{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:15:22Z","timestamp":1760890522883,"version":"3.37.3"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["304876\/2021-6"],"award-info":[{"award-number":["304876\/2021-6"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"publisher","award":["E-26\/200.880\/2021"],"award-info":[{"award-number":["E-26\/200.880\/2021"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004225","name":"Petrobras","doi-asserted-by":"publisher","award":["2022\/00124-0"],"award-info":[{"award-number":["2022\/00124-0"]}],"id":[{"id":"10.13039\/501100004225","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004901","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais","doi-asserted-by":"publisher","award":["APQ-01123-21"],"award-info":[{"award-number":["APQ-01123-21"]}],"id":[{"id":"10.13039\/501100004901","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00366-023-01805-y","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T12:03:39Z","timestamp":1678795419000},"page":"455-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing dynamic mode decomposition workflow with in situ visualization and data compression"],"prefix":"10.1007","volume":"40","author":[{"given":"Gabriel F.","family":"Barros","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mal\u00fa","family":"Grave","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 J.","family":"Camata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4764-1142","authenticated-orcid":false,"given":"Alvaro L. G. A.","family":"Coutinho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"1805_CR1","unstructured":"Adelsberger J, Esser P, Griebel M et al (2014) 3D incompressible two-phase flow benchmark computations for rising droplets. In: Proceedings of the 11th world congress on computational mechanics (WCCM XI), Barcelona, Spain"},{"issue":"1","key":"1805_CR2","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s11831-015-9161-0","volume":"24","author":"N Ahmed","year":"2017","unstructured":"Ahmed N, Rebollo TC, John V et al (2017) A review of variational multiscale methods for the simulation of turbulent incompressible flows. Arch Comput Methods Eng 24(1):115\u2013164","journal-title":"Arch Comput Methods Eng"},{"key":"1805_CR3","doi-asserted-by":"publisher","unstructured":"Ahrens J, Geveci B, Law C (2005) ParaView: an end-user tool for large-data visualization. In: Hansen CD, Johnson CR (eds) Visualization handbook. Butterworth-Heinemann, Burlington, pp 717\u2013731. https:\/\/doi.org\/10.1016\/B978-012387582-2\/50038-1","DOI":"10.1016\/B978-012387582-2\/50038-1"},{"issue":"5","key":"1805_CR4","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s00791-018-00303-9","volume":"19","author":"M Ainsworth","year":"2018","unstructured":"Ainsworth M, Tugluk O, Whitney B et al (2018) Multilevel techniques for compression and reduction of scientific data-the univariate case. Comput Vis Sci 19(5):65\u201376","journal-title":"Comput Vis Sci"},{"issue":"2","key":"1805_CR5","doi-asserted-by":"publisher","first-page":"A1278","DOI":"10.1137\/18M1166651","volume":"41","author":"M Ainsworth","year":"2019","unstructured":"Ainsworth M, Tugluk O, Whitney B et al (2019) Multilevel techniques for compression and reduction of scientific data\u2014the multivariate case. SIAM J Sci Comput 41(2):A1278\u2013A1303","journal-title":"SIAM J Sci Comput"},{"issue":"4","key":"1805_CR6","doi-asserted-by":"publisher","first-page":"A2146","DOI":"10.1137\/18M1208885","volume":"41","author":"M Ainsworth","year":"2019","unstructured":"Ainsworth M, Tugluk O, Whitney B et al (2019) Multilevel techniques for compression and reduction of scientific data-quantitative control of accuracy in derived quantities. SIAM J Sci Comput 41(4):A2146\u2013A2171","journal-title":"SIAM J Sci Comput"},{"issue":"2","key":"1805_CR7","doi-asserted-by":"publisher","first-page":"A1402","DOI":"10.1137\/19M1267878","volume":"42","author":"M Ainsworth","year":"2020","unstructured":"Ainsworth M, Tugluk O, Whitney B et al (2020) Multilevel techniques for compression and reduction of scientific data\u2014the unstructured case. SIAM J Sci Comput 42(2):A1402\u2013A1427","journal-title":"SIAM J Sci