{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:55:44Z","timestamp":1777568144154,"version":"3.51.4"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030905385","type":"print"},{"value":"9783030905392","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-90539-2_3","type":"book-chapter","created":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T13:02:56Z","timestamp":1636722176000},"page":"40-55","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Lettuce: PyTorch-Based Lattice Boltzmann Framework"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9281-2027","authenticated-orcid":false,"given":"Mario Christopher","family":"Bedrunka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3263-7287","authenticated-orcid":false,"given":"Dominik","family":"Wilde","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Kliemank","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1480-6745","authenticated-orcid":false,"given":"Dirk","family":"Reith","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2056-6960","authenticated-orcid":false,"given":"Holger","family":"Foysi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7699-3083","authenticated-orcid":false,"given":"Andreas","family":"Kr\u00e4mer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.camwa.2020.01.007","volume":"81","author":"M Bauer","year":"2021","unstructured":"Bauer, M., et al.: waLBerla: a block-structured high-performance framework for multiphysics simulations. Comput. Math. with Appl. 81, 478\u2013501 (2021)","journal-title":"Comput. Math. with Appl."},{"issue":"3","key":"3_CR2","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1103\/PhysRev.94.511","volume":"94","author":"PL Bhatnagar","year":"1954","unstructured":"Bhatnagar, P.L., Gross, E.P., Krook, M.: A model for collision processes in gases. I. Small amplitude processes in charged and neutral one-component systems. Phys. Rev. 94(3), 511\u2013525 (1954)","journal-title":"Phys. Rev."},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1017\/S0022112083001159","volume":"130","author":"ME Brachet","year":"1983","unstructured":"Brachet, M.E., Meiron, D.I., Orszag, S.A., Nickel, B., Morf, R.H., Frisch, U.: Small-scale structure of the Taylor-green vortex. J. Fluid Mech. 130, 411\u2013452 (1983)","journal-title":"J. Fluid Mech."},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1006\/jcph.1995.1205","volume":"122","author":"DL Brown","year":"1995","unstructured":"Brown, D.L.: Performance of under-resolved two-dimensional incompressible flow simulations. J. Comput. Phys. 122(1), 165\u2013183 (1995)","journal-title":"J. Comput. Phys."},{"issue":"12","key":"3_CR5","doi-asserted-by":"publisher","first-page":"125104","DOI":"10.1063\/5.0029424","volume":"32","author":"T Chen","year":"2020","unstructured":"Chen, T., Wen, X., Wang, L.P., Guo, Z., Wang, J., Chen, S.: Simulation of three-dimensional compressible decaying isotropic turbulence using a redesigned discrete unified gas kinetic scheme. Phys. Fluids 32(12), 125104 (2020)","journal-title":"Phys. Fluids"},{"issue":"11","key":"3_CR6","doi-asserted-by":"publisher","first-page":"116102","DOI":"10.1063\/5.0027986","volume":"32","author":"C Coreixas","year":"2020","unstructured":"Coreixas, C., Latt, J.: Compressible lattice Boltzmann methods with adaptive velocity stencils: an interpolation-free formulation. Phys. Fluids 32(11), 116102 (2020)","journal-title":"Phys. Fluids"},{"issue":"2","key":"3_CR7","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/S0021-9991(03)00279-1","volume":"190","author":"PJ Dellar","year":"2003","unstructured":"Dellar, P.J.: Incompressible limits of lattice Boltzmann equations using multiple relaxation times. J. Comput. Phys. 190(2), 351\u2013370 (2003)","journal-title":"J. Comput. Phys."},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s41403-020-00106-w","volume":"5","author":"SS Diwan","year":"2020","unstructured":"Diwan, S.S., Ravichandran, S., Govindarajan, R., Narasimha, R.: Understanding transmission dynamics of Covid-19-type infections by direct numerical simulations of cough\/sneeze flows. Trans. Indian Natl. Acad. Eng. 5, 255\u2013261 (2020)","journal-title":"Trans. Indian Natl. Acad. Eng."