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arXiv 1808.02158math.NAcs.NA
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一种新型优化方法用于虚构域方法

A Novel Optimization Approach to Fictitious Domain Methods

University of California, Irvine(加州大学尔湾分校)

Daniel Agress, Patrick Guidotti

AI总结:

本文提出了一种新的优化方法,用于解决边界值问题,该方法避免了其他虚构域方法的已知缺点,包括数据扩展的需要。该方法的核心是将整个边界值问题转化为一个线性约束的优化问题,从而能够更有效地处理非恒定系数、一般几何形状和不同边界条件。

摘要:

本文提出了一种新的方法,用于在所谓的虚构域方法哲学下解决边界值问题,该方法避免了其他虚构域方法已知的缺点,包括需要生成数据的扩展。该新方法称为SSEM(平滑选择嵌入法)的核心特征是将整个边界值问题转化为一个线性约束的优化问题,该问题是在一个更大的、更简单的集合中提出的,该集合包含问题所定义的域,并允许使用直接的离散化方法。所提出的方法本质上是通过选择一个平滑的元素作为扩展但未修改的欠定问题的完整仿射解集中的解,来计算该边界值问题的(离散)扩展。该扩展的实际正则性由解析解的正则性和目标函数的选择决定。数值实验将证明该方法可以稳定地用于高效处理非恒定系数、一般几何形状和不同边界条件,在d=1,2,3维中,并且能够产生可调节(且高精度)的解。

英文摘要:

A new approach to the solution of boundary value problems within the so-called fictitious domain methods philosophy is proposed which avoids well known shortcomings of other fictitious domain methods, including the need to generate extensions of the data. The salient feature of the novel method, which we refer to as SSEM (Smooth Selection Embedding Method), is that it reduces the whole boundary value problem to a linear constraint for an appropriate optimization problem formulated in a larger simpler set containing the domain on which the boundary value problem is posed and which allows for the use of straightforward discretizations. The proposed method in essence computes a (discrete) extension of the solution to the boundary value problem by selecting it as a smooth element of the complete affine family of solutions of the extended, yet unmodified, under-determined problem. The actual regularity of this extension is determined by that of the analytic solution and the choice of obejctive functional. Numerical experiments will demonstrate that it can be stably used to efficiently deal with non-constant coefficients, general geometries, and different boundary conditions in dimensions d=1,2,3 and that it produces solutions of tunable (and high) accuracy.

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。

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