Comput"},{"key":"1805_CR8","doi-asserted-by":"publisher","unstructured":"Alfatlawi M, Srivastava V (2020) An incremental approach to online dynamic mode decomposition for time-varying systems with applications to eeg data modeling. J Comput Dyn 7(2):209\u2013241. https:\/\/doi.org\/10.3934\/JCD.2020009. arXiv:1908.01047","DOI":"10.3934\/JCD.2020009"},{"issue":"111","key":"1805_CR9","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.jcp.2022.111849","volume":"475","author":"C Amor","year":"2023","unstructured":"Amor C, Schlatter P, Vinuesa R et al (2023) Higher-order dynamic mode decomposition on-the-fly: a low-order algorithm for complex fluid flows. J Comput Phys 475(111):849. https:\/\/doi.org\/10.1016\/j.jcp.2022.111849","journal-title":"J Comput Phys"},{"issue":"6","key":"1805_CR10","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s10043-009-0119-z","volume":"16","author":"AN Avanaki","year":"2009","unstructured":"Avanaki AN (2009) Exact global histogram specification optimized for structural similarity. Opt Rev 16(6):613\u2013621. https:\/\/doi.org\/10.1007\/s10043-009-0119-z","journal-title":"Opt Rev"},{"key":"1805_CR11","doi-asserted-by":"crossref","unstructured":"Ayachit U, Bauer A, Geveci B et\u00a0al (2015) Paraview catalyst: enabling in situ data analysis and visualization. In: Proceedings of the first workshop on in situ infrastructures for enabling extreme-scale analysis and visualization, pp 25\u201329","DOI":"10.1145\/2828612.2828624"},{"key":"1805_CR12","doi-asserted-by":"publisher","unstructured":"Ayachit U, Whitlock B, Wolf M et\u00a0al (2016) The sensei generic in situ interface. In: 2016 second workshop on in situ infrastructures for enabling extreme-scale analysis and visualization (ISAV), pp 40\u201344. https:\/\/doi.org\/10.1109\/ISAV.2016.013","DOI":"10.1109\/ISAV.2016.013"},{"key":"1805_CR13","doi-asserted-by":"crossref","unstructured":"Ayachit U, Bauer AC, Boeckel B et\u00a0al (2021) Catalyst revised: rethinking the paraview in situ analysis and visualization api. In: International conference on high performance computing. Springer, pp 484\u2013494","DOI":"10.1007\/978-3-030-90539-2_33"},{"key":"1805_CR14","unstructured":"Barros GF, C\u00f4rtes AMA, Coutinho AL (2020) Dynamic mode decomposition for density-driven gravity current simulations. In: CILAMCE 2020\u2014Proceedings of the XLI Ibero-Latin-American congress on computational methods in engineering"},{"key":"1805_CR15","doi-asserted-by":"publisher","unstructured":"Barros GF, Grave M, Viguerie A et\u00a0al (2022) Dynamic mode decomposition in adaptive mesh refinement and coarsening simulations. Eng Comput\u00a038:4241\u20134268. https:\/\/doi.org\/10.1007\/s00366-021-01485-6","DOI":"10.1007\/s00366-021-01485-6"},{"key":"1805_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/9781118483565","volume-title":"Computational fluid-structure interaction: methods and applications","author":"Y Bazilevs","year":"2013","unstructured":"Bazilevs Y, Takizawa K, Tezduyar TE (2013) Computational fluid-structure interaction: methods and applications. Wiley, New York"},{"key":"1805_CR17","doi-asserted-by":"publisher","unstructured":"Benner P, Grivet-Talocia S, Quarteroni A et al (2020) Model order reduction. De Gruyter. https:\/\/doi.org\/10.1515\/9783110499001","DOI":"10.1515\/9783110499001"},{"key":"1805_CR18","doi-asserted-by":"publisher","unstructured":"Berry MW, Mezher D, Philippe B et al (2005) Parallel algorithms for the singular value decomposition. In: Handbook of parallel computing and statistics. Chapman and Hall\/CRC, pp 117\u2013164. https:\/\/doi.org\/10.1201\/9781420028683.ch4","DOI":"10.1201\/9781420028683.ch4"},{"issue":"2","key":"1805_CR19","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0021-9991(92)90240-Y","volume":"100","author":"JU Brackbill","year":"1992","unstructured":"Brackbill JU, Kothe DB, Zemach C (1992) A continuum method for modeling surface tension. J Comput Phys 100(2):335\u2013354","journal-title":"J Comput Phys"},{"key":"1805_CR20","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/3-540-47969-4_47","volume-title":"Computer Vision\u2014ECCV 2002","author":"M Brand","year":"2002","unstructured":"Brand M (2002) Incremental singular value decomposition of uncertain data with