# System Identification with Copula Entropy

arXiv: https://arxiv.org/abs/2304.12922 · HTML: https://mlsanity.com/paper/2304.12922/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Machine Learning of Linear Differential Equations using Gaussian Processes](https://mlsanity.com/paper/1701.02440/) (2017)
- [Benchmarking sparse system identification with low-dimensional chaos](https://mlsanity.com/paper/2302.10787/) (2023)
- [Estimating Transfer Entropy via Copula Entropy](https://mlsanity.com/paper/1910.04375/) (2019)
- [Variable Selection with Copula Entropy](https://mlsanity.com/paper/1910.12389/) (2019)
- [Identifying Time Lag in Dynamical Systems with Copula Entropy based Transfer Entropy](https://mlsanity.com/paper/2301.06037/) (2023)

## Abstract (arXiv)

Identifying differential equation governing dynamical system is an important problem with wide applications. Copula Entropy (CE) is a mathematical concept for measuring statistical independence in information theory. In this paper we propose a method for identifying differential equation of dynamical systems with CE. The problem is considered as a variable selection problem and solved with the previously proposed CE-based method for variable selection. The proposed method composed of two components: the difference operator and the CE estimator. Since both components can be done non-parametrically, the proposed method is therefore model-free and hyperparameter-free. The simulation experiment with the 3D Lorenz system verified the effectiveness of the proposed method.