{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:09:18Z","timestamp":1780092558207,"version":"3.54.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2021R1A2C1007352"],"award-info":[{"award-number":["2021R1A2C1007352"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2021R1A5A1031868"],"award-info":[{"award-number":["2021R1A5A1031868"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>An improved nonintrusive parametric model order reduction (pMOR) approach is proposed for the flow field interpolation regarding fluid\u2013structure interaction (FSI) objects. Flow field computation using computational fluid dynamics (CFD) requires excessive computational time and memory. Nonintrusive and data-driven MOR schemes have been proposed to overcome such limitations. The present methodology is implemented by both proper orthogonal decomposition (POD) and a modified Nouveau variational autoencoder (mNVAE). POD attempts to reduce the number of degrees of freedom (DOFs) on the precomputed series of the full-order model parametric result. The reduced DOF yields parametrically independent reduced bases and dependent coefficients. Then, mNVAE is employed for the interpolation of POD coefficients, which will be combined with POD modes for parametrically interpolated flow field generation. The present approach is assessed on the benchmark problem of a two-dimensional plunging airfoil and the highly nonlinear FSI phenomenon of the limit cycle oscillation. The comparison was executed against other POD-based generative neural network approaches. The proposed methodology demonstrates applicability on highly nonlinear FSI objects with improved accuracy and efficiency.<\/jats:p>","DOI":"10.1007\/s00366-023-01782-2","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T18:02:59Z","timestamp":1673373779000},"page":"45-60","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Parametric model order reduction by machine learning for fluid\u2013structure interaction analysis"],"prefix":"10.1007","volume":"40","author":[{"given":"SiHun","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kijoo","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangmin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haeseong","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5835-4926","authenticated-orcid":false,"given":"SangJoon","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"1782_CR1","unstructured":"Lumley JL (1967) The structure of inhomogeneous turbulent flows. Atmos Turbul Radio Wave Propag"},{"key":"1782_CR2","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, parts I, II and III. Quart Appl Math 45:561\u2013590","journal-title":"Quart Appl Math"},{"issue":"1","key":"1782_CR3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TAC.1981.1102568","volume":"26","author":"B Moore","year":"1981","unstructured":"Moore B (1981) Principal component analysis in linear systems: controllability, observability, and model reduction. IEEE Trans Autom Control 26(1):17\u201332","journal-title":"IEEE Trans Autom Control"},{"issue":"2","key":"1782_CR4","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1016\/S1474-6670(17)56442-3","volume":"32","author":"S Lall","year":"1999","unstructured":"Lall S, Marsden JE, Glava\u0161ki S (1999) Empirical model reduction of controlled nonlinear systems. IFAC Proc Volumes 32(2):2598\u20132603","journal-title":"IFAC Proc Volumes"},{"issue":"1","key":"1782_CR5","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1146\/annurev.fl.25.010193.002543","volume":"25","author":"G Berkooz","year":"1993","unstructured":"Berkooz G, Holmes P, Lumley JL (1993) The proper orthogonal decomposition in the analysis of turbulent flows. Annu Rev Fluid Mech 25(1):539\u2013575","journal-title":"Annu Rev Fluid Mech"},{"issue":"1\u20132","key":"1782_CR6","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.physd.2003.03.001","volume":"189","author":"CW Rowley","year":"2004","unstructured":"Rowley CW, Colonius T, Murray RM (2004) Model reduction for compressible flows using pod and galerkin projection. Physica D 189(1\u20132):115\u2013129","journal-title":"Physica D"},{"issue":"1","key":"1782_CR7","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.jcp.2005.01.008","volume":"207","author":"M Couplet","year":"2005","unstructured":"Couplet M, Basdevant C, Sagaut P (2005) Calibrated reduced-order pod-Galerkin system for fluid flow modelling. J Comput Phys 207(1):192\u2013220","journal-title":"J