{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:26:45Z","timestamp":1771475205730,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1913073"],"award-info":[{"award-number":["DMS-1913073"]}],"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":["SC0020270"],"award-info":[{"award-number":["SC0020270"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-2012469"],"award-info":[{"award-number":["DMS-2012469"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sci Comput"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s10915-021-01668-9","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T19:42:41Z","timestamp":1635450161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A FOM\/ROM Hybrid Approach for Accelerating Numerical Simulations"],"prefix":"10.1007","volume":"89","author":[{"given":"Lihong","family":"Feng","sequence":"first","affiliation":[]},{"given":"Guosheng","family":"Fu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7821-8574","authenticated-orcid":false,"given":"Zhu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"issue":"3","key":"1668_CR1","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1016\/j.jfranklin.2020.11.012","volume":"358","author":"MMA Asif","year":"2021","unstructured":"Asif, M.M.A., Ahmad, M.I., Benner, P., Feng, L., Stykel, T.: Implicit higher-order moment matching technique for model reduction of quadratic-bilinear systems. J. Franklin Inst. 358(3), 2015\u20132038 (2021)","journal-title":"J. Franklin Inst."},{"issue":"12","key":"1668_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-020-03958-7","volume":"2","author":"F Bai","year":"2020","unstructured":"Bai, F., Wang, Y.: DEIM reduced order model constructed by hybrid snapshot simulation. SN Appl. Sci. 2(12), 1\u201325 (2020)","journal-title":"SN Appl. Sci."},{"issue":"01","key":"1668_CR3","doi-asserted-by":"publisher","first-page":"2050029","DOI":"10.1142\/S0219876220500292","volume":"18","author":"F Bai","year":"2021","unstructured":"Bai, F., Wang, Y.: Reduced-order modeling based on hybrid snapshot simulation. Int. J. Comput. Methods 18(01), 2050029 (2021)","journal-title":"Int. J. Comput. Methods"},{"issue":"5","key":"1668_CR4","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1002\/nme.4800","volume":"102","author":"K Carlberg","year":"2015","unstructured":"Carlberg, K.: Adaptive h-refinement for reduced-order models. Int. J. Numer. Meth. Eng. 102(5), 1192\u20131210 (2015)","journal-title":"Int. J. Numer. Meth. Eng."},{"issue":"23","key":"1668_CR5","doi-asserted-by":"publisher","first-page":"5320","DOI":"10.1002\/nme.6462","volume":"121","author":"S Chellappa","year":"2020","unstructured":"Chellappa, S., Feng, L., Benner, P.: Adaptive basis construction and improved error estimation for parametric nonlinear dynamical systems. Int. J. Numer. Meth. Eng. 121(23), 5320\u20135349 (2020)","journal-title":"Int. J. Numer. Meth. Eng."},{"key":"1668_CR6","doi-asserted-by":"crossref","unstructured":"Chellappa, S., Feng, L., Benner, P.: A training set subsampling strategy for the reduced basis method. arXiv preprint arXiv:2103.06185, (2021)","DOI":"10.1007\/s10915-021-01665-y"},{"key":"1668_CR7","doi-asserted-by":"crossref","unstructured":"Chen, F., Hesthaven, J.\u00a0S., Zhu, X.: On the use of reduced basis methods to accelerate and stabilize the parareal method. In: Reduced Order Methods for modeling and computational reduction, pages 187\u2013214. Springer, 2014","DOI":"10.1007\/978-3-319-02090-7_7"},{"issue":"5","key":"1668_CR8","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1051\/m2an\/2020004","volume":"54","author":"A Cohen","year":"2020","unstructured":"Cohen, A., Dahmen, W., DeVore, R., Nichols, J.: Reduced basis greedy selection using random training sets. ESAIM. Math. Model. Numer. Anal. 54(5), 1509\u20131524 (2020)","journal-title":"Math. Model. Numer. Anal."},{"issue":"2","key":"1668_CR9","doi-asserted-by":"publisher","first-page":"A631","DOI":"10.1137\/15M1019271","volume":"38","author":"Z Drmac","year":"2016","unstructured":"Drmac, Z., Gugercin, S.: A new selection operator for the discrete empirical interpolation method\u2013improved a priori error bound and extensions. SIAM J. Sci. Comput. 38(2), A631\u2013A648 (2016)","journal-title":"SIAM J. Sci. Comput."},{"key":"1668_CR10","doi-asserted-by":"crossref","unstructured":"Esfahanian, V., Ashrafi, K.: Equation-free\/Galerkin-free reduced-order modeling of the shallow water equations based on proper orthogonal decomposition. Journal of fluids engineering, 131(7), 2009","DOI":"10.1115\/1.3153368"},{"key":"1668_CR11","doi-asserted-by":"publisher","first-page":"112931","DOI":"10.1016\/j.cma.2020.112931","volume":"364","author":"PA Etter","year":"2020","unstructured":"Etter, P.A., Carlberg, K.T.: Online adaptive basis refinement and compression for reduced-order models via vector-space sieving. Comput. Methods Appl. Mech. Eng. 364, 112931 (2020)","journal-title":"Comput. Methods Appl. Mech. Eng."},{"issue":"5","key":"1668_CR12","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1002\/nme.1653","volume":"67","author":"C Farhat","year":"2006","unstructured":"Farhat, C., Cortial, J., Dastillung, C., Bavestrello, H.: Time-parallel implicit integrators for the near-real-time prediction of linear structural dynamic responses. Int. J. Numer. Meth. Eng. 67(5), 697\u2013724 (2006)","journal-title":"Int. J. Numer. Meth. Eng."