missing values. In: Heyden A, Sparr G, Nielsen M et al (eds) Computer Vision\u2014ECCV 2002. Springer, Berlin Heidelberg, pp 707\u2013720"},{"key":"1805_CR21","first-page":"2673","volume":"7","author":"A Bratko","year":"2006","unstructured":"Bratko A, Filipi\u010d B, Cormack GV et al (2006) Spam filtering using statistical data compression models. J Mach Learn Res 7:2673\u20132698","journal-title":"J Mach Learn Res"},{"key":"1805_CR22","doi-asserted-by":"publisher","unstructured":"Brunton SL, Kutz JN (2019) Data-driven science and engineering. Cambridge University Press, Cambridge. https:\/\/doi.org\/10.1017\/9781108380690","DOI":"10.1017\/9781108380690"},{"key":"1805_CR23","doi-asserted-by":"publisher","unstructured":"Brunton SL, Proctor JL, Kutz JN et\u00a0al (2016) Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci USA 113(15):3932\u20133937. https:\/\/doi.org\/10.1073\/pnas.1517384113. arXiv:1509.03580","DOI":"10.1073\/pnas.1517384113"},{"key":"1805_CR24","doi-asserted-by":"publisher","unstructured":"Bull DR, Zhang F (2021) Chapter 4\u2014digital picture formats and representations. In: Bull DR, Zhang F (eds) Intelligent image and video compression (Second Edition), 2nd edn. Academic Press, Oxford, pp 107\u2013142. https:\/\/doi.org\/10.1016\/B978-0-12-820353-8.00013-X","DOI":"10.1016\/B978-0-12-820353-8.00013-X"},{"issue":"1","key":"1805_CR25","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TC.2008.131","volume":"58","author":"M Burtscher","year":"2009","unstructured":"Burtscher M, Ratanaworabhan P (2009) FPC: a high-speed compressor for double-precision floating-point data. IEEE Trans Comput 58(1):18\u201331. https:\/\/doi.org\/10.1109\/TC.2008.131","journal-title":"IEEE Trans Comput"},{"issue":"3","key":"1805_CR26","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s10494-020-00156-8","volume":"105","author":"H Calmet","year":"2020","unstructured":"Calmet H, Pastrana D, Lehmkuhl O et al (2020) Dynamic mode decomposition analysis of high-fidelity CFD simulations of the sinus ventilation. Flow Turbul Combust 105(3):699\u2013713. https:\/\/doi.org\/10.1007\/s10494-020-00156-8","journal-title":"Flow Turbul Combust"},{"key":"1805_CR27","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.cageo.2017.09.013","volume":"110","author":"JJ Camata","year":"2018","unstructured":"Camata JJ, Silva V, Valduriez P et al (2018) In situ visualization and data analysis for turbidity currents simulation. Comput Geosci 110:23\u201331. https:\/\/doi.org\/10.1016\/j.cageo.2017.09.013","journal-title":"Comput Geosci"},{"issue":"2","key":"1805_CR28","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/nme.3050","volume":"86","author":"K Carlberg","year":"2011","unstructured":"Carlberg K, Bou-Mosleh C, Farhat C (2011) Efficient non-linear model reduction via a least-squares Petrov\u2013Galerkin projection and compressive tensor approximations. Int J Numer Methods Eng 86(2):155\u2013181. https:\/\/doi.org\/10.1002\/nme.3050","journal-title":"Int J Numer Methods Eng"},{"key":"1805_CR29","unstructured":"Childs H, Brugger E, Whitlock B et al (2012) VisIt: an end-user tool for visualizing and analyzing very large data. United States. Department of Energy. Office of Science"},{"issue":"109","key":"1805_CR30","first-page":"845","volume":"424","author":"Y Choi","year":"2021","unstructured":"Choi Y, Brown P, Arrighi W et al (2021) Space-time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems. J Comput Phys 424(109):845","journal-title":"J Comput Phys"},{"key":"1805_CR31","doi-asserted-by":"publisher","unstructured":"Claggett S, Azimi S, Burtscher M (2018) SPDP: an automatically synthesized lossless compression algorithm for floating-point data. In: 2018 data compression conference, pp 335\u2013344. https:\/\/doi.org\/10.1109\/DCC.2018.00042(ISSN: 2375-0359)","DOI":"10.1109\/DCC.2018.00042"},{"key":"1805_CR32","doi-asserted-by":"crossref","unstructured":"Codina R, Badia S, Baiges J et\u00a0al (2018) Variational multiscale methods in computational fluid dynamics. In: Encyclopedia of computational mechanics, 2nd edn, pp 1\u201328","DOI":"10.1002\/9781119176817.ecm2117"},{"key":"1805_CR33","doi-asserted-by":"crossref","unstructured":"Coupez T (2007) Convection of local level