Comput Phys"},{"issue":"1","key":"1782_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2190-5983-1-1","volume":"1","author":"A Quarteroni","year":"2011","unstructured":"Quarteroni A, Rozza G, Manzoni A (2011) Certified reduced basis approximation for parametrized partial differential equations and applications. J Math Ind 1(1):1\u201349","journal-title":"J Math Ind"},{"key":"1782_CR9","unstructured":"Chen H, et al (2012) Blackbox stencil interpolation method for model reduction. PhD thesis, Massachusetts Institute of Technology"},{"key":"1782_CR10","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/j.cma.2019.06.018","volume":"355","author":"D Xiao","year":"2019","unstructured":"Xiao D (2019) Error estimation of the parametric non-intrusive reduced order model using machine learning. Comput Methods Appl Mech Eng 355:513\u2013534","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"3","key":"1782_CR11","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1002\/nme.5624","volume":"113","author":"A Moosavi","year":"2018","unstructured":"Moosavi A, \u015etef\u0103nescu R, Sandu A (2018) Multivariate predictions of local reduced-order-model errors and dimensions. Int J Numer Meth Eng 113(3):512\u2013533","journal-title":"Int J Numer Meth Eng"},{"key":"1782_CR12","unstructured":"Krauth K, Bonilla EV, Cutajar K, Filippone M (2016) Autogp: Exploring the capabilities and limitations of gaussian process models. arXiv Preprint arXiv:1610.05392"},{"key":"1782_CR13","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jcp.2018.02.037","volume":"363","author":"JS Hesthaven","year":"2018","unstructured":"Hesthaven JS, Ubbiali S (2018) Non-intrusive reduced order modeling of nonlinear problems using neural networks. J Comput Phys 363:55\u201378","journal-title":"J Comput Phys"},{"key":"1782_CR14","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.jcp.2019.01.031","volume":"384","author":"Q Wang","year":"2019","unstructured":"Wang Q, Hesthaven JS, Ray D (2019) Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem. J Comput Phys 384:289\u2013307","journal-title":"J Comput Phys"},{"issue":"09","key":"1782_CR15","doi-asserted-by":"publisher","first-page":"2150035","DOI":"10.1142\/S0219876221500353","volume":"18","author":"T Li","year":"2021","unstructured":"Li T, Deng S, Zhang K, Wei H, Wang R, Fan J, Xin J, Yao J (2021) A nonintrusive parametrized reduced-order model for periodic flows based on extended proper orthogonal decomposition. Int J Comput Methods 18(09):2150035","journal-title":"Int J Comput Methods"},{"issue":"17","key":"1782_CR16","doi-asserted-by":"publisher","first-page":"4774","DOI":"10.1002\/nme.6712","volume":"122","author":"J Kneifl","year":"2021","unstructured":"Kneifl J, Grunert D, Fehr J (2021) A nonintrusive nonlinear model reduction method for structural dynamical problems based on machine learning. Int J Numer Meth Eng 122(17):4774\u20134786","journal-title":"Int J Numer Meth Eng"},{"key":"1782_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.114341","volume":"389","author":"C Hoang","year":"2022","unstructured":"Hoang C, Chowdhary K, Lee K, Ray J (2022) Projection-based model reduction of dynamical systems using space-time subspace and machine learning. Comput Methods Appl Mech Eng 389:114341","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1782_CR18","unstructured":"Mohan AT, Gaitonde DV (2018) A deep learning based approach to reduced order modeling for turbulent flow control using lstm neural networks. arXiv preprint arXiv:1804.09269"},{"key":"1782_CR19","first-page":"71","volume-title":"Computer graphics forum","author":"S Wiewel","year":"2019","unstructured":"Wiewel S, Becher M, Thuerey N (2019) Latent space physics: towards learning the temporal evolution of fluid flow. Computer graphics forum. Wiley Online Library, London, pp 71\u201382"},{"key":"1782_CR20","unstructured":"Gonzalez FJ, Balajewicz M (2018) Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems. arXiv preprint arXiv:1808.01346"},{"key":"1782_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.113999","volume":"384","author":"S Lee","year":"2021","unstructured":"Lee S, Jang K, Cho H, Kim H, Shin S (2021) Parametric non-intrusive model order reduction for flow-fields using unsupervised machine learning. Comput Methods Appl Mech Eng 384:113999","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"12","key":"1782_CR22","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1038\/s43588-021-00171-3","volume":"1","author":"T