Open Access
Issue
A&A
Volume 668, December 2022
Article Number A139
Number of page(s) 14
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202039956
Published online 15 December 2022
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, software available from tensorflow.org [Google Scholar]
  2. Agarap, A. F. 2018, ArXiv e-prints [arXiv: 1803.08375] [Google Scholar]
  3. Akimkin, V., Zhukovska, S., Wiebe, D., et al. 2013, ApJ, 766, 8 [NASA ADS] [CrossRef] [Google Scholar]
  4. Bai, X.-N. 2016, ApJ, 821, 80 [Google Scholar]
  5. Baulch, D. L., Bowman, C. T., Cobos, C. J., et al. 2005, J. Phys. Chem. Ref. Data, 34, 757 [Google Scholar]
  6. Bovino, S., Grassi, T., Latif, M. A., & Schleicher, D. R. G. 2013, MNRAS, 434, L36 [CrossRef] [Google Scholar]
  7. Bovino, S., Ferrada-Chamorro, S., Lupi, A., et al. 2019, ApJ, 887, 224 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bruderer, S., Doty, S. D., & Benz, A. O. 2009, ApJS, 183, 179 [Google Scholar]
  9. Brunton, S. L., Proctor, J. L., & Kutz, J. N. 2016, Proc. Natl. Acad. Sci., 113, 3932 [Google Scholar]
  10. Chakraborty, S., Tomsett, R., Raghavendra, R., et al. 2017, in 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/ UIC/ATC/CBDCom/IOP/SCI), 1-6 [Google Scholar]
  11. Champion, K., Lusch, B., Kutz, J. N., & Brunton, S. L. 2019, Proc. Natl. Acad. Sci., 116, 22445 [Google Scholar]
  12. Champion, K., Zheng, P., Aravkin, A. Y., Brunton, S. L., & Kutz, J. N. 2020, IEEE Access, 8, 169259 [CrossRef] [Google Scholar]
  13. Chen, R. T. Q., Rubanova, Y., Bettencourt, J., & Duvenaud, D. 2018, ArXiv e-prints [arXiv: 1806.07366] [Google Scholar]
  14. Chollet, F., et al. 2015, Keras, https://keras.i0 [Google Scholar]
  15. Choudhary, A., Lindner, J. F., Holliday, E. G., et al. 2020, Phys. Rev. E, 101, 062207 [NASA ADS] [CrossRef] [Google Scholar]
  16. Curtis, N. J., Niemeyer, K. E., & Sung, C.-J. 2017, Combustion Flame, 179, 312 [CrossRef] [Google Scholar]
  17. de Mijolla, D., Viti, S., Holdship, J., Manolopoulou, I., & Yates, J. 2019, A&A, 630, A117 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Duff, N., Duff, I., Erisman, A., Reid, C., & Reid, J. 1986, Direct Methods for Sparse Matrices, Monographs on Numerical Analysis (UK: Clarendon Press) Garrod, R. T. 2008, A&A, 491, 239 [Google Scholar]
  19. Glover, S. C. O., & Clark, P. C. 2012, MNRAS, 421, 116 [NASA ADS] [Google Scholar]
  20. Glover, S. C. O., Federrath, C., Mac Low, M. M., & Klessen, R. S. 2010, MNRAS, 404, 2 [Google Scholar]
  21. Gondara, L. 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) [Google Scholar]
  22. Gong, M., Ostriker, E. C., & Wolfire, M. G. 2017, ApJ, 843, 38 [NASA ADS] [CrossRef] [Google Scholar]
  23. Grassi, T., Merlin, E., Piovan, L., Buonomo, U., & Chiosi, C. 2011, ArXiv eprints [arXiv:1103.0509] [Google Scholar]
  24. Grassi, T., Bovino, S., Gianturco, F. A., Baiocchi, P., & Merlin, E. 2012, MNRAS, 425, 1332 [NASA ADS] [CrossRef] [Google Scholar]
  25. Grassi, T., Bovino, S., Schleicher, D., & Gianturco, F. A. 2013, MNRAS, 431, 1659 [CrossRef] [Google Scholar]
  26. Grassi, T., Bovino, S., Haugbølle, T., & Schleicher, D. R. G. 2017, MNRAS, 466, 1259 [Google Scholar]
  27. Grassi, T., Padovani, M., Ramsey, J. P., et al. 2019, MNRAS, 484, 161 [NASA ADS] [CrossRef] [Google Scholar]
  28. Gressel, O., Ramsey, J. P., Brinch, C., et al. 2020, ApJ, 896, 126 [Google Scholar]
  29. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357 [NASA ADS] [CrossRef] [Google Scholar]
  30. Henning, T., & Semenov, D. 2013, Chem. Rev., 113, 9016 [Google Scholar]
  31. Herbst, E., & van Dishoeck, E. F. 2009, ARA&A, 47, 427 [NASA ADS] [CrossRef] [Google Scholar]
  32. Heyl, J., Viti, S., Holdship, J., & Feeney, S. M. 2020, ApJ, 904, 197 [NASA ADS] [CrossRef] [Google Scholar]
  33. Hindmarsh, A. C., Brown, P. N., Grant, K. E., et al. 2005, ACM Trans. Math. Softw., 31, 363 [CrossRef] [Google Scholar]
  34. Hoffmann, M., Fröhner, C., & Noé, F. 2019, J. Chem. Phys., 150, 025101 [NASA ADS] [CrossRef] [Google Scholar]
  35. Holdship, J., Jeffrey, N., Makrymallis, A., Viti, S., & Yates, J. 2018, ApJ, 866, 116 [NASA ADS] [CrossRef] [Google Scholar]
  36. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
  37. Ilee, J. D., Forgan, D. H., Evans, M. G., et al. 2017, MNRAS, 472, 189 [NASA ADS] [CrossRef] [Google Scholar]