set function for moving surfaces and interfaces in forming flow. In: AIP conference proceedings. AIP, pp 61\u201366","DOI":"10.1063\/1.2740790"},{"key":"1805_CR34","doi-asserted-by":"publisher","unstructured":"Di S, Cappello F (2016) Fast error-bounded lossy HPC data compression with SZ. In: 2016 IEEE international parallel and distributed processing symposium (IPDPS), pp 730\u2013739. https:\/\/doi.org\/10.1109\/IPDPS.2016.11(ISSN: 1530-2075)","DOI":"10.1109\/IPDPS.2016.11"},{"key":"1805_CR35","doi-asserted-by":"publisher","unstructured":"Diffenderfer J, Fox AL, Hittinger JA et\u00a0al (2019) Error analysis of ZFP compression for floating-point data. SIAM J Sci Comput 41(3):A1867\u2013A1898. https:\/\/doi.org\/10.1137\/18M1168832. arXiv:1805.00546","DOI":"10.1137\/18M1168832"},{"issue":"3","key":"1805_CR36","doi-asserted-by":"publisher","first-page":"A1867","DOI":"10.1137\/18M1168832","volume":"41","author":"J Diffenderfer","year":"2019","unstructured":"Diffenderfer J, Fox AL, Hittinger JA et al (2019) Error analysis of zfp compression for floating-point data. SIAM J Sci Comput 41(3):A1867\u2013A1898. https:\/\/doi.org\/10.1137\/18M1168832","journal-title":"SIAM J Sci Comput"},{"issue":"4","key":"1805_CR37","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1137\/17M1117732","volume":"60","author":"J Dongarra","year":"2018","unstructured":"Dongarra J, Gates M, Haidar A et al (2018) The singular value decomposition: anatomy of optimizing an algorithm for extreme scale. SIAM Rev 60(4):808\u2013865. https:\/\/doi.org\/10.1137\/17M1117732","journal-title":"SIAM Rev"},{"issue":"3","key":"1805_CR38","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/18.382009","volume":"41","author":"DL Donoho","year":"1995","unstructured":"Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41(3):613\u2013627","journal-title":"IEEE Trans Inf Theory"},{"issue":"1","key":"1805_CR39","first-page":"1","volume":"89","author":"NB Erichson","year":"2019","unstructured":"Erichson NB, Voronin S, Brunton SL et al (2019) Randomized matrix decompositions using R. J Stat Softw 89(1):1\u201348","journal-title":"J Stat Softw"},{"issue":"6","key":"1805_CR40","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1177\/10943420211023549","volume":"35","author":"I Foster","year":"2021","unstructured":"Foster I, Ainsworth M, Bessac J et al (2021) Online data analysis and reduction: an important co-design motif for extreme-scale computers. Int J High Perform Comput Appl 35(6):617\u2013635","journal-title":"Int J High Perform Comput Appl"},{"issue":"115","key":"1805_CR41","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1016\/j.cma.2022.115436","volume":"399","author":"WD Fries","year":"2022","unstructured":"Fries WD, He X, Choi Y (2022) Lasdi: Parametric latent space dynamics identification. Comput Methods Appl Mech Eng 399(115):436. https:\/\/doi.org\/10.1016\/j.cma.2022.115436","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"8","key":"1805_CR42","doi-asserted-by":"publisher","first-page":"5040","DOI":"10.1109\/TIT.2014.2323359","volume":"60","author":"M Gavish","year":"2014","unstructured":"Gavish M, Donoho DL (2014) The optimal hard threshold for singular values is $$4\/\\sqrt{3}$$. IEEE Trans Inf Theory 60(8):5040\u20135053","journal-title":"IEEE Trans Inf Theory"},{"key":"1805_CR43","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.compfluid.2019.05.012","volume":"188","author":"L Gesenhues","year":"2019","unstructured":"Gesenhues L, Camata JJ, C\u00f4rtes AMA et al (2019) Finite element simulation of complex dense granular flows using a well-posed regularization of the $$\\mu (I)$$-rheology. Comput Fluids 188:102\u2013113. https:\/\/doi.org\/10.1016\/j.compfluid.2019.05.012","journal-title":"Comput Fluids"},{"issue":"7553","key":"1805_CR44","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1038\/nature14541","volume":"521","author":"Z Ghahramani","year":"2015","unstructured":"Ghahramani Z (2015) Probabilistic machine learning and artificial intelligence. Nature 521(7553):452\u2013459","journal-title":"Nature"},{"issue":"104","key":"1805_CR45","first-page":"667","volume":"209","author":"M Grave","year":"2020","unstructured":"Grave M, Camata JJ, Coutinho AL (2020) A