Many real-world processes and phenomena are modeled using systems of ordinary differential equations with parameters. Given such a system, we say that a parameter is globally identifiable if it can be uniquely recovered from input and output data. The main contribution of this paper is to provide the theory, an algorithm, and software for deciding global identifiability. First, we rigorously derive an algebraic criterion for global identifiability (this is an analytic property), which yields a deterministic algorithm. Second, we improve the efficiency by randomizing the algorithm while guaranteeing probability of correctness.

中文翻译：

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差分模型的全局可识别性

许多现实世界的过程和现象都是使用带参数的常微分方程系统建模的。给定这样的系统，如果一个参数可以从输入和输出数据中唯一地恢复，我们就说它是全局可识别的。本文的主要贡献是提供用于确定全局可识别性的理论、算法和软件。首先，我们严格推导出全局可识别性的代数标准（这是一个解析特性），它产生了一个确定性算法。其次，我们通过在保证正确概率的同时随机化算法来提高效率。