arXivDaily arXiv每日学术速递 周一至周五更新
北美退伍军人节(Veterans Day),12 日无更新
arXiv 2103.04343math.NAcs.NA
收藏

二维和三维半线性微分方程系统的多线性POD-DEIM模型降阶

Multilinear POD-DEIM model reduction for 2D and 3D semilinear systems of differential equations

Gerhard Kirsten

摘要:

我们主要研究二维和三维耦合非线性偏微分方程的数值解。在一定的假设下的域,我们利用Kronecker结构中产生的标准空间离散的微分算子,并说明如何得到的系统的常微分方程(ODE)可以直接在矩阵或张量的形式。此外,在适当的正交分解(POD)和离散经验插值方法(DEIM)的框架下,我们推导出一个双边和三边模型降阶策略,直接应用到常微分方程系统的矩阵和张量的形式分别。我们讨论了如何集成降阶模型,特别是,如何解决张量值线性系产生的每一个时间步的半隐式时间离散计划。我们说明了所提出的方法的效率,通过比较现有的技术对经典的基准问题,如二维和三维Burgers方程。

英文摘要:

We are interested in the numerical solution of coupled nonlinear partial differential equations (PDEs) in two and three dimensions. Under certain assumptions on the domain, we take advantage of the Kronecker structure arising in standard space discretizations of the differential operators and illustrate how the resulting system of ordinary differential equations (ODEs) can be treated directly in matrix or tensor form. Moreover, in the framework of the proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM) we derive a two- and three-sided model order reduction strategy that is applied directly to the ODE system in matrix and tensor form respectively. We discuss how to integrate the reduced order model and, in particular, how to solve the tensor-valued linear system arising at each timestep of a semi-implicit time discretization scheme. We illustrate the efficiency of the proposed method through a comparison to existing techniques on classical benchmark problems such as the two- and three-dimensional Burgers equation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