Kadeethum","year":"2021","unstructured":"Kadeethum T, O\u2019Malley D, Fuhg JN, Choi Y, Lee J, Viswanathan HS, Bouklas N (2021) A framework for data-driven solution and parameter estimation of PDES using conditional generative adversarial networks. Nature Comput Sci 1(12):819\u2013829","journal-title":"Nature Comput Sci"},{"key":"1782_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.advwatres.2021.104098","volume":"160","author":"T Kadeethum","year":"2022","unstructured":"Kadeethum T, Ballarin F, Choi Y, O\u2019Malley D, Yoon H, Bouklas N (2022) Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. Adv Water Resour 160:104098","journal-title":"Adv Water Resour"},{"key":"1782_CR24","doi-asserted-by":"crossref","unstructured":"Kadeethum T, Ballarin F, O\u2019Malley D, Choi Y, Bouklas N, Yoon H (2022) Reduced order modeling with barlow twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds. arXiv preprint arXiv:2202.05460","DOI":"10.2172\/2003261"},{"key":"1782_CR25","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1007\/s11071-022-07733-8","volume":"110","author":"H Kim","year":"2022","unstructured":"Kim H, Cheon S, Jeong I, Cho H, Kim H (2022) Enhanced model reduction method via combined supervised and unsupervised learning for real-time solution of nonlinear structural dynamics. Nonlinear Dyn 110:2165\u20132195","journal-title":"Nonlinear Dyn"},{"issue":"45","key":"1782_CR26","doi-asserted-by":"publisher","first-page":"22445","DOI":"10.1073\/pnas.1906995116","volume":"116","author":"K Champion","year":"2019","unstructured":"Champion K, Lusch B, Kutz JN, Brunton SL (2019) Data-driven discovery of coordinates and governing equations. Proc Natl Acad Sci 116(45):22445\u201322451","journal-title":"Proc Natl Acad Sci"},{"key":"1782_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.114181","volume":"388","author":"S Fresca","year":"2022","unstructured":"Fresca S, Manzoni A (2022) Pod-dl-rom: enhancing deep learning-based reduced order models for nonlinear parametrized PDES by proper orthogonal decomposition. Comput Methods Appl Mech Eng 388:114181","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1782_CR28","doi-asserted-by":"publisher","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 399:115436","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1782_CR29","doi-asserted-by":"crossref","unstructured":"He X, Choi Y, Fries WD, Belof J, Chen JS (2022) glasdi: parametric physics-informed greedy latent space dynamics identification. arXiv preprint arXiv:2204.12005","DOI":"10.2139\/ssrn.4108989"},{"key":"1782_CR30","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 GE (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":"1782_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2021.110666","volume":"446","author":"W Chen","year":"2021","unstructured":"Chen W, Wang Q, Hesthaven JS, Zhang C (2021) Physics-informed machine learning for reduced-order modeling of nonlinear problems. J Comput Phys 446:110666","journal-title":"J Comput Phys"},{"issue":"2","key":"1782_CR32","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1002\/aic.690370209","volume":"37","author":"MA Kramer","year":"1991","unstructured":"Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AIChE J 37(2):233\u2013243","journal-title":"AIChE J"},{"key":"1782_CR33","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114"},{"key":"1782_CR34","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv neural Inf Process Syst"},{"issue":"1","key":"1782_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1006\/jcph.2002.7146","volume":"182","author":"M Milano","year":"2002","unstructured":"Milano M, Koumoutsakos P (2002) Neural network modeling for near wall turbulent flow. J Comput Phys 182(1):1\u201326","journal-title":"J Comput Phys"},{"issue":"5786","key":"1782_CR36","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"key":"1782_CR37","doi-asserted-by":"crossref","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp 1096\u20131103","DOI":"10.1145\/1390156.1390294"},{"key":"1782_CR38","unstructured":"Cho K (2013) Simple sparsification improves sparse denoising autoencoders in denoising highly corrupted images. In: International Conference on Machine Learning, PMLR, pp 432\u2013440"},{"key":"1782_CR39","doi-asserted-by":"crossref","unstructured":"Bergmann P, L\u00f6we S, Fauser M, Sattlegger D, Steger C (2018) Improving unsupervised