS] [CrossRef] [Google Scholar]
  64. Sulzer, I., & Buck, T. 2023, arXiv e-prints [arXiv:2312.06015] [Google Scholar]
  65. Tacchella, S., Forbes, J. C., & Caplar, N. 2020, MNRAS, 497, 698 [Google Scholar]
  66. Theuns, T., Leonard, A., Efstathiou, G., Pearce, F. R., & Thomas, P. A. 1998, MNRAS, 301, 478 [NASA ADS] [CrossRef] [Google Scholar]
  67. Trebitsch, M., Dubois, Y., Volonteri, M., et al. 2021, A&A, 653, A154 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  68. Ucci, G., Ferrara, A., Pallottini, A., & Gallerani, S. 2018, MNRAS, 477, 1484 [Google Scholar]
  69. Vallini, L., Pallottini, A., Ferrara, A., et al. 2018, MNRAS, 473, 271 [NASA ADS] [CrossRef] [Google Scholar]
  70. Van Rossum, G., & Drake, F. L. 2009, Python 3 Reference Manual (Scotts Valley, CA: CreateSpace) [Google Scholar]
  71. Wakelam, V., Herbst, E., Loison, J. C., et al. 2012, ApJS, 199, 21 [Google Scholar]
  72. Webber, W. R. 1998, ApJ, 506, 329 [NASA ADS] [CrossRef] [Google Scholar]
  73. Wolfire, M. G., Vallini, L., & Chevance, M. 2022, ARA&A, 60, 247 [NASA ADS] [CrossRef] [Google Scholar]
  74. Zhu, M., Zhang, H., Jiao, A., Karniadakis, G. E., & Lu, L. 2023, Comput. Methods Appl. Mech. Eng., 412, 116064 [CrossRef] [Google Scholar]
  75. Ziegler, U. 2016, A&A, 586, A82 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]

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