and guesses for the thresholds, the hysteresis relation is found automatically. Adequate models were identified under varying sample rate and signal noise ratio. Designing a suited library remains the main challenge for the user. It was shown that systemic knowledge can be included in the function library, and that specific adaptations, e.g. with regard to poor data quality, can be integrated. The library should not be larger than necessary, as this complicates the regression and hinders identification of the correct basis functions. The resulting models are sparse and thus easily interpretable. This enables testing for plausibility, which is required for many industrial applications. However, this method may show problems for systems with many discrete states. For each state, a hysteron will be identified, which is multiplied with functions describing the dynamics for that particular state. This results in a solution with a high number of overall terms, which is not according to the idea behind SINDy that the optimal solution is sparse.

It must be noted, that creating the hysteron requires knowledge about the actual threshold value of the state transition. For two-point controlled systems, those threshold values can usually be read from data. The $\epsilon$-range around the threshold values 