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. Secondarily, 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.
翻译:经验格拉姆框架 -- -- " 综合 " -- -- 的5.99版完成了一个发展周期,重点是气体网络模型的参数模型减少,同时保持与以前在神经科学网络模型中应用综合状态和参数减少的开发的兼容性。第二,增加了关于经验格拉姆类型、扰动设计和轨迹后处理以及除默认 MATLAB/Octave 实施外的Python版本的新特征,并增加了一个Python版本。这项工作概述了这些变化,特别是自“复合”5.4版(见Himpe,2018年[Agorithms 11(7):91]以来的这些变化,并提供了最新的和今后的应用,例如根据目前的特征集在系统生物学中进行参数识别。