},{"issue":"3","key":"3_CR9","doi-asserted-by":"publisher","first-page":"035122","DOI":"10.1063\/5.0042086","volume":"33","author":"A Fabregat","year":"2021","unstructured":"Fabregat, A., Gisbert, F., Vernet, A., Dutta, S., Mittal, K., Pallar\u00e8s, J.: Direct numerical simulation of the turbulent flow generated during a violent expiratory event. Phys. Fluids 33(3), 035122 (2021)","journal-title":"Phys. Fluids"},{"key":"3_CR10","doi-asserted-by":"publisher","first-page":"110199","DOI":"10.1016\/j.jcp.2021.110199","volume":"434","author":"B Font","year":"2021","unstructured":"Font, B., Weymouth, G.D., Nguyen, V.T., Tutty, O.R.: Deep learning of the spanwise-averaged Navier-Stokes equations. J. Comput. Phys. 434, 110199 (2021)","journal-title":"J. Comput. Phys."},{"issue":"6","key":"3_CR11","doi-asserted-by":"publisher","first-page":"061301","DOI":"10.1103\/PhysRevE.92.061301","volume":"92","author":"N Frapolli","year":"2015","unstructured":"Frapolli, N., Chikatamarla, S.S., Karlin, I.V.: Entropic lattice Boltzmann model for compressible flows. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 92(6), 061301 (2015)","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.jcp.2017.05.040","volume":"348","author":"M Geier","year":"2017","unstructured":"Geier, M., Pasquali, A., Sch\u00f6nherr, M.: Parametrization of the cumulant lattice Boltzmann method for fourth order accurate diffusion. Part I: Derivation and validation. J. Comput. Phys. 348, 862\u2013888 (2017)","journal-title":"J. Comput. Phys."},{"issue":"2","key":"3_CR13","first-page":"427","volume":"3","author":"I Ginzburg","year":"2008","unstructured":"Ginzburg, I., Verhaeghe, F., D\u2019Humi\u00e8res, D.: Two-relaxation-time Lattice Boltzmann scheme: about parametrization, velocity, pressure and mixed boundary conditions. Commun. Comput. Phys. 3(2), 427\u2013478 (2008)","journal-title":"Commun. Comput. Phys."},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Godenschwager, C., Schornbaum, F., Bauer, M., K\u00f6stler, H., R\u00fcde, U.: A framework for hybrid parallel flow simulations with a trillion cells in complex geometries. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis - SC 2013, New York, NY, USA, pp. 1\u201312. ACM Press (2013)","DOI":"10.1145\/2503210.2503273"},{"key":"3_CR15","unstructured":"Hennigh, O.: Lat-Net: compressing lattice Boltzmann flow simulations using deep neural networks. arXiv preprint arXiv:1705.09036 (2017)"},{"key":"3_CR16","unstructured":"Herrera, P.: pyevtk 1.2.0. PyPI (2021). https:\/\/pypi.org\/project\/pyevtk\/"},{"key":"3_CR17","unstructured":"Heuveline, V., Krause, M.J.: OpenLB: towards an efficient parallel open source library for lattice Boltzmann fluid flow simulations. In: International Workshop on State-of-the-Art in Scientific and Parallel Computing, PARA, vol. 9 (2010)"},{"issue":"3","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1103\/PhysRevE.90.031302","volume":"90","author":"IV Karlin","year":"2014","unstructured":"Karlin, I.V., B\u00f6sch, F., Chikatamarla, S.: Gibbs\u2019 principle for the lattice-kinetic theory of fluid dynamics. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 90(3), 1\u20135 (2014)","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Kochkov, D., Smith, J.A., Alieva, A., Wang, Q., Brenner, M.P., Hoyer, S.: Machine learning accelerated computational fluid dynamics. Proc. Nat. Acad. Sci. 118(21), e2101784118 (2021).","DOI":"10.1073\/pnas.2101784118"},{"issue":"2","key":"3_CR20","doi-asserted-by":"publisher","first-page":"023302","DOI":"10.1103\/PhysRevE.100.023302","volume":"100","author":"A Kr\u00e4mer","year":"2019","unstructured":"Kr\u00e4mer, A., Wilde, D., K\u00fcllmer, K., Reith, D., Foysi, H.: Pseudoentropic derivation of the regularized lattice Boltzmann method. Phys. Rev. E 100(2), 023302 (2019)","journal-title":"Phys. Rev. E"},{"key":"3_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-44649-3","volume-title":"The Lattice Boltzmann Method: Principles and Practice","author":"T Kr\u00fcger","year":"2017","unstructured":"Kr\u00fcger, T., Kusumaatmaja, H., Kuzmin, A., Shardt, O., Silva, G., Viggen, E.M.: The Lattice Boltzmann Method: Principles and Practice. Springer, Heidelberg (2017)"},{"issue":"6","key":"3_CR22","first-page":"6546","volume":"61","author":"P Lallemand","year":"2000","unstructured":"Lallemand, P., Luo, L.S.: Theory of the lattice Boltzmann method: dispersion, dissipation, isotropy, Galilean invariance, and stability. