still popular because it allows to include domain-specific knowledge, constraints, and to achieve transparency. The presented work provides an example of combining explicit knowledge with a flexible data-driven approach in order to estimate hysteresis behavior.

A traditional approach of obtaining a system model combines physical laws and technical parameters to so called white-box-models. Alternatively, grey-box-models can be used, which have a fixed model structure but chosen parameters are tuned using measurement data. Methods from machine learning partially abandon fixed model structures, like in the case of neural networks. These approaches do not provide a clear transparency, which implies a need for empirical validation. Furthermore, pure data-driven methods require for extensive data for all considered situations and operation points. However, due to high costs, efforts or dangers it is not always feasible to conduct real experiments.

This article proposes an extension of the Sparse Identification of Nonlinear Dynamics (SINDy) framework developed and presented by S. Brunton and J.N. Kutz in their prominent publication [@BruntonSINDy.2016]. Adding the robust identification of hysteresis behavior in hybrid systems holds the potential to identify all discrete states of a hysteresis controlled system and build a nonlinear model for the dynamics on all states in one step. 