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Comparison of POD reduced order strategies for the nonlinear 2D shallow water equations

机译:非线性二维浅水方程的POD降阶策略比较

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摘要

This paper introduces tensorial calculus techniques in the framework of POD to reduce the computational complexity of the reduced nonlinear terms. The resulting method, named tensorial POD, can be applied to polynomial nonlinearities of any degree p. Such nonlinear terms have an online complexity of O(kp+1), where k is the dimension of POD basis and therefore is independent of full space dimension. However, it is efficient only for quadratic nonlinear terms because for higher nonlinearities, POD model proves to be less time consuming once the POD basis dimension k is increased. Numerical experiments are carried out with a two-dimensional SWE test problem to compare the performance of tensorial POD, POD, and POD/discrete empirical interpolation method (DEIM). Numerical results show that tensorial POD decreases by 76x the computational cost of the online stage of POD model for configurations using more than 300,000 model variables. The tensorial POD SWE model was only 2 to 8x slower than the POD/DEIM SWE model but the implementation effort is considerably increased. Tensorial calculus was again employed to construct a new algorithm allowing POD/DEIM SWE model to compute its offline stage faster than POD and tensorial POD approaches. Copyright (c) 2014 John Wiley & Sons, Ltd.
机译:本文介绍了在POD框架中的张量微积分技术,以减少简化的非线性项的计算复杂性。所得的方法称为张量POD,可以应用于任何度p的多项式非线性。这样的非线性项的在线复杂度为O(kp + 1),其中k是POD基的维数,因此与全空间维数无关。但是,它仅对二次非线性项有效,因为对于更高的非线性度,一旦POD基本尺寸k增加,POD模型将证明耗时更少。使用二维SWE测试问题进行了数值实验,以比较张量POD,POD和POD /离散经验插值方法(DEIM)的性能。数值结果表明,对于使用超过300,000个模型变量的配置,张量POD降低了在线POD模型在线阶段计算成本的76倍。张量POD SWE模型仅比POD / DEIM SWE模型慢2到8倍,但是实现工作却大大增加了。再次使用张量演算来构建新算法,该算法允许POD / DEIM SWE模型比POD和张量POD方法更快地计算其离线阶段。版权所有(c)2014 John Wiley&Sons,Ltd.

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