{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T08:27:42Z","timestamp":1758702462116,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,17]],"date-time":"2019-11-17T00:00:00Z","timestamp":1573948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NS-1617967, CCF-1553645 ,CCF-171819"],"award-info":[{"award-number":["NS-1617967, CCF-1553645 ,CCF-171819"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,11,17]]},"DOI":"10.1145\/3295500.3356147","type":"proceedings-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T19:43:22Z","timestamp":1573155802000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Adaptive neural network-based approximation to accelerate eulerian fluid simulation"],"prefix":"10.1145","author":[{"given":"Wenqian","family":"Dong","sequence":"first","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Xie","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,11,17]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2042-7158.2012.01511.x"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Sanghun Choi Shinjiro Miyawaki and Ching-Long Lin. A feasible computational fluid dynamics study for relationships of structural and functional alterations with particle depositions in severe asthmatic lungs. Computational and mathematical methods in medicine 2018.  Sanghun Choi Shinjiro Miyawaki and Ching-Long Lin. A feasible computational fluid dynamics study for relationships of structural and functional alterations with particle depositions in severe asthmatic lungs. Computational and mathematical methods in medicine 2018.","DOI":"10.1155\/2018\/6564854"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/21681163.2017.1278619"},{"key":"e_1_3_2_1_4_1","volume-title":"A gpu-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics. arXiv preprint arXiv:1903.10134","author":"Xia Yidong","year":"2019","unstructured":"Yidong Xia , Ansel Blumers , Zhen Li , Lixiang Luo , Yu-Hang Tang , Joshua Kane , Hai Huang , Matthew Andrew , Milind Deo , and Jan Goral . A gpu-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics. arXiv preprint arXiv:1903.10134 , 2019 . Yidong Xia, Ansel Blumers, Zhen Li, Lixiang Luo, Yu-Hang Tang, Joshua Kane, Hai Huang, Matthew Andrew, Milind Deo, and Jan Goral. A gpu-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics. arXiv preprint arXiv:1903.10134, 2019."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/lubricants7030023"},{"key":"e_1_3_2_1_6_1","volume-title":"International Conference on High Energy Physics","author":"Radovic Alexander","year":"2016","unstructured":"Alexander Radovic . Neutrino Identification with a Convolutional Neural Network in the NOvA Detectors . In International Conference on High Energy Physics , 2016 . Alexander Radovic. Neutrino Identification with a Convolutional Neural Network in the NOvA Detectors. In International Conference on High Energy Physics, 2016."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep25890"},{"key":"e_1_3_2_1_8_1","volume-title":"NIPS","author":"Racah Evan","year":"2017","unstructured":"Evan Racah , Christopher Beckham , Tegan Maharaj , Samira Kahou , Mr. Prabhat , and Chris Pal . ExtremeWeather : A Large-scale Climate Dataset for Semi-supervised Detection, Localization, and Understanding of Extreme Weather Events . In NIPS , 2017 . Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Kahou, Mr. Prabhat, and Chris Pal. ExtremeWeather: A Large-scale Climate Dataset for Semi-supervised Detection, Localization, and Understanding of Extreme Weather Events. In NIPS, 2017."},{"key":"e_1_3_2_1_9_1","first-page":"59","volume-title":"Computer Graphics Forum","author":"Kim Byungsoo","year":"2019","unstructured":"Byungsoo Kim , Vinicius C Azevedo , Nils Thuerey , Theodore Kim , Markus Gross , and Barbara Solenthaler . Deep fluids: A generative network for parameterized fluid simulations . In Computer Graphics Forum , volume 38 , pages 59 -- 70 . Wiley Online Library , 2019 . Byungsoo Kim, Vinicius C Azevedo, Nils Thuerey, Theodore Kim, Markus Gross, and Barbara Solenthaler. Deep fluids: A generative network for parameterized fluid simulations. In Computer Graphics Forum, volume 38, pages 59--70. Wiley Online Library, 2019."