Version 5.99 of the empirical Gramian framework – emgr – completes a development cycle which focused on parametric model order reduction of gas network models while preserving compatibility to the previous development for the application of combined state and parameter reduction for neuroscience network models. Second, new features concerning empirical Gramian types, perturbation design, and trajectory post-processing, as well as a Python version in addition to the default MATLAB / Octave implementation, have been added. This work summarizes these changes, particularly since emgr version 5.4, see Himpe, 2018 [Algorithms 11(7): 91], and gives recent as well as future applications, such as parameter identification in systems biology, based on the current feature set.

经验格拉米安框架 5.99 版 –肌电图– 完成一个开发周期，重点关注天然气网络模型的参数模型降阶，同时保留与之前开发的神经科学网络模型组合状态和参数缩减应用的兼容性。其次，除了默认的 MATLAB / Octave 实现之外，还添加了有关经验 Gramian 类型、扰动设计和轨迹后处理的新功能，以及 Python 版本。这项工作总结了这些变化，特别是自肌电图版本 5.4，参见Himpe，2018 [算法 11(7): 91]，并给出了最近和未来的应用，例如基于当前功能集的系统生物学中的参数识别。