new convected level-set method for gas bubble dynamics. Comput Fluids 209(104):667","journal-title":"Comput Fluids"},{"issue":"8","key":"1805_CR46","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1002\/fld.3820","volume":"73","author":"GM Guerra","year":"2013","unstructured":"Guerra GM, Zio S, Camata JJ et al (2013) Numerical simulation of particle-laden flows by the residual-based variational multiscale method. Int J Numer Methods Fluids 73(8):729\u2013749. https:\/\/doi.org\/10.1002\/fld.3820","journal-title":"Int J Numer Methods Fluids"},{"issue":"2","key":"1805_CR47","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1137\/090771806","volume":"53","author":"N Halko","year":"2011","unstructured":"Halko N, Martinsson PG, Tropp JA (2011) Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev 53(2):217\u2013288. https:\/\/doi.org\/10.1137\/090771806. arxiv:0909.4061","journal-title":"SIAM Rev"},{"key":"1805_CR48","doi-asserted-by":"crossref","unstructured":"He X, Choi Y, Fries WD et\u00a0al (2022) glasdi: parametric physics-informed greedy latent space dynamics identification. arXiv:2204.12005","DOI":"10.2139\/ssrn.4108989"},{"key":"1805_CR49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22470-1","author":"JS Hesthaven","year":"2015","unstructured":"Hesthaven JS, Rozza G, Stamm B (2015) Certified reduced basis methods for parametrized partial differential equations. Springer. https:\/\/doi.org\/10.1007\/978-3-319-22470-1","journal-title":"Springer"},{"key":"1805_CR50","doi-asserted-by":"crossref","unstructured":"Hughes TJ, Scovazzi G, Franca LP (2004) Multiscale and stabilized methods. In: Encyclopedia of computational mechanics, 2nd edn","DOI":"10.1002\/0470091355.ecm051"},{"key":"1805_CR51","doi-asserted-by":"crossref","unstructured":"Jin S, Tao D, Tang H et\u00a0al (2022) Accelerating parallel write via deeply integrating predictive lossy compression with HDF5. In: Proceedings of the international conference on high performance computing, networking, storage and analysis. IEEE Press, SC \u201922","DOI":"10.1109\/SC41404.2022.00066"},{"issue":"104","key":"1805_CR52","doi-asserted-by":"publisher","first-page":"098","DOI":"10.1016\/j.advwatres.2021.104098","volume":"160","author":"T Kadeethum","year":"2022","unstructured":"Kadeethum T, Ballarin F, Choi Y et al (2022) Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. Adv Water Resour 160(104):098. https:\/\/doi.org\/10.1016\/j.advwatres.2021.104098","journal-title":"Adv Water Resour"},{"issue":"1","key":"1805_CR53","doi-asserted-by":"publisher","first-page":"20654","DOI":"10.1038\/s41598-022-24545-3","volume":"12","author":"T Kadeethum","year":"2022","unstructured":"Kadeethum T, Ballarin F, O\u2019malley D et al (2022) Reduced order modeling for flow and transport problems with barlow twins self-supervised learning. Sci Rep 12(1):20654","journal-title":"Sci Rep"},{"key":"1805_CR54","doi-asserted-by":"publisher","unstructured":"Kaiser E, Kutz JN, Brunton SL (2018) Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proc R Soc A Math Phys Eng Sci 474(2219). https:\/\/doi.org\/10.1098\/rspa.2018.0335. arXiv:1711.05501","DOI":"10.1098\/rspa.2018.0335"},{"key":"1805_CR55","doi-asserted-by":"crossref","unstructured":"Kim Y, Choi Y, Widemann D et\u00a0al (2020) Efficient nonlinear manifold reduced order model. arXiv:2011.07727","DOI":"10.2172\/1669223"},{"key":"1805_CR56","doi-asserted-by":"publisher","unstructured":"Kim Y, Choi Y, Widemann D et\u00a0al (2022) A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder. J Comput Phys 451. https:\/\/doi.org\/10.1016\/j.jcp.2021.110841. arXiv:2009.11990","DOI":"10.1016\/j.jcp.2021.110841"},{"issue":"3\u20134","key":"1805_CR57","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s00366-006-0049-3","volume":"22","author":"BS Kirk","year":"2006","unstructured":"Kirk BS, Peterson JW, Stogner RH et al (2006) libMesh: a C++ library for parallel adaptive mesh refinement\/coarsening simulations. Eng Comput 22(3\u20134):237\u2013254. https:\/\/doi.org\/10.1007\/s00366-006-0049-3","journal-title":"Eng Comput"},{"key":"1805_CR58","doi-asserted-by":"crossref","unstructured":"Kutz JN, Brunton SL, Brunton BW et\u00a0al (2016) Dynamic mode decomposition: data-driven modeling of complex systems. SIAM.