# Global Identifiability of Differential Models

@article{Hong2020GlobalIO, title={Global Identifiability of Differential Models}, author={Hoon Hong and Alexey Ovchinnikov and Gleb Pogudin and Chee-Keng Yap}, journal={Communications on Pure and Applied Mathematics}, year={2020}, volume={73} }

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…

## 38 Citations

### SIAN: a tool for assessing structural identifiability of parametric ODEs

- Computer ScienceACCA
- 2019

New software SIAN (Structural Identifiability ANalyser) that solves the problem of multiple parameter values that yield the same observed behavior even in the case of continuous noise-free data.

### Differential elimination for dynamical models via projections with applications to structural identifiability

- Computer Science, Mathematics
- 2021

An algorithm is proposed that computes a description of the set of differential-algebraic relations between the input and output variables of a dynamical system model and a new randomized algorithm for assessing structural identifiability of a parameter in a parametric model is built.

### Computing all identifiable functions of parameters for ODE models

- Computer Science, MathematicsSyst. Control. Lett.
- 2021

### Multi-experiment parameter identifiability of ODEs and model theory

- Mathematics, Computer ScienceSIAM Journal on Applied Algebra and Geometry
- 2022

An algorithm to determine the exact number of experiments for multi-experiment local identifiability and obtain an upper bound that is off at most by one for the number of ExperimentsBound, a Monte Carlo randomized version of the algorithm with a polynomial arithmetic complexity.

### Computing all identifiable functions for ODE models

- Mathematics, Computer ScienceArXiv
- 2020

This work gives an algorithm that not only finds all identifiable functions of parameters but also provides an upper bound for the number of experiments to be performed to identify these functions.

### Parameter identifiability and input-output equations

- Mathematics, EconomicsArXiv
- 2020

Identifiability implies input-output identifiability; these notions coincide if the model does not have rational first integrals; and the field of input- Output identifiable functions is generated by the coefficients of a "minimal" characteristic set of the corresponding differential ideal.

### Observability and Structural Identifiability of Nonlinear Biological Systems

- MathematicsComplex.
- 2019

This review article is threefold: to serve as a tutorial on observability and structural identifiability of nonlinear systems, using the differential geometry approach for their analysis; to review recent advances in the field; and to identify open problems and suggest new avenues for research in this area.

### Computing input-output projections of dynamical models with applications to structural identifiability

- Computer Science, MathematicsArXiv
- 2021

An algorithm is proposed that computes a description of the set of differential-algebraic relations between the input and output variables of a dynamical system model and a new randomized algorithm for assessing structural identifiability of a parameter in a parametric model is built.

### A priori identifiability: An overview on definitions and approaches

- MathematicsAnnu. Rev. Control.
- 2020

### Input-output equations and identifiability of linear ODE models

- MathematicsIEEE Transactions on Automatic Control
- 2022

This paper proves identifiability of the coefficients of input-output equations for types of differential models that often appear in practice, such as linear models with one output and linear compartment models in which, from each compartment, one can reach either a leak or an input.

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