We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and discover a method for speeding up the Gr\"obner basis computation by using a weighted ordering. Our method explores the structure of the ODE model to generate weight assignments for each variable. We provide empirical results that show improvement across different symbolic computing frameworks

翻译：我们考虑了在普通差异方程模型(ODE)的参数可识别性问题中产生的一特定类别的多元系统,并发现了通过使用加权顺序加速“Gr\'obner”基准计算的方法。我们的方法探索了ODE模型的结构以产生每个变量的权重分配。我们提供了实验结果,表明不同符号计算框架的改进。