{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T07:57:24Z","timestamp":1776931044999,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,16]]},"DOI":"10.1145\/3712285.3759850","type":"proceedings-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T16:05:39Z","timestamp":1762963539000},"page":"1888-1900","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9418-3861","authenticated-orcid":false,"given":"Zhuoqiang","family":"Guo","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijiang, China and University of Chinese Academy of Sciences, Beijing, Beijiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2771-6271","authenticated-orcid":false,"given":"Runze","family":"Mao","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7278-775X","authenticated-orcid":false,"given":"Lijun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Graduate School of Engineering, Osaka University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6361-5948","authenticated-orcid":false,"given":"Guangming","family":"Tan","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8539-8326","authenticated-orcid":false,"given":"Weile","family":"Jia","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1149-1998","authenticated-orcid":false,"given":"Zhi X.","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China and AI for Science Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,15]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"2024. Top 500 List. https:\/\/top500.org\/lists\/top500\/2024\/11\/."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Abouelmagd Abdelsamie Gordon Fru Timo Oster Felix Dietzsch G\u00e1bor Janiga and Dominique Th\u00e9venin. 2016. Towards direct numerical simulations of low-Mach number turbulent reacting and two-phase flows using immersed boundaries. Computers & Fluids 131 (2016) 123\u2013141.","DOI":"10.1016\/j.compfluid.2016.03.017"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Abouelmagd Abdelsamie Ghislain Lartigue Christos\u00a0E Frouzakis and Dominique Thevenin. 2021. The Taylor\u2013Green vortex as a benchmark for high-fidelity combustion simulations using low-Mach solvers. Computers & Fluids 223 (2021) 104935.","DOI":"10.1016\/j.compfluid.2021.104935"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Abouelmagd Abdelsamie Ghislain Lartigue Christos\u00a0E. Frouzakis and Dominique Th\u00e9venin. 2021. The Taylor\u2013Green vortex as a benchmark for high-fidelity combustion simulations using low-Mach solvers. Comput. Fluids 223 (2021) 104935.","DOI":"10.1016\/j.compfluid.2021.104935"},{"key":"e_1_3_3_2_6_2","unstructured":"Satish Balay Shrirang Abhyankar Mark\u00a0F. Adams Steven Benson Jed Brown Peter Brune Kris Buschelman Emil\u00a0M. Constantinescu Lisandro Dalcin Alp Dener Victor Eijkhout Jacob Faibussowitsch William\u00a0D. Gropp V\u00e1clav Hapla Tobin Isaac Pierre Jolivet Dmitry Karpeev Dinesh Kaushik Matthew\u00a0G. Knepley Fande Kong Scott Kruger Dave\u00a0A. May Lois\u00a0Curfman McInnes Richard\u00a0Tran Mills Lawrence Mitchell Todd Munson Jose\u00a0E. Roman Karl Rupp Patrick Sanan Jason Sarich Barry\u00a0F. Smith Stefano Zampini Hong Zhang Hong Zhang and Junchao Zhang. 2025. PETSc Web page. https:\/\/petsc.org\/. https:\/\/petsc.org\/"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Josette Bellan. 2000. Supercritical (and subcritical) fluid behavior and modeling: drops streams shear and mixing layers jets and sprays. Progress in energy and combustion science 26 4-6 (2000) 329\u2013366.","DOI":"10.1016\/S0360-1285(00)00008-3"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Pierre Boivin Muhammad Tayyab and Song Zhao. 2021. Benchmarking a lattice-Boltzmann solver for reactive flows: Is the method worth the effort for combustion? Physics of Fluids 33 7 (2021).","DOI":"10.1063\/5.0057352"},{"key":"e_1_3_3_2_9_2","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared\u00a0D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et\u00a0al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877\u20131901."