defect segmentation by applying structural similarity to autoencoders. arXiv preprint arXiv:1807.02011","DOI":"10.5220\/0007364503720380"},{"issue":"1","key":"1782_CR40","first-page":"1","volume":"2","author":"J An","year":"2015","unstructured":"An J, Cho S (2015) Variational autoencoder based anomaly detection using reconstruction probability. Spec Lect IE 2(1):1\u201318","journal-title":"Spec Lect IE"},{"key":"1782_CR41","doi-asserted-by":"crossref","unstructured":"Bowman SR, Vilnis L, Vinyals O, Dai AM, Jozefowicz R, Bengio S (2015) Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349","DOI":"10.18653\/v1\/K16-1002"},{"key":"1782_CR42","unstructured":"S\u00f8nderby CK, Raiko T, Maal\u00f8e L, S\u00f8nderby SK, Winther O (2016 Ladder variational autoencoders. Adv Neural Inf Process Syst"},{"key":"1782_CR43","doi-asserted-by":"crossref","unstructured":"Fu H, Li C, Liu X, Gao J, Celikyilmaz A, Carin L (2019) Cyclical annealing schedule: A simple approach to mitigating kl vanishing. arXiv preprint arXiv:1903.10145","DOI":"10.18653\/v1\/N19-1021"},{"key":"1782_CR44","first-page":"19667","volume":"33","author":"A Vahdat","year":"2020","unstructured":"Vahdat A, Kautz J (2020) Nvae: a deep hierarchical variational autoencoder. Adv Neural Inf Process Syst 33:19667\u201319679","journal-title":"Adv Neural Inf Process Syst"},{"issue":"15","key":"1782_CR45","doi-asserted-by":"publisher","first-page":"3780","DOI":"10.1002\/nme.6681","volume":"122","author":"TR Phillips","year":"2021","unstructured":"Phillips TR, Heaney CE, Smith PN, Pain CC (2021) An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion. Int J Numer Meth Eng 122(15):3780\u20133811","journal-title":"Int J Numer Meth Eng"},{"key":"1782_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113379","volume":"372","author":"J Xu","year":"2020","unstructured":"Xu J, Duraisamy K (2020) Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics. Comput Methods Appl Mech Eng 372:113379","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1782_CR47","unstructured":"Spinner T, K\u00f6rner J, G\u00f6rtler J, Deussen O (2018) Towards an interpretable latent space: an intuitive comparison of autoencoders with variational autoencoders. In: IEEE VIS"},{"key":"1782_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113375","volume":"372","author":"M Cheng","year":"2020","unstructured":"Cheng M, Fang F, Pain C, Navon I (2020) An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling. Comput Methods Appl Mech Eng 372:113375","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1782_CR49","unstructured":"Rasmus A, Berglund M, Honkala M, Valpola H, Raiko T (2015) Semi-supervised learning with ladder networks. Adv Neural Inf Process Syst"},{"key":"1782_CR50","unstructured":"White T (2016) Sampling generative networks. arXiv preprint arXiv:1609.04468"},{"key":"1782_CR51","unstructured":"Jang K (2022) Parametric interpolation of flow field based on the proper orthogonal decomposition and unsupervised machine learning."},{"issue":"4","key":"1782_CR52","doi-asserted-by":"publisher","first-page":"616","DOI":"10.2514\/2.2345","volume":"35","author":"T O\u2019Neil","year":"1998","unstructured":"O\u2019Neil T, Strganac TW (1998) Aeroelastic response of a rigid wing supported by nonlinear springs. J Aircr 35(4):616\u2013622","journal-title":"J Aircr"},{"key":"1782_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109787","volume":"423","author":"Y Choi","year":"2020","unstructured":"Choi Y, Boncoraglio G, Anderson S, Amsallem D, Farhat C (2020) Gradient-based constrained optimization using a database of linear reduced-order models. J Comput Phys 423:109787","journal-title":"J Comput Phys"},{"key":"1782_CR54","unstructured":"Choi Y, Oxberry G, White D, Kirchdoerfer T (2019) Accelerating design optimization using reduced order models. arXiv preprint arXiv:1909.11320"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-023-01782-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-023-01782-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-023-01782-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:00:17Z","timestamp":1706752817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-023-01782-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1782"],"URL":"https:\/\/doi.org\/10.1007\/s00366-023-01782-2","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]},"assertion":[{"value":"9 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}