关于生物化学网络的基于投影的模型降阶--第一部分：确定性情况
On Projection-Based Model Reduction of Biochemical Networks-- Part I: The Deterministic Case
AI总结：
本文提出基于投影的模型降阶算法，利用广义块对角Gramians保持结构和正性，通过数值示例展示其在生物化学网络模型降阶中的应用。
摘要：
本文探讨了动态系统模型在描述生物化学反应网络中的降阶问题。此类模型固有特性包括稳定性、正性和网络结构。理想情况下，降阶过程应保留这些特性，尽管传统基于投影的方法难以实现。我们提出了一种基于投影的降阶算法，利用广义块对角Gramians来保持结构和正性。提出了两种算法，一种提供更准确的降阶模型，另一种提供更易于模拟的降阶模型。结果通过数值示例进行说明。
英文摘要：
This paper addresses the problem of model reduction for dynamical system models that describe biochemical reaction networks. Inherent in such models are properties such as stability, positivity and network structure. Ideally these properties should be preserved by model reduction procedures, although traditional projection based approaches struggle to do this. We propose a projection based model reduction algorithm which uses generalised block diagonal Gramians to preserve structure and positivity. Two algorithms are presented, one provides more accurate reduced order models, the second provides easier to simulate reduced order models. The results are illustrated through numerical examples.
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。