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Yuqing Cai Ruixin Yang Han Li Jiayang Xu Ke Xiao Zhi\u00a0X Chen and Hu Wang. 2025. Efficient machine learning method for supercritical combustion: Predicting real-fluid properties and chemical ODEs. Aerospace Science and Technology (2025) 110034.","DOI":"10.1016\/j.ast.2025.110034"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Jacqueline\u00a0H Chen Alok Choudhary Bronis De\u00a0Supinski Matthew DeVries Evatt\u00a0R Hawkes Scott Klasky Wei-Keng Liao Kwan-Liu Ma John Mellor-Crummey Norbert Podhorszki et\u00a0al. 2009. Terascale direct numerical simulations of turbulent combustion using S3D. Computational Science & Discovery 2 1 (2009) 015001.","DOI":"10.1088\/1749-4699\/2\/1\/015001"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Wai\u00a0Tong Chung Aashwin\u00a0Ananda Mishra and Matthias Ihme. 2022. Interpretable data-driven methods for subgrid-scale closure in LES for transcritical LOX\/GCH4 combustion. Combustion and Flame 239 (2022) 111758.","DOI":"10.1016\/j.combustflame.2021.111758"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977967.2"},{"key":"e_1_3_3_2_14_2","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1810.04805 (2018)."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Tianjie Ding Thomas Readshaw Stelios Rigopoulos and WP Jones. 2021. Machine learning tabulation of thermochemistry in turbulent combustion: An approach based on hybrid flamelet\/random data and multiple multilayer perceptrons. Combustion and Flame 231 (2021) 111493.","DOI":"10.1016\/j.combustflame.2021.111493"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Pascale Domingo and Luc Vervisch. 2023. Recent developments in DNS of turbulent combustion. Proceedings of the Combustion Institute 39 2 (2023) 2055\u20132076.","DOI":"10.1016\/j.proci.2022.06.030"},{"key":"e_1_3_3_2_17_2","volume-title":"Combustion","author":"Glassman Irvin","year":"2014","unstructured":"Irvin Glassman, Richard\u00a0A Yetter, and Nick\u00a0G Glumac. 2014. Combustion. Academic press."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503221.3508425"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Umut Guven and Guillaume Ribert. 2019. Impact of non-ideal transport modeling on supercritical flow simulation. Proceedings of the Combustion Institute 37 3 (2019) 3255\u20133262.","DOI":"10.1016\/j.proci.2018.05.013"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Evatt\u00a0R Hawkes Obulesu Chatakonda Hemanth Kolla Alan\u00a0R Kerstein and Jacqueline\u00a0H Chen. 2012. A petascale direct numerical simulation study of the modelling of flame wrinkling for large-eddy simulations in intense turbulence. Combustion and flame 159 8 (2012) 2690\u20132703.","DOI":"10.1016\/j.combustflame.2011.11.020"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Marc\u00a0T Henry\u00a0de Frahan Jon\u00a0S Rood Marc\u00a0S Day Hariswaran Sitaraman Shashank Yellapantula Bruce\u00a0A Perry Ray\u00a0W Grout Ann Almgren Weiqun Zhang John\u00a0B Bell et\u00a0al. 2023. PeleC: An adaptive mesh refinement solver for compressible reacting flows. The International Journal of High Performance Computing Applications 37 2 (2023) 115\u2013131.","DOI":"10.1177\/10943420221121151"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Matthias Ihme Wai\u00a0Tong Chung and Aashwin\u00a0Ananda Mishra. 2022. Combustion machine learning: Principles progress and prospects. Progress in Energy and Combustion Science 91 (2022) 101010.","DOI":"10.1016\/j.pecs.2022.101010"},{"key":"e_1_3_3_2_23_2","first-page":"1","volume-title":"International workshop on coupled methods in numerical dynamics","author":"Jasak Hrvoje","year":"2007","unstructured":"Hrvoje Jasak, Aleksandar Jemcov, Zeljko Tukovic, et\u00a0al. 2007. OpenFOAM: A C++ library for complex physics simulations. In International workshop on coupled methods in numerical dynamics , Vol.\u00a01000. 1\u201320."},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00009"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Llu\u00eds Jofre and Javier Urzay. 2021. Transcritical diffuse-interface hydrodynamics of propellants in high-pressure combustors of chemical propulsion systems. Progress in Energy and Combustion Science 82 (2021) 100877.","DOI":"10.1016\/j.pecs.2020.100877"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Katharina Kohse-H\u00f6inghaus. 