# Parameter identifiability, parameter estimation and model prediction for differential equation models

arXiv: https://arxiv.org/abs/2405.08177 · HTML: https://mlsanity.com/paper/2405.08177/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2024.

## Built on by (most cited first, 2 of 2)

- [Analysis of Adam Algorithms for Stochastic Dynamic Systems](https://mlsanity.com/paper/2606.28879/) (2026)
- [Learning functional components of PDEs from data using neural networks](https://mlsanity.com/paper/2602.13174/) (2026)

## Abstract (arXiv)

Interpreting data with mathematical models is an important aspect of real-world industrial and applied mathematical modeling. Often we are interested to understand the extent to which a particular set of data informs and constrains model parameters. This question is closely related to the concept of parameter identifiability, and in this article we present a series of computational exercises to introduce tools that can be used to assess parameter identifiability, estimate parameters and generate model predictions. Taking a likelihood-based approach, we show that very similar ideas and algorithms can be used to deal with a range of different mathematical modeling frameworks. The exercises and results presented in this article are supported by a suite of open access codes that can be accessed on GitHub.