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3306035"},{"key":"e_1_3_2_1_11_1","volume-title":"Data-driven projection method in fluid simulation. Computer Animation and Virtual Worlds, 27(3--4):415--424","author":"Yang Cheng","year":"2016","unstructured":"Cheng Yang , Xubo Yang , and Xiangyun Xiao . Data-driven projection method in fluid simulation. Computer Animation and Virtual Worlds, 27(3--4):415--424 , 2016 . Cheng Yang, Xubo Yang, and Xiangyun Xiao. Data-driven projection method in fluid simulation. Computer Animation and Virtual Worlds, 27(3--4):415--424, 2016."},{"key":"e_1_3_2_1_12_1","volume-title":"Efficient neural architecture search with network morphism. arXiv preprint arXiv:1806.10282","author":"Jin Haifeng","year":"2018","unstructured":"Haifeng Jin , Qingquan Song , and Xia Hu . Efficient neural architecture search with network morphism. arXiv preprint arXiv:1806.10282 , 2018 . Haifeng Jin, Qingquan Song, and Xia Hu. Efficient neural architecture search with network morphism. arXiv preprint arXiv:1806.10282, 2018."},{"key":"e_1_3_2_1_13_1","volume-title":"Design and results. arXiv preprint arXiv:1903.05263","author":"Escalante Hugo Jair","year":"2019","unstructured":"Hugo Jair Escalante , Wei-Wei Tu , Isabelle Guyon , Daniel L Silver , Evelyne Viegas , Yuqiang Chen , Wenyuan Dai , and Qiang Yang . Automl@ neurips 2018 challenge : Design and results. arXiv preprint arXiv:1903.05263 , 2019 . Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, and Qiang Yang. Automl@ neurips 2018 challenge: Design and results. arXiv preprint arXiv:1903.05263, 2019."},{"key":"e_1_3_2_1_14_1","volume-title":"http:\/\/mantaflow.com","author":"Thuerey Nils","year":"2016","unstructured":"Nils Thuerey and Tobias Pfaff . Mantaflow. http:\/\/mantaflow.com , 2016 . Nils Thuerey and Tobias Pfaff. Mantaflow. http:\/\/mantaflow.com, 2016."},{"key":"e_1_3_2_1_15_1","volume-title":"Numerical calculation of time-dependent viscous incompressible flow of fluid with free surface. The physics of fluids, 8(12):2182--2189","author":"Harlow Francis H","year":"1965","unstructured":"Francis H Harlow and J Eddie Welch . Numerical calculation of time-dependent viscous incompressible flow of fluid with free surface. The physics of fluids, 8(12):2182--2189 , 1965 . Francis H Harlow and J Eddie Welch. Numerical calculation of time-dependent viscous incompressible flow of fluid with free surface. The physics of fluids, 8(12):2182--2189, 1965."},{"key":"e_1_3_2_1_16_1","volume-title":"Applied partial differential equations: an introduction","author":"Jeffrey Alan","year":"2003","unstructured":"Alan Jeffrey . Applied partial differential equations: an introduction . Academic Press , 2003 . Alan Jeffrey. Applied partial differential equations: an introduction. Academic Press, 2003."},{"key":"e_1_3_2_1_17_1","volume-title":"Journal of computational physics, 207(1):1--27","author":"Zhaosheng Yu. A","year":"2005","unstructured":"Zhaosheng Yu. A DLM\/FD method for fluid\/flexible-body interactions. Journal of computational physics, 207(1):1--27 , 2005 . Zhaosheng Yu. A DLM\/FD method for fluid\/flexible-body interactions. Journal of computational physics, 207(1):1--27, 2005."},{"key":"e_1_3_2_1_18_1","volume-title":"Flux vector splitting of the inviscid gasdynamic equations with application to finite-difference methods. Journal of computational physics, 40(2):263--293","author":"Steger Joseph L","year":"1981","unstructured":"Joseph L Steger and RF Warming . Flux vector splitting of the inviscid gasdynamic equations with application to finite-difference methods. Journal of computational physics, 40(2):263--293 , 1981 . Joseph L Steger and RF Warming. Flux vector splitting of the inviscid gasdynamic equations with application to finite-difference methods. Journal of computational physics, 40(2):263--293, 1981."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-65108-3_2"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1968-0242392-2"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/1921427.1921438"},{"key":"e_1_3_2_1_22_1","first-page":"31","volume-title":"Graphics Interface","volume":"2003","author":"G\u00e9nevaux Olivier","year":"2003","unstructured":"Olivier G\u00e9nevaux , Arash Habibi , and Jean-Michel Dischler . Simulating fluid-solid interaction . In Graphics Interface , volume 2003 , pages 31 -- 38 , 2003 . Olivier G\u00e9nevaux, Arash Habibi, and Jean-Michel Dischler. Simulating fluid-solid interaction. In Graphics Interface, volume 2003, pages 31--38, 2003."