Computer-implemented method for safe, dynamic exploration of an input value range of a physical system for training a time series-based model of the physical system, comprising the steps of: defining a safety threshold value, a; initializing by implementing safe initial curves as input values on the physical system, creating an initial regression model and an initial safety model, wherein the regression model and the safety model are each Gaussian processes, wherein the safety model is designed to output a safety value, z, which characterizes a distance between a given curve point, x, and an unknown safety limit and indicates whether the given curve point, x, is safe or unsafe in regard to damage to the physical system; repeatedly carrying out the steps of updating the regression model; updating the safety model; determining a new curve section, T, which comprises a plurality of curve points, x 1 , ..., x m , as input values of the physical system, wherein the new curve section, T, is determined by optimization taking into account a safety condition, wherein the safety condition is that, on the basis of the safety values, z 1 , ..., z m , which were ascertained by means of the safety model for the plurality of curve points of the curve section, the curve section, T, is classified as safe if the probability ∫ z 1,...,zm ≥0 p ( z 1 , ... , z m |τ) dz 1 , ... , z m is greater than 1 - a , wherein the safety threshold value, a , indicates a threshold value in respect of the new curve section being unsafe; implementing the determined new curve section on the physical system and measuring output values; including the output values in the regression model and in the safety model until a predefinable number N of passes have been carried out; and updating and outputting the regression model and the safety model. defining a safety threshold value, a; initializing by implementing safe initial curves as input values on the physical system, creating an initial regression model and an initial safety model, wherein the regression model and the safety model are each Gaussian processes, wherein the safety model is designed to output a safety value, z, which characterizes a distance between a given curve point, x, and an unknown safety limit and indicates whether the given curve point, x, is safe or unsafe in regard to damage to the physical system; repeatedly carrying out the steps of updating the regression model; updating the safety model; determining a new curve section, T, which comprises a plurality of curve points, x 1 , ..., x m , as input values of the physical system, wherein the new curve section, T, is determined by optimization taking into account a safety condition, wherein the safety condition is that, on the basis of the safety values, z 1 , ..., z m , which were ascertained by means of the safety model for the plurality of curve points of the curve section, the curve section, T, is classified as safe if the probability ∫ z 1,...,zm ≥0 p ( z 1 , ... , z m |τ) dz 1 , ... , z m is greater than 1 - a , wherein the safety threshold value, a , indicates a threshold value in respect of the new curve section being unsafe; implementing the determined new curve section on the physical system and measuring output values; including the output values in the regression model and in the safety model until a predefinable number N of passes have been carried out; and updating and outputting the regression model and the safety model. <EOS>