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 61(6), 6546\u20136562 (2000)","journal-title":"Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top."},{"issue":"2\u20136","key":"3_CR23","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.matcom.2006.05.017","volume":"72","author":"J Latt","year":"2006","unstructured":"Latt, J., Chopard, B.: Lattice Boltzmann method with regularized pre-collision distribution functions. Math. Comput. Simul. 72(2\u20136), 165\u2013168 (2006)","journal-title":"Math. Comput. Simul."},{"issue":"2175","key":"3_CR24","doi-asserted-by":"publisher","first-page":"20190559","DOI":"10.1098\/rsta.2019.0559","volume":"378","author":"J Latt","year":"2020","unstructured":"Latt, J., Coreixas, C., Beny, J., Parmigiani, A.: Efficient supersonic flow simulations using lattice Boltzmann methods based on numerical equilibria. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 378(2175), 20190559 (2020)","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"3_CR25","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.camwa.2020.03.022","volume":"81","author":"J Latt","year":"2021","unstructured":"Latt, J., et al.: Palabos: parallel lattice Boltzmann solver. Comput. Math. Appl. 81, 334\u2013350 (2021)","journal-title":"Comput. Math. Appl."},{"key":"3_CR26","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.jweia.2019.03.012","volume":"189","author":"S Lenz","year":"2019","unstructured":"Lenz, S., et al.: Towards real-time simulation of turbulent air flow over a resolved urban canopy using the cumulant lattice Boltzmann method on a GPGPU. J. Wind Eng. Ind. Aerodyn. 189, 151\u2013162 (2019)","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"issue":"20","key":"3_CR27","doi-asserted-by":"publisher","first-page":"2332","DOI":"10.1103\/PhysRevLett.61.2332","volume":"61","author":"GR McNamara","year":"1988","unstructured":"McNamara, G.R., Zanetti, G.: Use of the Boltzmann equation to simulate lattice-gas automata. Phys. Rev. Lett. 61(20), 2332\u20132335 (1988)","journal-title":"Phys. Rev. Lett."},{"issue":"1","key":"3_CR28","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1093\/gji\/ggz423","volume":"220","author":"P Mora","year":"2020","unstructured":"Mora, P., Morra, G., Yuen, D.A.: A concise python implementation of the lattice Boltzmann method on HPC for geo-fluid flow. Geophys. J. Int. 220(1), 682\u2013702 (2020)","journal-title":"Geophys. J. Int."},{"issue":"6","key":"3_CR29","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.parco.2013.04.001","volume":"39","author":"C Obrecht","year":"2013","unstructured":"Obrecht, C., Kuznik, F., Tourancheau, B., Roux, J.J.: Scalable lattice Boltzmann solvers for CUDA GPU clusters. Parallel Comput. 39(6), 259\u2013270 (2013)","journal-title":"Parallel Comput."},{"key":"3_CR30","unstructured":"Pastewka, L., Greiner, A.: HPC with python: an MPI-parallel implementation of the lattice Boltzmann method. In: Proceedings of the 5th bwHPC Symposium (2019)"},{"key":"3_CR31","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., D\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024\u20138035. Curran Associates, Inc. (2019)"},{"issue":"1","key":"3_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10546-019-00473-0","volume":"174","author":"F Port\u00e9-Agel","year":"2020","unstructured":"Port\u00e9-Agel, F., Bastankhah, M., Shamsoddin, S.: Wind-turbine and wind-farm flows: a review. Boundary Layer Meteorol. 174(1), 1\u201359 (2020)","journal-title":"Boundary Layer Meteorol."},{"key":"3_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/978-3-030-59851-8_6","volume-title":"High Performance Computing","author":"M R\u00fcttgers","year":"2020","unstructured":"R\u00fcttgers, M., Koh, S.-R., Jitsev, J., Schr\u00f6der, W., Lintermann, A.: Prediction of acoustic fields using a lattice-Boltzmann method and deep learning. In: Jagode, H., Anzt, H., Juckeland, G., Ltaief, H. (eds.) ISC High Performance 2020. LNCS, vol. 12321, pp. 81\u2013101. Springer, Cham (2020)"},{"issue":"4","key":"3_CR34","doi-asserted-by":"publisher","first-page":"046104","DOI":"10.1063\/5.0048029","volume":"33","author":"MH Saadat","year":"2021","unstructured":"Saadat, M.H., Hosseini, S.A., Dorschner, B., Karlin, I.V.: Extended lattice Boltzmann model for gas dynamics. Phys. Fluids 33(4), 046104 (2021)","journal-title":"Phys. Fluids"},{"issue":"1","key":"3_CR35","doi-asserted-by":"publisher","first-page":"013306","DOI":"10.1103\/PhysRevE.99.013306","volume":"99","author":"MH