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383260"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806777.2806945"},{"key":"e_1_3_2_1_25_1","volume-title":"Proc. VLDB Endow., 11(5)","author":"Li Tian","year":"2018","unstructured":"Tian Li , Jie Zhong , Ji Liu , Wentao Wu , and Ce Zhang . Ease.ml : Towards multi-tenant resource sharing for machine learning workloads . Proc. VLDB Endow., 11(5) , 2018 . Tian Li, Jie Zhong, Ji Liu, Wentao Wu, and Ce Zhang. Ease.ml: Towards multi-tenant resource sharing for machine learning workloads. Proc. VLDB Endow., 11(5), 2018."},{"key":"e_1_3_2_1_26_1","first-page":"1097","volume-title":"Advances in neural information processing systems","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . Imagenet classification with deep convolutional neural networks . In Advances in neural information processing systems , pages 1097 -- 1105 , 2012 . Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012."},{"key":"e_1_3_2_1_27_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 , 2014 . Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0048216"},{"key":"e_1_3_2_1_29_1","volume-title":"analyzing, and using data on test items. Educational measurement, 2","author":"Henrysson Sten","year":"1971","unstructured":"Sten Henrysson . Gathering , analyzing, and using data on test items. Educational measurement, 2 , 1971 . Sten Henrysson. Gathering, analyzing, and using data on test items. Educational measurement, 2, 1971."},{"key":"e_1_3_2_1_30_1","series-title":"Series C (Applied Statistics), 24(3):377--379","volume-title":"Algorithm as 89: the upper tail probabilities of spearman's rho. Journal of the Royal Statistical Society","author":"Best DJ","year":"1975","unstructured":"DJ Best and DE Roberts . Algorithm as 89: the upper tail probabilities of spearman's rho. Journal of the Royal Statistical Society . Series C (Applied Statistics), 24(3):377--379 , 1975 . DJ Best and DE Roberts. Algorithm as 89: the upper tail probabilities of spearman's rho. Journal of the Royal Statistical Society. Series C (Applied Statistics), 24(3):377--379, 1975."},{"key":"e_1_3_2_1_32_1","first-page":"50","volume-title":"ACM Transactions on Graphics (TOG)","author":"Kim Theodore","unstructured":"Theodore Kim , Nils Th\u00fcrey , Doug James , and Markus Gross . Wavelet turbulence for fluid simulation . In ACM Transactions on Graphics (TOG) , volume 27 , page 50 . ACM, 2008. Theodore Kim, Nils Th\u00fcrey, Doug James, and Markus Gross. Wavelet turbulence for fluid simulation. In ACM Transactions on Graphics (TOG), volume 27, page 50. ACM, 2008."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2005.10.009"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1080\/10691898.2011.11889610"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cryogenics.2014.03.003"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cryogenics.2015.04.004"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2018.00068"},{"key":"e_1_3_2_1_38_1","volume-title":"Eurographics\/ ACM SIGGRAPH Symposium on Computer Animation. The Eurographics Association","author":"McAdams Aleka","year":"2010","unstructured":"Aleka McAdams , Eftychios Sifakis , and Joseph Teran . A Parallel Multigrid Poisson Solver for Fluids Simulation on Large Grids. In MZoran Popovic and Miguel Otaduy, editors , Eurographics\/ ACM SIGGRAPH Symposium on Computer Animation. The Eurographics Association , 2010 . Aleka McAdams, Eftychios Sifakis, and Joseph Teran. A Parallel Multigrid Poisson Solver for Fluids Simulation on Large Grids. In MZoran Popovic and Miguel Otaduy, editors, Eurographics\/ ACM SIGGRAPH Symposium on Computer Animation. The Eurographics Association, 2010."},{"key":"e_1_3_2_1_39_1","first-page":"114","volume-title":"ACM Transactions on Graphics (TOG)","author":"Lentine Michael","unstructured":"Michael Lentine , Wen Zheng , and Ronald Fedkiw . A novel algorithm for incompressible flow using only a coarse grid projection . In ACM Transactions on Graphics (TOG) , volume 29 , page 114 . ACM, 2010. Michael Lentine, Wen Zheng, and Ronald Fedkiw. A novel algorithm for incompressible flow using only a coarse grid projection. In ACM Transactions on Graphics (TOG), volume 29, page 114. ACM, 2010."