SIAM Journal on Scientific Computing, Volume 42, Issue 2, Page A1402-A1427, January 2020.
Previous work on multilevel techniques for compression and reduction of scientific data is extended to the case of data given on unstructured meshes in two and three dimensions. The centerpiece of the work is a decomposition algorithm which is shown to be optimal, in terms of both storage and operational complexity, applicable to unstructured grids in both two and three dimensions, and which implicitly gives a Riesz basis that can be exploited to reduce the data while maintaining rigorous bounds on the loss incurred. The flexibility of the approach is illustrated by applications to potential flow around an airfoil and the effect of compression on quantities of interest relevant to airfoil design; compression of computational simulation of a nonlinear reaction-diffusion system with special attention given to the problem of time series reduction; and, data from a simulation of magnetically confined plasma in a fusion reactor reduced so as to preserve the electric field computed from the data.

中文翻译：

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科学数据压缩和缩减的多级技术-非结构化案例

SIAM科学计算杂志，第42卷，第2期，第A1402-A1427页，2020年1月。
先前有关压缩和还原科学数据的多级技术的工作扩展到二维和三维非结构网格上给出的数据的情况。这项工作的核心是分解算法，从存储和操作复杂性两方面来看，它是最佳的，适用于二维和三维的非结构化网格，并且隐含地给出了Riesz基础，可以利用该基础来减少数据，同时严格限制损失。这种方法的灵活性体现在翼型周围潜在流动的应用以及压缩对与翼型设计相关的感兴趣量的影响上。压缩非线性反应扩散系统的计算仿真，并特别关注时间序列约简的问题；