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00925njm 22002777a 4500 dc Hernández Ortega, Joaquín Alberto author Bravo Martínez, José Raúl author Ares de Parga Regalado, Sebastian author 2024-01 We propose a method for finding optimal quadrature/cubature rules with positive weights for parameterized functions in 1D, 2D or 3D spatial domains. The method takes as starting point the values of the functions at the Gauss points of a finite element (FE) mesh of the spatial domain for a representative sample of input parameters, and then construct an elementwise continuous orthogonal basis for such functions using the truncated Singular Value Decomposition (SVD) along with element polynomial fitting. To avoid possible memory bottlenecks in computing the SVD, we propose a Sequential Randomized SVD (SRSVD) in which the matrix is provided in a column-partitioned format, and which uses randomization to accelerate the processing of each individual block. After computing the basis functions, the method determines an exact integration rule for such functions, featuring as many points as functions, and in which the points are selected among the Gauss points of the FE mesh. Finally, the desired optimal rule is obtained by an sparsification process in which the algorithm zeroes one weight at a time while readjusting the positions and weights of the remaining points so that the constraints of the problem are satisfied. We apply this methodology to multivariate polynomials in cartesian domains to demonstrate that the method is indeed able to produce optimal rules – i.e., Gauss product rules –, and to a 2-parameters, 3D sinusoidal–exponential function to illustrate the use of the SRSVD in scenarios in which the standard SVD cannot handle the operation because of memory limitations. Lastly, the fact that the method does not require the analytical expression of the integrand functions – just their values at the FE Gauss points – makes it suitable for dealing with the so-called hyperreduction of parameterized finite element models. We exemplify this by showing its performance in the derivation of low-dimensional surrogate models in the context of the multiscale FE method. The Matlab source codes of both the CECM and the SRSVD, along with the scripts for launching the numerical tests, are openly accessible in the public repository https://github.com/Rbravo555/CECM-continuous-empirical-cubature-method. This work is sponsored in part by the Spanish Ministry of Economy and Competitiveness, through the Severo Ochoa Programme for Centres of Excellence in R&D (CEX2018-000797-S)”. The authors acknowledge the support of the European High-Performance Computing Joint Undertaking (JU) under grant agreement No. 955558 (the JU receives, in turn, support from the European Union’s Horizon 2020 research and innovation programme and Spain, Germany, France, Italy, Poland, Switzerland, Norway), as well as the R&D project PCI2021-121944, financed by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”. J.A. Hernández expresseses gratitude by the support of, on the one hand, the “MCIN/AEI/10.13039/501100011033/y por FEDER una manera de hacer Europa” (PID2021-122518OBI00), and, on the other hand, the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 952966 (project FIBREGY). Lastly, both J.R. Bravo and S. Ares de Parga acknowledge the Departament de Recerca i Universitats de la Generalitat de Catalunya for the financial support through doctoral grants FI-SDUR 2020 and FI SDUR-2021, respectively. Peer Reviewed Postprint (author's final draft) Àrees temàtiques de la UPC::Enginyeria dels materials Finite element method Decomposition method Gaussian distribution Empirical Cubature Method Hyperreduction Reduced-order modeling Singular Value Decomposition Quadrature Elements finits, Mètode dels Descomposició, Mètode de Distribució de Gauss CECM: a continuous empirical cubature method with application to the dimensional hyperreduction of parameterized finite element models