METHOD FOR VALIDATING OR VERIFYING A TECHNICAL SYSTEM

Method for verifying and/or validating whether a technical system (40) fulfills a desired criterion, wherein the technical system (40) emits output signals based on input signals supplied to the technical system (40), wherein the method comprises the steps of:a. Obtaining models (M1,M2,MC) for a plurality of components (S1,S2,SC) comprised by the technical system (40), wherein a connection between the obtained models characterizes which component passes which signal to which other component;b. Obtaining a plurality of validation measurements, wherein a validation measurement comprises a measurement input and a measurement output (p1,p2,pC-1,pC), wherein the measurement output is obtained from a component (S1,S2,SC) of the technical system (40) for the measurement input if the measurement input is provided to the component (S1,S2,SC);c. For each component (S1,S2,SC), training a machine learning model (V1,V2,VC) to predict measurement outputs (p1,p2,pC-1,pC) of the respective component (S1,S2,SC) based on inputs of the respective component, wherein at least parts of the validation measurements are used as training dataset and wherein the machine learning model (V1,V2,VC) corresponds to the model (M1,M2,MC) obtained for the component;d. Obtaining first test outputs (qM,C) from a last model (MC) based on test inputs (q°), wherein the first test outputs (qM,C) are obtained by propagating the test inputs (q°) through the connection of models;e. Determining, second test outputs (qV,C) from the machine learning model (VC) corresponding to the last model and based on the test inputs (q0) of the models (M1,M2,MC), wherein the second test outputs (qV,C) are obtained by propagating the test inputs (q0) through a connection of the machine learning models (V1,V2,VC), wherein the connection of the machine learning models (V1,V2,VC) is according to the connection of the models (M1,M2,MC) the respective machine learning models (V1,V2,VC) correspond to;f. Determining a discrepancy (d), wherein the discrepancy (d) characterizes a difference between a distribution of first test outputs (qM,C) determined from the last model (MC) and a distribution of second test outputs (qV,C) determined by the machine learning model (VC) corresponding to the last model (MC);g. Verifying and/or validating whether the technical system (40) fulfills the criterion, wherein verifying and/or validating is characterized by maximizing a probability of a distribution of measurement outputs (pC) of a l