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• W2759490286 abstract "This work presents a novel approach to construct surrogate models of parametric differential algebraic equations based on a tensor representation of the solutions. The procedure consists of building simultaneously an approximation given in tensor-train format, for every output of the reference model. A parsimonious exploration of the parameter space coupled with a compact data representation allows alleviating the curse of dimensionality. The approach is thus appropriate when many parameters with large domains of variation are involved. The numerical results obtained for a nonlinear elasto-viscoplastic constitutive law show that the constructed surrogate model is sufficiently accurate to enable parametric studies such as the calibration of material coefficients." @default.
• W2759490286 created "2017-10-06" @default.
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• W2759490286 date "2019-01-28" @default.
• W2759490286 modified "2023-10-13" @default.
• W2759490286 title "Multiple Tensor Train Approximation of Parametric Constitutive Equations in Elasto-Viscoplasticity" @default.
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• W2759490286 doi "https://doi.org/10.3390/mca24010017" @default.
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