{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:13:12Z","timestamp":1757542392505,"version":"3.37.3"},"reference-count":78,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1955764","2112606"],"award-info":[{"award-number":["1955764","2112606"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["47145"],"award-info":[{"award-number":["47145"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UT-Battelle LLC","award":["4000159447","17-SC-20-SC"],"award-info":[{"award-number":["4000159447","17-SC-20-SC"]}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006168","name":"National Nuclear Security Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006168","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006168","name":"National Nuclear Security Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006168","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006224","name":"Argonne National Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006224","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Visual. Comput. Graphics"],"published-print":{"date-parts":[[2023,6,1]]},"DOI":"10.1109\/tvcg.2022.3148745","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T20:56:02Z","timestamp":1644267362000},"page":"3052-3066","source":"Crossref","is-referenced-by-count":7,"title":["Reinforcement Learning for Load-Balanced Parallel Particle Tracing"],"prefix":"10.1109","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9091-6412","authenticated-orcid":false,"given":"Jiayi","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7776-1834","authenticated-orcid":false,"given":"Hanqi","family":"Guo","sequence":"additional","affiliation":[{"name":"Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1211-2320","authenticated-orcid":false,"given":"Han-Wei","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA"}]},{"given":"Mukund","family":"Raj","sequence":"additional","affiliation":[{"name":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6685-615X","authenticated-orcid":false,"given":"Skylar W.","family":"Wurster","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA"}]},{"given":"Tom","family":"Peterka","sequence":"additional","affiliation":[{"name":"Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA"}]}],"member":"263","reference":[{"journal-title":"Reinforcement Learning An Introduction","year":"2018","author":"sutton","key":"ref13"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2015.2499244"},{"journal-title":"Computers and Intractability A Guide to the Theory of NP-Completeness","year":"1979","author":"gary","key":"ref12"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2010.04.016"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.1206"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/125\/1\/012076"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8191(01)00098-9"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1985.tb01383.x"},{"article-title":"On the theory of policy gradient methods: Optimality, approximation, and distribution shift","year":"2019","author":"agarwal","key":"ref53"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/800119.803884"},{"key":"ref55","first-page":"6820","article-title":"On the global convergence rates of softmax policy gradient methods","author":"mei","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2011.62"},{"key":"ref54","first-page":"64","article-title":"Optimality and approximation with policy gradient methods in Markov decision processes","author":"agarwal","year":"2020","journal-title":"Proc Conf Learn Theory"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/B978-044482322-9\/50093-1"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2019.2899843"},{"key":"ref19","first-page":"87","article-title":"Optimizing parallel performance of streamline visualization for large distributed flow datasets","author":"chen","year":"2008","journal-title":"Proc Pacific Vis Symp"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1362622.1362655"},{"article-title":"Model-based reinforcement learning: A survey","year":"2020","author":"moerland","key":"ref51"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICSTCC50638.2020.9259716"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/VISUAL.2004.107"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/S0097-8493(02)00056-0"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-017-0468-y"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.1997.606886"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/LDAV.2012.6378984"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/LDAV.2011.6092326"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/PacificVis.2018.00018"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/PACIFICVIS.2016.7465254"},{"article-title":"Model-based reinforcement learning for atari","year":"2019","author":"kaiser","key":"ref49"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2014.2346418"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2063384.2063397"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/1654059.1654076"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2012.93"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-2789(00)00199-8"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2014.87"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744059"},{"key":"ref40","article-title":"Flow Web: A graph based user interface for 3D flow field exploration","volume":"7530","author":"xu","year":"2010","journal-title":"Vis Data Anal"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s12650-017-0470-2"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1201\/b12985-8"},{"article-title":"Asynchronous methods for deep reinforcement learning","year":"2016","author":"mnih","key":"ref78"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/324133.324234"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TC.1987.1676942"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2010.259"},{"article-title":"Adam: A method for stochastic optimization","year":"2014","author":"kingma","key":"ref75"},{"key":"ref30","first-page":"802","article-title":"Highly parallel vector visualization using line integral convolution","author":"cabral","year":"1995","journal-title":"Proc SIAM Conf Parallel Process Sci Comput"},{"key":"ref74","first-page":"26","article-title":"Lecture 6.5&#x2014;RmsProp: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"tieleman","year":"2012","journal-title":"COURSERA Neural Netw Mach Learn"},{"key":"ref33","first-page":"7","article-title":"Machine learning-based autotuning for parallel particle advection","author":"schwartz","year":"2021","journal-title":"Proc Eurograph Symp Parallel Graph Vis"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref32","first-page":"45","article-title":"Performance-Portable Particle Advection with VTK-m","author":"pugmire","year":"2018","journal-title":"Proc Eurograph Symp Parallel Graph Vis"},{"article-title":"Playing atari with deep reinforcement learning","year":"2013","author":"mnih","key":"ref76"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/PVGS.2003.1249047"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/166117.166151"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2038037.1941582"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/1654059.1654113"},{"key":"ref71","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc 33rd Int Conf Neural Informat Process Syst"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/MCG.2011.102"},{"key":"ref73","first-page":"318","article-title":"Learning internal representations by error propagation","author":"rumelhart","year":"1987","journal-title":"Parallel Distributed Processing Explorations in the Microstructure of Cognition Foundations"},{"key":"ref72","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"2017","journal-title":"Proc Neural Informat Process Syst Autodiff Workshop"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/PacificVis.2018.00019"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/LDAV.2016.7874307"},{"key":"ref23","first-page":"33","article-title":"Scalable Lagrangian-based attribute space projection for multivariate unsteady flow data","author":"guo","year":"2014","journal-title":"Proc IEEE Pacific Vis Symp"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/LDAV.2011.6092324"},{"key":"ref26","first-page":"1","article-title":"GPU acceleration of particle advection workloads in a parallel, distributed memory setting","author":"camp","year":"2013","journal-title":"Proc Eurograph Symp Parallel Graph Vis"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/LDAV48142.2019.8944355"},{"article-title":"DIY: data-parallel out-of-core library","year":"0","author":"morozov","key":"ref69"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2011.219"},{"journal-title":"The C Programming Language","year":"1988","author":"ritchie","key":"ref64"},{"journal-title":"Python Reference Manual","year":"2009","author":"van rossum","key":"ref63"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1109\/TVCG.2013.144","article-title":"Coupled ensemble flow line advection and analysis","volume":"19","author":"guo","year":"2013","journal-title":"IEEE Trans Vis Comput Graphics"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2005.03.010"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/LDAV.2013.6675152"},{"key":"ref65","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MCSE.2010.118","article-title":"Cython: The best of both worlds","volume":"13","author":"behnel","year":"2010","journal-title":"Comput Sci Eng"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/VISUAL.1994.346311"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/HiPC.2014.7116900"},{"article-title":"Parallelizing a particle tracer for flow visualization","year":"1995","author":"lane","key":"ref29"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.ocemod.2003.12.001"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1080\/14685240802376389"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1017\/jfm.2012.5"}],"container-title":["IEEE Transactions on Visualization and Computer Graphics"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/2945\/10115225\/9706326-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/2945\/10115225\/09706326.pdf?arnumber=9706326","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T17:50:10Z","timestamp":1684777810000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9706326\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":78,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tvcg.2022.3148745","relation":{},"ISSN":["1077-2626","1941-0506","2160-9306"],"issn-type":[{"type":"print","value":"1077-2626"},{"type":"electronic","value":"1941-0506"},{"type":"electronic","value":"2160-9306"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}