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Ideal minimal residual-based proper generalized decomposition for non-symmetric multi-field models – Application to transient elastodynamics in space-time domain

机译:非对称多场模型的理想的基于最小残差的适当广义分解–在时空域中的瞬态弹性动力学中的应用

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摘要

It is now well established that separated representations built with the help of proper generalized decomposition (PGD) can drastically reduce computational costs associated with solution of a wide variety of problems. However, it is still an open question to know if separated representations can be efficiently used to approximate solutions of hyperbolic evolution problems in space-time domain. In this paper, we numerically address this issue and concentrate on transient elastodynamic models. For such models, the operator associated with the space-time problem is non-symmetric and low-rank approximations are classically computed by minimizing the space-time residual in a natural L2 sense, yet leading to non optimal approximations in usual solution norms. Therefore, a new algorithm has been recently introduced by one of the authors and allows to find a quasi-optimal low-rank approximation a priori with respect to a target norm. We presently extend this new algorithm to multi-field models. The proposed algorithm is applied to elastodynamics formulated over space-time domain with the Time Discontinuous Galerkin method in displacement and velocity. Numerical examples demonstrate convergence of the proposed algorithm and comparisons are made with classical a posteriori and a priori approaches.
机译:现已公认,借助适当的广义分解(PGD)构建的分离表示形式可以大大降低与解决各种问题相关的计算成本。但是,要知道在空时域中分离表示是否可以有效地用于近似双曲演化问题的解决方案,仍然是一个悬而未决的问题。在本文中,我们用数值方法解决了这个问题,并专注于瞬态弹性动力学模型。对于此类模型,与时空问题相关的算子是非对称的,低阶近似值是通过在自然L2意义上最小化时空残差来经典计算的,但会导致常规解范数中出现非最佳近似值。因此,作者之一最近引入了一种新算法,该算法允许相对于目标范数先验地找到准最佳低秩近似。我们目前将该新算法扩展到多字段模型。将该算法应用于位移和速度时空不连续伽勒金方法在时空范围内的弹性动力学公式。数值算例表明了该算法的收敛性,并与经典的后验方法和先验方法进行了比较。

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