\u00a0ISBN: 978-1-611974-49-2","DOI":"10.1137\/1.9781611974508"},{"key":"1805_CR59","doi-asserted-by":"publisher","unstructured":"Larsen M, Ahrens J, Ayachit U et\u00a0al (2017) The alpine in situ infrastructure: ascending from the ashes of strawman. In: Proceedings of the in situ infrastructures on enabling extreme-scale analysis and visualization. Association for Computing Machinery, New York, ISAV\u201917, pp 42\u201346. https:\/\/doi.org\/10.1145\/3144769.3144778","DOI":"10.1145\/3144769.3144778"},{"issue":"7","key":"1805_CR60","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.1109\/TC.2021.3092201","volume":"71","author":"X Liang","year":"2022","unstructured":"Liang X, Whitney B, Chen J et al (2022) Mgard+: optimizing multilevel methods for error-bounded scientific data reduction. IEEE Trans Comput 71(7):1522\u20131536. https:\/\/doi.org\/10.1109\/TC.2021.3092201","journal-title":"IEEE Trans Comput"},{"issue":"12","key":"1805_CR61","doi-asserted-by":"publisher","first-page":"2674","DOI":"10.1109\/TVCG.2014.2346458","volume":"20","author":"P Lindstrom","year":"2014","unstructured":"Lindstrom P (2014) Fixed-rate compressed floating-point arrays. IEEE Trans Vis Comput Graph 20(12):2674\u20132683. https:\/\/doi.org\/10.1109\/TVCG.2014.2346458","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"1805_CR62","doi-asserted-by":"publisher","unstructured":"Lindstrom P, Isenburg M (2006) Fast and efficient compression of floating-point data. IEEE Trans Vis Comput Graph 12(5):1245\u20131250. https:\/\/doi.org\/10.1109\/TVCG.2006.143. http:\/\/ieeexplore.ieee.org\/document\/4015488\/","DOI":"10.1109\/TVCG.2006.143"},{"key":"1805_CR63","unstructured":"Littlestone N, Warmuth M (1986) Relating data compression and learnability. Technical report, Department of Computer and Information Sciences, Santa Cruz"},{"key":"1805_CR64","unstructured":"Matsumoto D, Indinger T (2017) On-the-fly algorithm for dynamic mode decomposition using incremental singular value decomposition and total least squares. arXiv:1703.11004"},{"key":"1805_CR65","doi-asserted-by":"publisher","unstructured":"Maulik R, Mengaldo G (2021) PyParSVD: a streaming, distributed and randomized singular-value-decomposition library. In: Proceedings of DRBSD-7 2021: 7th international workshop on data analysis and reduction for big scientific data, held in conjunction with SC 2021: the international conference for high performance computing, networking, storage and analysis, pp 19\u201325. https:\/\/doi.org\/10.1109\/DRBSD754563.2021.00007. arXiv:2108.08845","DOI":"10.1109\/DRBSD754563.2021.00007"},{"key":"1805_CR66","doi-asserted-by":"publisher","unstructured":"Newberry F, Wetterer-Nelson C, Evans JA et\u00a0al (2022) Software tools to enable immersive simulation. Eng Comput 38:4697\u20134713. https:\/\/doi.org\/10.1007\/s00366-022-01714-6","DOI":"10.1007\/s00366-022-01714-6"},{"key":"1805_CR67","unstructured":"Nilsson J, Akenine-M\u00f6ller T (2020) Understanding SSIM. arXiv:2006.13846"},{"issue":"2","key":"1805_CR68","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1002\/nme.5283","volume":"109","author":"GM Oxberry","year":"2017","unstructured":"Oxberry GM, Kostova-Vassilevska T, Arrighi W et al (2017) Limited-memory adaptive snapshot selection for proper orthogonal decomposition. Int J Numer Methods Eng 109(2):198\u2013217. https:\/\/doi.org\/10.1002\/nme.5283","journal-title":"Int J Numer Methods Eng"},{"issue":"4","key":"1805_CR69","doi-asserted-by":"publisher","first-page":"A2123","DOI":"10.1137\/140989169","volume":"37","author":"B Peherstorfer","year":"2015","unstructured":"Peherstorfer B, Willcox K (2015) Online adaptive model reduction for nonlinear systems via low-rank updates. SIAM J Sci Comput 37(4):A2123\u2013A2150. https:\/\/doi.org\/10.1137\/140989169","journal-title":"SIAM J Sci Comput"},{"key":"1805_CR70","doi-asserted-by":"publisher","unstructured":"Peherstorfer B, Willcox K, Gunzburger M (2018) Survey of multifidelity methods in uncertainty propagation, inference, and optimization. SIAM Rev 60(3):550\u2013591. https:\/\/doi.org\/10.1137\/16M1082469. arxiv:1806.10761","DOI":"10.1137\/16M1082469"},{"issue":"12","key":"1805_CR71","doi-asserted-by":"publisher","first-page":"2674","DOI":"10.1109\/TVCG.2014.2346458","volume":"20","author":"Peter