doi:10.1155/2018/5608286engIbanez, R.Abisset-Chavanne, E.Ammar, A.Gonzalez, D.Cueto, E.Huerta, A.Duval, J.L.Chinesta, F.A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized DecompositionART-2018-109051Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.2018http://zaguan.unizar.es/record/7596210.1155/2018/5608286http://zaguan.unizar.es/record/75962oai:zaguan.unizar.es:75962info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-Rinfo:eu-repo/grantAgreement/ES/MINECO/DPI2015-72365-EXPThis project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 675919-AdMoReinfo:eu-repo/grantAgreement/EC/H2020/675919/EU/Empowered decision-making in simulation-based engineering: Advanced Model Reduction for real-time, inverse and optimization in industrial problems/AdMoReinfo:eu-repo/grantAgreement/ES/DGA/T24-17RComplexity 18, 5608286 (2018), [11 pp]byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess