论文标题
比较（基于经验的基于克拉米亚的）模型订单还原算法
Comparing (Empirical-Gramian-Based) Model Order Reduction Algorithms
论文作者
论文摘要
在这项工作中，基于经验的模型减少方法：可怜的人的截短平衡实现，经验近似平衡，经验优势子空间，经验平衡的截断和经验平衡的增长，在非参数和两个参数变体中比较了$ limate y_ $ $ $ $ $ libe $ l l l l ly l l _ $ l l l l ly_2 $ l_ \ infty $，hardy $ h_2 $，$ h_ \ infty $，Hankel，Hilbert-Schmidt-Hankel，用于热块模型还原基准的变体的修改后的诱导原始和修改的诱导双标准。该比较是通过新的元估计量来进行的，用于模型可降低，称为MorScore。
In this work, the empirical-Gramian-based model reduction methods: Empirical poor man's truncated balanced realization, empirical approximate balancing, empirical dominant subspaces, empirical balanced truncation, and empirical balanced gains are compared in a non-parametric and two parametric variants, via ten error measures: Approximate Lebesgue $L_0$, $L_1$, $L_2$, $L_\infty$, Hardy $H_2$, $H_\infty$, Hankel, Hilbert-Schmidt-Hankel, modified induced primal, and modified induced dual norms, for variants of the thermal block model reduction benchmark. This comparison is conducted via a new meta-measure for model reducibility called MORscore.
