The invention relates to a computer-implemented method and
system for indirectly measuring, by means of a trained
machine learning model, deformation parameters for a
crash test of a vehicle to be analyzed, having the following work steps: • detecting geometry parameters of a plurality of different vehicles, the parameters being measured by means of a sensor; • calculating a location probability distribution for each component and / or
assembly, in particular each
crash-relevant component and / or
assembly, within a vehicle
configuration space of the vehicle to be analyzed, the location probability distribution being derived from the detected geometry parameters; • determining boundary conditions with respect to the vehicle structure, in particular the arrangement of the components and the rigidities thereof, of the vehicle to be analyzed; • determining the vehicle structure, which is characterized by at least one geometry parameter, of a vehicle to be analyzed on the basis of the boundary conditions and the location probability distribution; • detecting at least one material parameter of individual portions of the vehicle structure, said parameters characterizing the
mass, plastic properties, and elastic properties, in particular moduli of elasticity and flow curves, of the individual portions of the vehicle structure, the at least one geometry parameter and the at least one material parameter forming vehicle data relating to the vehicle to be analyzed; • calculating output data means of the
machine learning model on the basis of input data, the input data being based on the vehicle data and the output data comprising a value for at least one first deformation parameter for the intrusion into the vehicle to be analyzed; and • outputting the at least one value for the first deformation parameter. The invention further relates to a computer-implemented method and a
system for training the
machine learning model.