{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:17:17Z","timestamp":1770884237791,"version":"3.50.1"},"reference-count":15,"publisher":"The Open Journal","issue":"67","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JOSS"],"published-print":{"date-parts":[[2021,11,26]]},"DOI":"10.21105\/joss.03199","type":"journal-article","created":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T14:00:00Z","timestamp":1637935200000},"page":"3199","source":"Crossref","is-referenced-by-count":2,"title":["Sapsan: Framework for Supernovae Turbulence Modeling with Machine Learning"],"prefix":"10.21105","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4311-8490","authenticated-orcid":false,"given":"Platon","family":"Karpov","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6809-8943","authenticated-orcid":false,"given":"Iskandar","family":"Sitdikov","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3176-8042","authenticated-orcid":false,"given":"Chengkun","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2624-0056","authenticated-orcid":false,"given":"Chris","family":"Fryer","sequence":"additional","affiliation":[]}],"member":"8722","reference":[{"key":"ref1","unstructured":"Lilly, D. K., On the application of the eddy viscosity concept in the Inertial sub-range of turbulence, NCAR Manuscript 123, 1966, jan, http:\/\/dx.doi.org\/10.5065\/D67H1GGQ, 1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.93.031301"},{"key":"ref3","unstructured":"Treuille, Adrien, Turn Python Scripts into Beautiful ML Tools, Towards Data Science, 8, 2019, oct, 10"},{"key":"ref4","unstructured":"Ronacher, Armin, Click, https:\/\/click.palletsprojects.com\/, 2021"},{"key":"ref5","unstructured":"Databricks, Inc, MLflow, 2020, GitHub, GitHub repository, https:\/\/github.com\/mlflow\/mlflow"},{"key":"ref6","unstructured":"PyTorch: An Imperative Style, High-Performance Deep Learning Library, Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith, Advances in Neural Information Processing Systems 32, Wallach, H. and Larochelle, H. and Beygelzimer, A. and d\u2019 Alch\u00e9-Buc, F. and Fox, E. and Garnett, R., 8024\u20138035, 2019, Curran Associates, Inc., http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"ref7","unstructured":"Scikit-learn: Machine Learning in Python, Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E., Journal of Machine Learning Research, 12, 2825\u20132830, 2011"},{"key":"ref8","unstructured":"Zhang, Weiwei and Zhu, Linyang and Liu, Yilang and Kou, Jiaqing, Machine learning methods for turbulence modeling in subsonic flows over airfoils, arXiv e-prints, Physics - Fluid Dynamics, Physics - Computational Physics, 2018, jun, arXiv:1806.05904, arXiv:1806.05904, arXiv, 1806.05904, physics.flu-dyn, https:\/\/ui.adsabs.harvard.edu\/abs\/2018arXiv180605904Z, Provided by the SAO\/NASA Astrophysics Data System, 6"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.91.045002"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/14685240802376389"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/nature12128"},{"key":"ref12","unstructured":"Murphy, Kevin P., Machine Learning: A Probabilistic Perspective, The MIT Press, 2012, 492-493, 14.4.3"},{"key":"ref13","unstructured":"Kolesnikov, Sergey, Accelerated DL R&D, 2018, GitHub, GitHub repository, https:\/\/github.com\/catalyst-team\/catalyst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1126\/science.359.6377.725"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1017\/S0022112094002296"}],"container-title":["Journal of Open Source Software"],"original-title":[],"link":[{"URL":"https:\/\/joss.theoj.org\/papers\/10.21105\/joss.03199.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T14:00:07Z","timestamp":1637935207000},"score":1,"resource":{"primary":{"URL":"https:\/\/joss.theoj.org\/papers\/10.21105\/joss.03199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,26]]},"references-count":15,"journal-issue":{"issue":"67","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["10.21105\/joss.03199"],"URL":"https:\/\/doi.org\/10.21105\/joss.03199","relation":{"has-review":[{"id-type":"uri","id":"https:\/\/github.com\/openjournals\/joss-reviews\/issues\/3199","asserted-by":"subject"}],"references":[{"id-type":"doi","id":"10.5281\/zenodo.5720254\u201d","asserted-by":"subject"}]},"ISSN":["2475-9066"],"issn-type":[{"value":"2475-9066","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,26]]}}}