# Web-based Structural Identifiability Analyzer

arXiv: https://arxiv.org/abs/2106.15066 · HTML: https://mlsanity.com/paper/2106.15066/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2021.

## Built on by (most cited first, 3 of 3)

- [Structure is information: structural identifiability mappings for machine learning with partially observed dynamical systems](https://mlsanity.com/paper/2502.04131/) (2025)
- [More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability](https://mlsanity.com/paper/2204.01623/) (2022)
- [CBINNS: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification](https://mlsanity.com/paper/2510.17920/) (2025)

## Abstract (arXiv)

Parameter identifiability describes whether, for a given differential model, one can determine parameter values from model equations. Knowing global or local identifiability properties allows construction of better practical experiments to identify parameters from experimental data. In this work, we present a web-based software tool that allows to answer specific identifiability queries. Concretely, our toolbox can determine identifiability of individual parameters of the model and also provide all functions of parameters that are identifiable (also called identifiable combinations) from single or multiple experiments. The program is freely available at https://maple.cloud/app/6509768948056064.