# Equation Discovery for Nonlinear System Identification

## TL;DR

This paper presents a process-based equation discovery method for nonlinear system identification that combines domain knowledge with data-driven modeling, demonstrated through synthetic and real-world case studies.

## Contribution

It introduces an integrated approach for nonlinear system identification that jointly identifies model structure and parameters using process-based modeling.

## Key findings

- Successfully reconstructed model structure and parameters from measured data.
- Demonstrated effectiveness on synthetic and benchmark real-world data.
- Showed advantages over traditional methods in nonlinear system modeling.

## Abstract

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two interlaced parts of modeling that are computer-aided. The first performs computer-aided identification of a model structure composed of elements selected from user-specified domain-specific modeling knowledge, while the second part performs parameter estimation. In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated on two continuous-time case studies. The first case study illustrates the use of the process-based modeling on synthetic data while the second case-study evaluates on measured data for a standard system-identification benchmark. The experimental results clearly demonstrate the ability of process-based modeling to reconstruct both model structure and parameters from measured data.