Saadat","year":"2019","unstructured":"Saadat, M.H., B\u00f6sch, F., Karlin, I.V.: Lattice Boltzmann model for compressible flows on standard lattices: variable Prandtl number and adiabatic exponent. Phys. Rev. E 99(1), 013306 (2019)","journal-title":"Phys. Rev. E"},{"key":"3_CR36","volume-title":"Large Eddy Simulation for Incompressible Flows. An Introduction","author":"P Sagaut","year":"2006","unstructured":"Sagaut, P.: Large Eddy Simulation for Incompressible Flows. An Introduction. Springer, Heidelberg (2006)"},{"issue":"5","key":"3_CR37","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1063\/1.1355682","volume":"13","author":"R Samtaney","year":"2001","unstructured":"Samtaney, R., Pullin, D.I., Kosovi\u0107, B.: Direct numerical simulation of decaying compressible turbulence and shocklet statistics. Phys. Fluids 13(5), 1415\u20131430 (2001)","journal-title":"Phys. Fluids"},{"key":"3_CR38","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.cpc.2017.03.013","volume":"217","author":"S Schmieschek","year":"2017","unstructured":"Schmieschek, S., et al.: LB3D: a parallel implementation of the lattice-Boltzmann method for simulation of interacting amphiphilic fluids. Comput. Phys. Commun. 217, 149\u2013161 (2017)","journal-title":"Comput. Phys. Commun."},{"key":"3_CR39","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1146\/annurev-fluid-010719-060214","volume":"52","author":"SL Brunton","year":"2020","unstructured":"Brunton, S.L., Noack, B.R., Koumoutsakos, P.: Machine learning for fluid mechanics. Annu. Rev. Fluid Mech. 52, 477\u2013508 (2020)","journal-title":"Annu. Rev. Fluid Mech."},{"key":"3_CR40","unstructured":"Um, K., Fei, Y.R., Holl, P., Brand, R., Thuerey, N.: Solver-in-the-loop: learning from differentiable physics to interact with iterative PDE-solvers. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, vol. 1, no. c, pp. 1\u201337 (2020)"},{"key":"3_CR41","doi-asserted-by":"crossref","unstructured":"Wichmann, K.R., Kronbichler, M., L\u00f6hner, R., Wall, W.A.: A runtime based comparison of highly tuned lattice Boltzmann and finite difference solvers. Int. J. High Perform. Comput. Appl. (2021).","DOI":"10.1177\/10943420211006169"},{"key":"3_CR42","unstructured":"Wilcox, D.C.: Turbulence Modeling for CFD. DCW Industries, CA (1993)"},{"key":"3_CR43","doi-asserted-by":"publisher","first-page":"101355","DOI":"10.1016\/j.jocs.2021.101355","volume":"51","author":"D Wilde","year":"2021","unstructured":"Wilde, D., Kr\u00e4mer, A., Bedrunka, M., Reith, D., Foysi, H.: Cubature rules for weakly and fully compressible off-lattice Boltzmann methods. J. Comput. Sci. 51, 101355 (2021)","journal-title":"J. Comput. Sci."},{"issue":"5","key":"3_CR44","doi-asserted-by":"publisher","first-page":"53306","DOI":"10.1103\/PhysRevE.101.053306","volume":"101","author":"D Wilde","year":"2020","unstructured":"Wilde, D., Kr\u00e4mer, A., Reith, D., Foysi, H.: Semi-Lagrangian lattice Boltzmann method for compressible flows. Phys. Rev. E 101(5), 53306 (2020)","journal-title":"Phys. Rev. E"}],"container-title":["Lecture Notes in Computer Science","High Performance Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-90539-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T10:03:28Z","timestamp":1655892208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-90539-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030905385","9783030905392"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-90539-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"13 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISC High Performance","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on High Performance Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"36","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"supercomputing2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isc-hpc.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Linklings","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"74","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.28","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.13","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"In the ISC High Performance Workshop, there were 49 submissions, out of which 35  were accepted.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}