Method for providing training data for a machine learning (ml) model for predicting the behavior of a technical system

A non-transitory machine-readable data carrier on which is stored a computer program including machine-readable instructions for providing training data for a machine learning (ML) model for predicting behavior of a technical system, the instructions, when executed by one or more computers and/or computer instances, cause the one or more computers and/or computer instances to perform the following steps:

• generating a first data set, wherein the first data set contains simulation data for the technical system;
• generating a second data set, wherein the second data set contains prototype data of the technical system;
• generating a third data set by combining the first data set with the second data set;
• training the ML model based on the third data set;
• generating a fourth data set as first input data based on the third data set by maximizing an information function, to obtain a first feature combination as input data for the technical system;
• measuring the first feature combination for the prototype data of the technical system, to obtain a fifth data set;
• and
• adding the fifth data set as output data and the generated fourth data set as first input data to the third data set as training data for the ML model.

One or more computers and/or computer instances equipped by a non-transitory machine-readable data carrier on which is stored a computer program including machine-readable instructions for providing training data for a machine learning (ML) model for predicting behavior of a technical system, the instructions, when executed by the one or more computers and/or computer instances, cause the one or more computers and/or computer instances to perform the following steps:

• generating a first data set, wherein the first data set contains simulation data for the technical system;
• generating a second data set, wherein the second data set contains prototype data of the technical system;
• generating a third data set by combining the first data set with the second data set;
• training the ML model based on the third data set;
• generating a fourth data set as first input data based on the third data set by maximizing an information function, to obtain a first feature combination as input data for the technical system;
• measuring the first feature combination for the prototype data of the technical system, to obtain a fifth data set;
• and
• adding the fifth data set as output data and the generated fourth data set as first input data to the third data set as training data for the ML model.