  38. Jolliffe, I. 2002, Principal Component Analysis (New York: Springer Verlag) [Google Scholar]
  39. Jørgensen, J. K., Belloche, A., & Garrod, R. T. 2020, Ann. Rev. Astron. Astrophys., 58, 727 [CrossRef] [Google Scholar]
  40. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv: 1412.6980] [Google Scholar]
  41. Kingma, D. P., & Welling, M. 2013, ArXiv e-prints [arXiv: 1312.6114] [Google Scholar]
  42. Kramer, M. A. 1991, AIChE J., 37, 233 [CrossRef] [Google Scholar]
  43. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 86, 2278 [Google Scholar]
  44. Lipton, Z. C. 2016, ArXiv e-prints [arXiv: 1606.03490] [Google Scholar]
  45. Long, Z., Lu, Y., Ma, X., & Dong, B. 2017, ArXiv e-prints [arXiv: 1710.09668] [Google Scholar]
  46. Lupi, A., & Bovino, S. 2020, MNRAS, 492, 2818 [Google Scholar]
  47. McGuire, B. A. 2018, ApJS, 239, 17 [Google Scholar]
  48. Miller, T. 2017, ArXiv e-prints [arXiv: 1706.07269] [Google Scholar]
  49. Nejad, L. 2005, Ap&SS, 299, 1 [NASA ADS] [CrossRef] [Google Scholar]
  50. Nicolini, P., & Frezzato, D. 2013, J. Chem. Phys., 138, 234102 [NASA ADS] [CrossRef] [Google Scholar]
  51. Perini, F., Galligani, E., & Reitz, R. D. 2012, Energy Fuels, 26, 4804 [CrossRef] [Google Scholar]
  52. Plewa, T., & Müller, E. 1999, A&A, 342, 179 [NASA ADS] [Google Scholar]
  53. Rab, C., Elbakyan, V., Vorobyov, E., et al. 2017, A&A, 604, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Rackauckas, C., Innes, M., Ma, Y., et al. 2019, ArXiv eprints [arXiv:1902.02376] [Google Scholar]
  55. Raissi, M., & Karniadakis, G. E. 2018, J. Comput. Phys., 357, 125 [NASA ADS] [CrossRef] [Google Scholar]
  56. Raissi, M., Perdikaris, P., & Karniadakis, G. 2019, J. Comput. Phys., 378, 686 [NASA ADS] [CrossRef] [Google Scholar]
  57. Röllig, M., Abel, N. P., Bell, T., et al. 2007, A&A, 467, 187 [Google Scholar]
  58. Rubanova, Y., Chen, R. T. Q., & Duvenaud, D. K. 2019, in Advances in Neural Information Processing Systems 32, eds. H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, & R. Garnett (New York: Curran Associates, Inc.), 5320 [Google Scholar]
  59. Ruffle, D. P., Rae, J. G. L., Pilling, M. J., Hartquist, T. W., & Herbst, E. 2002, A&A, 381, L13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. 1988, Learning Representations by Back-Propagating Errors (Cambridge, MA, USA: MIT Press), 696 [Google Scholar]
  61. Semenov, D., Wiebe, D., & Henning, T. 2004, A&A, 417, 93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Semenov, D., Hersant, F., Wakelam, V., et al. 2010, A&A, 522, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Shampine, L. F., & Reichelt, M. W. 1997, SIAM J. Sci. Comput., 18, 1 [NASA ADS] [CrossRef] [Google Scholar]
  64. Shlens, J. 2014, ArXiv e-prints [arXiv: 1404.1100] [Google Scholar]
  65. Sipilä, O., Hugo, E., Harju, J., et al. 2010, A&A, 509, A98 [Google Scholar]
  66. Tian, X., Saito, H., Preis, S. V., et al. 2013, in 2013 IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum, 1149 [CrossRef] [Google Scholar]
  67. Tibshirani, R. 1996, J. R. Stat. Soc. Ser. B Methodol., 58, 267 [Google Scholar]
  68. Tupper, P. 2002, BIT Numerical Math., 42, 447 [CrossRef] [Google Scholar]
  69. Valorani, M., & Goussis, D. A. 2001, J. Comput. Phys., 169, 44 [NASA ADS] [CrossRef] [Google Scholar]
  70. Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nat. Methods, 17, 261 [Google Scholar]
  71. Wakelam, V., Herbst, E., Loison, J. C., et al. 2012, ApJS, 199, 21 [Google Scholar]
  72. Walsh, C., Millar, T. J., Nomura, H., et al. 2014, A&A, 563, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Wiebe, D., Semenov, D., & Henning, T. 2003, A&A, 399, 197 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  74. Wiewel, S., Becher, M., & Thuerey, N. 2019, Comput. Graphics Forum, 38, 71 [CrossRef] [Google Scholar]
  75. Woitke, P., Kamp, I., & Thi, W. F. 2009, A&A, 501, 383 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  76. Xu, R., Bai, X.-N., Öberg, K., & Zhang, H. 2019, ApJ, 872, 107 [NASA ADS] [CrossRef] [Google Scholar]
  77. Yıldız, Ç., Heinonen, M., & Lähdesmäki, H. 2019, ArXiv e-prints [arXiv:1905.10994] [Google Scholar]
  78. Yoon, J., & Kwak, K. 2018, J. Phys. Conf. Ser., 1031, 012023 [NASA ADS] [CrossRef] [Google Scholar]
  79. Zhou, C., & Paffenroth, R. C. 2017, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 665 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.