Lindstrom","year":"2014","unstructured":"Lindstrom Peter (2014) Fixed-rate compressed floating-point arrays. IEEE Trans Vis Comput Graph 20(12):2674\u20132683. https:\/\/doi.org\/10.1109\/TVCG.2014.2346458","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"1805_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113120","author":"P Phalippou","year":"2020","unstructured":"Phalippou P, Bouabdallah S, Breitkopf P et al (2020) \u2018On-the-fly\u2019 snapshots selection for proper orthogonal decomposition with application to nonlinear dynamics. Comput Methods Appl Mech Eng. https:\/\/doi.org\/10.1016\/j.cma.2020.113120","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"2","key":"1805_CR73","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1093\/inthealth\/ihv009","volume":"7","author":"JL Proctor","year":"2015","unstructured":"Proctor JL, Eckhoff PA (2015) Discovering dynamic patterns from infectious disease data using dynamic mode decomposition. Int Health 7(2):139\u2013145. https:\/\/doi.org\/10.1093\/inthealth\/ihv009","journal-title":"Int Health"},{"issue":"1","key":"1805_CR74","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1137\/15M1013857","volume":"15","author":"JL Proctor","year":"2016","unstructured":"Proctor JL, Brunton SL, Kutz JN (2016) Dynamic mode decomposition with control. SIAM J Appl Dyn Syst 15(1):142\u2013161. https:\/\/doi.org\/10.1137\/15M1013857. arxiv:1409.6358","journal-title":"SIAM J Appl Dyn Syst"},{"key":"1805_CR75","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi M, Perdikaris P, Karniadakis G (2019) Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686\u2013707. https:\/\/doi.org\/10.1016\/j.jcp.2018.10.045","journal-title":"J Comput Phys"},{"key":"1805_CR76","doi-asserted-by":"publisher","unstructured":"Rasthofer U, Gravemeier V (2018) Recent developments in variational multiscale methods for large-eddy simulation of turbulent flow. Arch Comput Methods Eng\u00a025:647\u2013690. https:\/\/doi.org\/10.1007\/s11831-017-9209-4","DOI":"10.1007\/s11831-017-9209-4"},{"key":"1805_CR77","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.compfluid.2013.06.008","volume":"88","author":"AL Rossa","year":"2013","unstructured":"Rossa AL, Coutinho AL (2013) Parallel adaptive simulation of gravity currents on the lock-exchange problem. Comput Fluids 88:782\u2013794","journal-title":"Comput Fluids"},{"key":"1805_CR78","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1017\/S0022112009992059","volume":"641","author":"CW Rowley","year":"2009","unstructured":"Rowley CW, Mezic I, Bagheri S et al (2009) Spectral analysis of nonlinear flows. J Fluid Mech 641:115\u2013127","journal-title":"J Fluid Mech"},{"issue":"2229","key":"1805_CR79","doi-asserted-by":"publisher","first-page":"20210199","DOI":"10.1098\/rsta.2021.0199","volume":"380","author":"D Sashidhar","year":"2022","unstructured":"Sashidhar D, Kutz JN (2022) Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification. Philos Trans R Soc A 380(2229):20210199","journal-title":"Philos Trans R Soc A"},{"key":"1805_CR80","doi-asserted-by":"publisher","unstructured":"Sayood K (2012) Introduction to data compression. Elsevier. https:\/\/doi.org\/10.1016\/C2010-0-69630-1","DOI":"10.1016\/C2010-0-69630-1"},{"key":"1805_CR81","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1017\/S0022112010001217","volume":"656","author":"PJ Schmid","year":"2010","unstructured":"Schmid PJ (2010) Dynamic mode decomposition of numerical and experimental data. J Fluid Mech 656:5\u201328","journal-title":"J Fluid Mech"},{"key":"1805_CR82","doi-asserted-by":"publisher","unstructured":"Sculley D, Brodley CE (2006) Compression and machine learning: a new perspective on feature space vectors. In: Data compression conference proceedings, pp 332\u2013341. https:\/\/doi.org\/10.1109\/DCC.2006.13","DOI":"10.1109\/DCC.2006.13"},{"key":"1805_CR83","doi-asserted-by":"publisher","unstructured":"Silva V, Campos V, Guedes T et al (2020) DfAnalyzer: runtime dataflow analysis tool for Computational Science and Engineering applications. SoftwareX 