},{"key":"e_1_3_2_1_40_1","first-page":"9","volume-title":"Low viscosity flow simulations for animation","author":"Molemaker Jeroen","year":"2008","unstructured":"Jeroen Molemaker , Jonathan M. Cohen , Sanjit Patel , and Jonyong Noh . Low viscosity flow simulations for animation . pages 9 -- 18 , 01 2008 . Jeroen Molemaker, Jonathan M. Cohen, Sanjit Patel, and Jonyong Noh. Low viscosity flow simulations for animation. pages 9--18, 01 2008."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2077341.2077351"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045437"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1175\/JAM2173.1"},{"key":"e_1_3_2_1_44_1","volume-title":"A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282","author":"Cheng Yu","year":"2017","unstructured":"Yu Cheng , Duo Wang , Pan Zhou , and Tao Zhang . A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282 , 2017 . Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282, 2017."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_2_1_46_1","volume-title":"Training and inference with integers in deep neural networks. arXiv preprint arXiv:1802.04680","author":"Wu Shuang","year":"2018","unstructured":"Shuang Wu , Guoqi Li , Feng Chen , and Luping Shi . Training and inference with integers in deep neural networks. arXiv preprint arXiv:1802.04680 , 2018 . Shuang Wu, Guoqi Li, Feng Chen, and Luping Shi. Training and inference with integers in deep neural networks. arXiv preprint arXiv:1802.04680, 2018."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.643"},{"key":"e_1_3_2_1_48_1","volume-title":"Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440","author":"Molchanov Pavlo","year":"2016","unstructured":"Pavlo Molchanov , Stephen Tyree , Tero Karras , Timo Aila , and Jan Kautz . Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440 , 2016 . Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440, 2016."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"e_1_3_2_1_50_1","volume-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830","author":"Courbariaux Matthieu","year":"2016","unstructured":"Matthieu Courbariaux , Itay Hubara , Daniel Soudry , Ran El-Yaniv , and Yoshua Bengio . Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 , 2016 . Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830, 2016."},{"key":"e_1_3_2_1_51_1","volume-title":"Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067","author":"Tai Cheng","year":"2015","unstructured":"Cheng Tai , Tong Xiao , Yi Zhang , Xiaogang Wang , Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067 , 2015 . Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, et al. Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067, 2015."},{"key":"e_1_3_2_1_52_1","volume-title":"Deep model compression: Distilling knowledge from noisy teachers. arXiv preprint arXiv:1610.09650","author":"Sau Bharat Bhusan","year":"2016","unstructured":"Bharat Bhusan Sau and Vineeth N Balasubramanian . Deep model compression: Distilling knowledge from noisy teachers. arXiv preprint arXiv:1610.09650 , 2016 . Bharat Bhusan Sau and Vineeth N Balasubramanian. Deep model compression: Distilling knowledge from noisy teachers. arXiv preprint arXiv:1610.09650, 2016."},{"key":"e_1_3_2_1_53_1","volume-title":"Data-free knowledge distillation for deep neural networks. arXiv preprint arXiv:1710.07535","author":"Lopes Raphael Gontijo","year":"2017","unstructured":"Raphael Gontijo Lopes , Stefano Fenu , and Thad Starner . Data-free knowledge distillation for deep neural networks. arXiv preprint arXiv:1710.07535 , 2017 . Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner. Data-free knowledge distillation for deep neural networks. arXiv preprint arXiv:1710.07535, 2017."}],"event":{"name":"SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","IEEE CS"],"location":"Denver Colorado","acronym":"SC '19"},"container-title":["Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3295500.3356147","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3295500.3356147","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3295500.3356147","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:13Z","timestamp":1750208533000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3295500.3356147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,17]]},"references-count":52,"alternative-id":["10.1145\/3295500.3356147","10.1145\/3295500"],"URL":"https:\/\/doi.org\/10.1145\/3295500.3356147","relation":{},"subject":[],"published":{"date-parts":[[2019,11,17]]},"assertion":[{"value":"2019-11-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}