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arXiv 1402.6402math.NAcs.NA
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模型降阶与网格细化

Model reduction and mesh refinement

University of Minnesota(明尼苏达大学)

Panagiotis Stinis

AI总结:

本文提出利用降阶模型进行网格细化的新算法,通过控制活动转移率来减少有限分辨率带来的误差,应用于Burgers方程和Schrödinger方程检测奇点。

摘要:

在近期工作中,我们提出了一种新颖的网格细化算法,该算法利用降阶模型。具体而言,降阶模型用于监控活动(如质量、能量)从大尺度向小尺度的转移。当活动转移速率超过预设容忍度时,算法相应细化网格。该算法应用于无粘Burgers方程和聚焦Schrödinger方程以检测奇点。本文提供了一个简单的证明,说明使用降阶模型进行网格细化是合理的。我们通过展示通过控制活动转移速率来控制因有限分辨率不可避免产生的误差,从而证明其合理性。

英文摘要:

In recent work we have presented a novel algorithm for mesh refinement which utilizes a reduced model. In particular, the reduced model is used to monitor the transfer of activity (e.g. mass, energy) from larger to smaller scales. When the activity transfer rate exceeds a prescribed tolerance, the algorithm refines the mesh accordingly. The algorithm was applied to the inviscid Burgers and focusing Schrödinger equations to detect singularities. We offer here a simple proof of why the use of a reduced model for mesh refinement is a good idea. We do so by showing that by controlling the transfer of activity rate one controls the error caused inevitably by a finite resolution.

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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