},{"key":"1668_CR13","doi-asserted-by":"crossref","unstructured":"Fresca, S., Manzoni, A.: POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. arXiv preprint arXiv:2101.11845, (2021)","DOI":"10.1016\/j.cma.2021.114181"},{"issue":"9","key":"1668_CR14","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.1109\/TCAD.2011.2142184","volume":"30","author":"C Gu","year":"2011","unstructured":"Gu, C.: QLMOR: A projection-based nonlinear model order reduction approach using quadratic-linear representation of nonlinear systems. IEEE Trans. Comput. Aided Des. Integr. Circuits. Syst. 30(9), 1307\u20131320 (2011)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits. Syst."},{"issue":"02","key":"1668_CR15","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1051\/m2an:2008001","volume":"42","author":"B Haasdonk","year":"2008","unstructured":"Haasdonk, B., Ohlberger, M.: Reduced basis method for finite volume approximations of parametrized linear evolution equations. ESAIM. Math. Model. Numer. Anal. 42(02), 277\u2013302 (2008)","journal-title":"Math. Model. Numer. Anal."},{"key":"1668_CR16","unstructured":"Hesthaven, J.\u00a0S., Pagliantini, C., Ripamonti, N.: Rank-adaptive structure-preserving reduced basis methods for Hamiltonian systems. arXiv preprint arXiv:2007.13153, (2020)"},{"key":"1668_CR17","doi-asserted-by":"crossref","unstructured":"Kim, Y., Choi, Y., Widemann, D., Zohdi, T.: Efficient nonlinear manifold reduced order model. arXiv preprint arXiv:2011.07727, (2020)","DOI":"10.2172\/1669223"},{"key":"1668_CR18","doi-asserted-by":"crossref","unstructured":"Lee, K., Carlberg, K.: Deep conservation: A latent-dynamics model for exact satisfaction of physical conservation laws. arXiv preprint arXiv:1909.09754, (2019)","DOI":"10.2172\/1569346"},{"key":"1668_CR19","doi-asserted-by":"publisher","first-page":"108973","DOI":"10.1016\/j.jcp.2019.108973","volume":"404","author":"K Lee","year":"2020","unstructured":"Lee, K., Carlberg, K.T.: Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. J. Comput. Phys. 404, 108973 (2020)","journal-title":"J. Comput. Phys."},{"key":"1668_CR20","doi-asserted-by":"publisher","first-page":"112915","DOI":"10.1016\/j.cam.2020.112915","volume":"377","author":"Y Maday","year":"2020","unstructured":"Maday, Y., Mula, O.: An adaptive parareal algorithm. J. Comput. Appl. Math. 377, 112915 (2020)","journal-title":"J. Comput. Appl. Math."},{"issue":"3","key":"1668_CR21","doi-asserted-by":"publisher","first-page":"037106","DOI":"10.1063\/5.0039986","volume":"33","author":"R Maulik","year":"2021","unstructured":"Maulik, R., Lusch, B., Balaprakash, P.: Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders. Phys. Fluids 33(3), 037106 (2021)","journal-title":"Phys. Fluids"},{"issue":"5","key":"1668_CR22","doi-asserted-by":"publisher","first-page":"A2803","DOI":"10.1137\/19M1257275","volume":"42","author":"B Peherstorfer","year":"2020","unstructured":"Peherstorfer, B.: Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling. SIAM J. Sci. Comput. 42(5), A2803\u2013A2836 (2020)","journal-title":"SIAM J. Sci. Comput."},{"issue":"6","key":"1668_CR23","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1007\/s10444-018-9590-z","volume":"44","author":"O San","year":"2018","unstructured":"San, O., Maulik, R.: Neural network closures for nonlinear model order reduction. Adv. Comput. Math. 44(6), 1717\u20131750 (2018)","journal-title":"Adv. Comput. Math."},{"key":"1668_CR24","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1016\/j.jcp.2005.01.024","volume":"207","author":"S Sirisup","year":"2005","unstructured":"Sirisup, S., Karniadakis, G.E., Xiu, D., Kevrekidis, I.G.: Equation-free\/Galerkin-free POD-assisted computation of incompressible flows. J. Comput. Phys. 207, 568\u2013587 (2005)","journal-title":"J. Comput. Phys."},{"key":"1668_CR25","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.cam.2015.11.023","volume":"307","author":"Z Wang","year":"2016","unstructured":"Wang, Z., McBee, B., Iliescu, T.: Approximate partitioned method of snapshots for POD. J. Comput. Appl. Math. 307, 374\u2013384 (2016)","journal-title":"J. Comput. Appl. Math."},{"issue":"6","key":"1668_CR26","doi-asserted-by":"publisher","first-page":"B910","DOI":"10.1137\/140998603","volume":"37","author":"Y Zhang","year":"2015","unstructured":"Zhang, Y., Feng, L., Li, S., Benner, P.: An efficient output error estimation for model order reduction of parametrized evolution equations. SIAM J. Sci. Comput. 37(6), B910\u2013B936 (2015)","journal-title":"SIAM J. Sci. Comput."}],"container-title":["Journal of Scientific Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10915-021-01668-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10915-021-01668-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10915-021-01668-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T07:19:04Z","timestamp":1637651944000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10915-021-01668-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,28]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["1668"],"URL":"https:\/\/doi.org\/10.1007\/s10915-021-01668-9","relation":{},"ISSN":["0885-7474","1573-7691"],"issn-type":[{"value":"0885-7474","type":"print"},{"value":"1573-7691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,28]]},"assertion":[{"value":"15 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"61"}}