Global Identifiability of Differential Models
By Hoon Hong, Alexey Ovchinnikov, Gleb Pogudin, Chee-Keng Yap. Communications on Pure and Applied Mathematics, 2018
96 citations recorded by the source corpus.
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 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 the probability of correctness. With our new algorithm, we can tackle problems that could not be tackled before. A software based on the algorithm (called SIAN) is available at https://github.com/pogudingleb/SIAN. © 2020 Wiley Periodicals LLC
Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/04e44cb9155cb8015cf71fab7fb156d4bd9a3389
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In Computational Theory and Mathematics
Citations in the corpus
- On structural and practical identifiability (2021, methodology): “Given the multitude of recently developed methods [13, 16, 27, 32], we consider the file of identifying structurally non-identifiable parameters as closed.”