12. https:\/\/doi.org\/10.1016\/j.softx.2020.100592","DOI":"10.1016\/j.softx.2020.100592"},{"issue":"3","key":"1805_CR84","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1090\/qam\/910462","volume":"45","author":"L Sirovich","year":"1987","unstructured":"Sirovich L (1987) Turbulence and the dynamics of coherent structures I, II and III. Q Appl Math 45(3):561\u2013590","journal-title":"Q Appl Math"},{"issue":"12","key":"1805_CR85","doi-asserted-by":"publisher","first-page":"4013","DOI":"10.2514\/1.J056060","volume":"55","author":"K Taira","year":"2017","unstructured":"Taira K, Brunton SL, Dawson ST et al (2017) Modal analysis of fluid flows: an overview. AIAA J 55(12):4013\u20134041","journal-title":"AIAA J"},{"key":"1805_CR86","unstructured":"The HDF Group (2020) Hierarchical data format version 5. https:\/\/www.hdfgroup.org\/solutions\/hdf5\/"},{"issue":"114","key":"1805_CR87","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.cma.2022.114600","volume":"391","author":"A Viguerie","year":"2022","unstructured":"Viguerie A, Barros GF, Grave M et al (2022) Coupled and uncoupled dynamic mode decomposition in multi-compartmental systems with applications to epidemiological and additive manufacturing problems. Comput Methods Appl Mech Eng 391(114):600. https:\/\/doi.org\/10.1016\/j.cma.2022.114600","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"12","key":"1805_CR88","doi-asserted-by":"publisher","DOI":"10.1115\/1.4054925","volume":"144","author":"A Viguerie","year":"2022","unstructured":"Viguerie A, Grave M, Barros GF et al (2022) Data-driven simulation of Fisher-Kolmogorov tumor growth models using dynamic mode decomposition. J Biomech Eng 144(12):121001","journal-title":"J Biomech Eng"},{"issue":"3","key":"1805_CR89","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1002\/fld.2259","volume":"66","author":"L Ville","year":"2011","unstructured":"Ville L, Silva L, Coupez T (2011) Convected level set method for the numerical simulation of fluid buckling. Int J Numer Methods Fluids 66(3):324\u2013344","journal-title":"Int J Numer Methods Fluids"},{"key":"1805_CR90","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.453","volume":"2","author":"S van der Walt","year":"2014","unstructured":"van der Walt S, Sch\u00f6nberger JL, Nunez-Iglesias J et al (2014) scikit-image: image processing in Python. PeerJ 2:e453. https:\/\/doi.org\/10.7717\/peerj.453","journal-title":"PeerJ"},{"issue":"1","key":"1805_CR91","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/MSP.2008.930649","volume":"26","author":"Z Wang","year":"2009","unstructured":"Wang Z, Bovik AC (2009) Mean squared error: love it or leave it? IEEE Signal Process Mag 26(1):98\u2013117","journal-title":"IEEE Signal Process Mag"},{"issue":"4","key":"1805_CR92","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans Image Process"},{"key":"1805_CR93","doi-asserted-by":"publisher","unstructured":"Zhang H, Rowley CW, Deem EA et\u00a0al (2019) Online dynamic mode decomposition for time-varying systems. SIAM J Appl Dyn Syst 18(3):1586\u20131609. https:\/\/doi.org\/10.1137\/18M1192329. arXiv:1707.02876","DOI":"10.1137\/18M1192329"},{"key":"1805_CR94","doi-asserted-by":"publisher","unstructured":"Zhao K, Di S, Lian X et\u00a0al (2020) Sdrbench: scientific data reduction benchmark for lossy compressors. In: 2020 IEEE international conference on big data (Big Data), pp 2716\u20132724. https:\/\/doi.org\/10.1109\/BigData50022.2020.9378449","DOI":"10.1109\/BigData50022.2020.9378449"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-023-01805-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-023-01805-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-023-01805-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:08:10Z","timestamp":1706753290000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-023-01805-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,14]]},"references-count":94,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1805"],"URL":"https:\/\/doi.org\/10.1007\/s00366-023-01805-y","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"type":"print","value":"0177-0667"},{"type":"electronic","value":"1435-5663"}],"subject":[],"published":{"date-parts":[[2023,3,14]]},"assertion":[{"value":"18 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}