2021. Combustion in the future: The importance of chemistry. Proceedings of the Combustion Institute 38 1 (2021) 1\u201356.","DOI":"10.1016\/j.proci.2020.06.375"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Tianfeng Lu and Chung\u00a0K Law. 2009. Toward accommodating realistic fuel chemistry in large-scale computations. Progress in Energy and Combustion Science 35 2 (2009) 192\u2013215.","DOI":"10.1016\/j.pecs.2008.10.002"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Peter\u00a0C Ma Yu Lv and Matthias Ihme. 2017. An entropy-stable hybrid scheme for simulations of transcritical real-fluid flows. J. Comput. Phys. 340 (2017) 330\u2013357.","DOI":"10.1016\/j.jcp.2017.03.022"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"publisher","unstructured":"Runze Mao Minqi Lin Yan Zhang Tianhan Zhang Zhi-Qin\u00a0John Xu and Zhi\u00a0X. Chen. 2023. DeepFlame: A deep learning empowered open-source platform for reacting flow simulations. Computer Physics Communications 291 (2023) 108842. 10.1016\/j.cpc.2023.108842","DOI":"10.1016\/j.cpc.2023.108842"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Runze Mao Min Zhang Yingrui Wang Han Li Jiayang Xu Xinyu Dong Yan Zhang and Zhi\u00a0X Chen. 2024. An integrated framework for accelerating reactive flow simulation using GPU and machine learning models. Proceedings of the Combustion Institute 40 1-4 (2024) 105512.","DOI":"10.1016\/j.proci.2024.105512"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"Petro\u00a0Junior Milan Jean-Pierre Hickey Xingjian Wang and Vigor Yang. 2021. Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields. J. Comput. Phys. 444 (2021) 110567.","DOI":"10.1016\/j.jcp.2021.110567"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Daniel Mira Eduardo\u00a0J P\u00e9rez-S\u00e1nchez Ricard Borrell and Guillaume Houzeaux. 2023. HPC-enabling technologies for high-fidelity combustion simulations. Proceedings of the Combustion Institute 39 4 (2023) 5091\u20135125.","DOI":"10.1016\/j.proci.2022.07.222"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"crossref","unstructured":"Florian Monnier and Guillaume Ribert. 2022. Simulation of high-pressure methane-oxygen combustion with a new reduced chemical mechanism. Combustion and Flame 235 (2022) 111735.","DOI":"10.1016\/j.combustflame.2021.111735"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"crossref","unstructured":"Florian Monnier and Guillaume Ribert. 2023. Numerical simulations of supercritical CH4\/O2 flame propagation in inhomogeneous mixtures following ignition. Proceedings of the Combustion Institute 39 2 (2023) 2747\u20132755.","DOI":"10.1016\/j.proci.2022.07.213"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Florian Monnier and Guillaume Ribert. 2023. Numerical simulations of supercritical CH4\/O2 flame propagation in inhomogeneous mixtures following ignition. Proc. Combust. Inst 39 2 (2023) 2747\u20132755.","DOI":"10.1016\/j.proci.2022.07.213"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Vincent Moureau P Domingo and Luc Vervisch. 2011. From large-eddy simulation to direct numerical simulation of a lean premixed swirl flame: Filtered laminar flame-pdf modeling. Combustion and Flame 158 7 (2011) 1340\u20131357.","DOI":"10.1016\/j.combustflame.2010.12.004"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS49936.2021.00016"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"crossref","unstructured":"Joseph\u00a0C Oefelein. 2005. Thermophysical characteristics of shear-coaxial LOX\u2013H2 flames at supercritical pressure. Proceedings of the Combustion Institute 30 2 (2005) 2929\u20132937.","DOI":"10.1016\/j.proci.2004.08.212"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-61142-8_588"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17012-7"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","unstructured":"T. Poinsot. 2017. Prediction and control of combustion instabilities in real engines. Proceedings of the Combustion Institute 36 1 (2017) 1\u201328. 10.1016\/j.proci.2016.05.007","DOI":"10.1016\/j.proci.2016.05.007"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Alexei\u00a0Y Poludnenko Jessica Chambers Kareem Ahmed Vadim\u00a0N Gamezo and Brian\u00a0D Taylor. 2019. A unified mechanism for unconfined deflagration-to-detonation transition in terrestrial chemical systems and type Ia supernovae. Science 366 6465 (2019) eaau7365.","DOI":"10.1126\/science.aau7365"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"crossref","unstructured":"Maziar Raissi Paris Perdikaris and George\u00a0E Karniadakis. 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics 378 (2019) 686\u2013707.","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"crossref","unstructured":"Anthony\u00a0M Ruiz Guilhem Lacaze Joseph\u00a0C Oefelein Raphae\u00eb Mari B\u00e9n\u00e9dicte Cuenot Laurent Selle and Thierry Poinsot. 