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On the equivalence of dynamically orthogonal and bi-orthogonal methods: Theory and numerical simulations

机译:关于动态正交和双正交方法的等价性:理论与数值模拟

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The Karhunen-Lòeve (KL) decomposition provides a low-dimensional representation for random fields as it is optimal in the mean square sense. Although for many stochastic systems of practical interest, described by stochastic partial differential equations (SPDEs), solutions possess this low-dimensional character, they also have a strongly time-dependent form and to this end a fixed-in-time basis may not describe the solution in an efficient way. Motivated by this limitation of standard KL expansion, Sapsis and Lermusiaux (2009) [26] developed the dynamically orthogonal (DO) field equations which allow for the simultaneous evolution of both the spatial basis where uncertainty 'lives' but also the stochastic characteristics of uncertainty. Recently, Cheng et al. (2013) [28] introduced an alternative approach, the bi-orthogonal (BO) method, which performs the exact same tasks, i.e. it evolves the spatial basis and the stochastic characteristics of uncertainty. In the current work we examine the relation of the two approaches and we prove theoretically and illustrate numerically their equivalence, in the sense that one method is an exact reformulation of the other. We show this by deriving a linear and invertible transformation matrix described by a matrix differential equation that connects the BO and the DO solutions. We also examine a pathology of the BO equations that occurs when two eigenvalues of the solution cross, resulting in an instantaneous, infinite-speed, internal rotation of the computed spatial basis. We demonstrate that despite the instantaneous duration of the singularity this has important implications on the numerical performance of the BO approach. On the other hand, it is observed that the BO is more stable in nonlinear problems involving a relatively large number of modes. Several examples, linear and nonlinear, are presented to illustrate the DO and BO methods as well as their equivalence.
机译:Karhunen-LòEVE(KL)分解为随机字段提供了低维表示,因为它在平均方形感中是最佳的。虽然对于许多实际兴趣的随机系统,但是由随机部分微分方程(SPDES)描述,溶液具有这种低维特征,它们还具有强时依赖性的形式,并且在此期限内可能无法描述以有效的方式解决方案。通过这种标准KL扩展的这种限制,SASASIS和Lermusiaux(2009)[26]开发了动态正交(DO)场方程,其允许同时演变的空间基础,其中不确定的“生命”,而且是不确定的随机特征。最近,Cheng等人。 (2013)[28]介绍了一种替代方法,双正交(BO)方法,其执行完全相同的任务,即它发展了空间基础和不确定度的随机特征。在目前的工作中,我们检查了两种方法的关系,理论上证明并以数字方式证明它们的等价,从而在一种方法是另一种方法的精确重构的意义上。我们通过推导通过连接BO和DO解决方案的矩阵微分方程描述的线性和可逆的变换矩阵来显示这一点。我们还研究了当溶液交叉的两个特征值时发生的BO方程的病理学,导致计算的空间基础的瞬时,无限速度,内部旋转。我们证明,尽管奇异的瞬时持续时间,但这对BO方法的数值表现具有重要意义。另一方面,观察到博在涉及相对大量模式的非线性问题中更稳定。提出了几个例子,线性和非线性以说明DO和BO方法以及它们的等价。

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