2016. Numerical benchmark for high-Reynolds-number supercritical flows with large density gradients. Aiaa Journal 54 5 (2016) 1445\u20131460.","DOI":"10.2514\/1.J053931"},{"key":"e_1_3_3_2_45_2","doi-asserted-by":"crossref","unstructured":"Manabu Saito Jiangkuan Xing Jun Nagao and Ryoichi Kurose. 2023. Data-driven simulation of ammonia combustion using neural ordinary differential equations (NODE). Applications in Energy and Combustion Science 16 (2023) 100196.","DOI":"10.1016\/j.jaecs.2023.100196"},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.5555\/3433701.3433763"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"crossref","unstructured":"Thomas Schmitt Yoann M\u00e9ry Matthieu Boileau and Sebastien Candel. 2011. Large-eddy simulation of oxygen\/methane flames under transcritical conditions. Proceedings of the Combustion Institute 33 1 (2011) 1383\u20131390.","DOI":"10.1016\/j.proci.2010.07.036"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"crossref","unstructured":"AG Tomboulides JCY Lee and SA Orszag. 1997. Numerical simulation of low Mach number reactive flows. Journal of Scientific Computing 12 (1997) 139\u2013167.","DOI":"10.1023\/A:1025669715376"},{"key":"e_1_3_3_2_49_2","doi-asserted-by":"crossref","unstructured":"Zhijian\u00a0J Wang Krzysztof Fidkowski R\u00e9mi Abgrall Francesco Bassi Doru Caraeni Andrew Cary Herman Deconinck Ralf Hartmann Koen Hillewaert Hung\u00a0T Huynh et\u00a0al. 2013. High-order CFD methods: current status and perspective. International Journal for Numerical Methods in Fluids 72 8 (2013) 811\u2013845.","DOI":"10.1002\/fld.3767"},{"key":"e_1_3_3_2_50_2","unstructured":"Wikipedia contributors. 2024. SpaceXRaptor \u2014 Wikipedia The Free Encyclopedia. https:\/\/en.wikipedia.org\/wiki\/SpaceX_Raptor. [Online; accessed 7-April-2024]."},{"key":"e_1_3_3_2_51_2","unstructured":"Jiayang Xu Yifan Xu Zifeng Weng Yuqing Cai Runze Mao Ruixin Yang and Zhi\u00a0X. Chen. 2023. Detailed simulation of LOX\/GCH4 flame-vortex interaction in supercritical Taylor-Green flows with machine learning. arxiv:https:\/\/arXiv.org\/abs\/2312.04830\u00a0[physics.flu-dyn]"},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"crossref","unstructured":"Vigor Yang. 2000. Modeling of supercritical vaporization mixing and combustion processes in liquid-fueled propulsion systems. Proceedings of the Combustion Institute 28 1 (2000) 925\u2013942.","DOI":"10.1016\/S0082-0784(00)80299-4"},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"crossref","unstructured":"Min Zhang Runze Mao Han Li Zhenhua An and Zhi\u00a0X Chen. 2024. Graphics processing unit\/artificial neural network-accelerated large-eddy simulation of swirling premixed flames. Physics of Fluids 36 5 (2024).","DOI":"10.1063\/5.0202321"},{"key":"e_1_3_3_2_54_2","doi-asserted-by":"crossref","unstructured":"Thorsten Zirwes Marvin Sontheimer Feichi Zhang Abouelmagd Abdelsamie Francisco E\u00a0Hern\u00e1ndez P\u00e9rez Oliver\u00a0T Stein Hong\u00a0G Im Andreas Kronenburg and Henning Bockhorn. 2023. Assessment of numerical accuracy and parallel performance of OpenFOAM and its reacting flow extension EBIdnsFoam. Flow Turbulence and Combustion 111 2 (2023) 567\u2013602.","DOI":"10.1007\/s10494-023-00449-8"},{"key":"e_1_3_3_2_55_2","doi-asserted-by":"crossref","unstructured":"Thorsten Zirwes Marvin Sontheimer Feichi Zhang Abouelmagd Abdelsamie Francisco E\u00a0Hern\u00e1ndez P\u00e9rez Oliver\u00a0T Stein Hong\u00a0G Im Andreas Kronenburg and Henning Bockhorn. 2023. Assessment of numerical accuracy and parallel performance of OpenFOAM and its reacting flow extension EBIdnsFoam. Flow Turbulence and Combustion 111 2 (2023) 567\u2013602.","DOI":"10.1007\/s10494-023-00449-8"}],"event":{"name":"SC '25: The International Conference for High Performance Computing, Networking, Storage and Analysis","location":"St. Louis MO USA","acronym":"SC '25","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing"]},"container-title":["Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3712285.3759850","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T18:32:28Z","timestamp":1773253948000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3712285.3759850"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,15]]},"references-count":54,"alternative-id":["10.1145\/3712285.3759850","10.1145\/3712285"],"URL":"https:\/\/doi.org\/10.1145\/3712285.3759850","relation":{},"subject":[],"published":{"date-parts":[[2025,11,15]]},"assertion":[{"value":"2025-11-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}