Computer-implemented method for determining a physical shape having a predefined physical target property, wherein the method comprises: providing a physical model and capturing shape data for the physical model and/or providing shape data of a physical model for the purpose of respectively creating a shape data record which identifies locations at or on a physical shape which corresponds to the shape data (S1, S1'); capturing the target property for the physical shape which corresponds to the shape data record (S2, S2'); determining sensitivity values for a plurality of locations at or on the physical shape on the basis of the shape data record for the purpose of generating a sensitivity landscape for the respective shape data record (S3'), wherein a sensitivity value for a location at or on the physical shape indicates a degree of change for the change in the target property of the physical shape if the physical shape changes in the region of the location as a result of a change in the shape data record; using a machine learning method to produce a calculation device which assigns a sensitivity landscape to a respective shape data record (S4'), wherein the machine learning is carried out on the basis of a plurality of shape data records and the sensitivity values captured for this purpose; calculating a sensitivity landscape on the basis of a shape data record for a physical shape to be determined with the aid of the calculation device (S5, S5'); and changing the shape data record for the physical shape to be determined on the basis of the calculated sensitivity landscape in such a manner that the predefined physical target property changes in the direction of a predetermined value (S6, S6'); and wherein the calculation device carries out pattern recognition on shape data records and outputs a sensitivity landscape. providing a physical model and capturing shape data for the physical model and/or providing shape data of a physical model for the purpose of respectively creating a shape data record which identifies locations at or on a physical shape which corresponds to the shape data (S1, S1'); capturing the target property for the physical shape which corresponds to the shape data record (S2, S2'); determining sensitivity values for a plurality of locations at or on the physical shape on the basis of the shape data record for the purpose of generating a sensitivity landscape for the respective shape data record (S3'), wherein a sensitivity value for a location at or on the physical shape indicates a degree of change for the change in the target property of the physical shape if the physical shape changes in the region of the location as a result of a change in the shape data record; using a machine learning method to produce a calculation device which assigns a sensitivity landscape to a respective shape data record (S4'), wherein the machine learning is carried out on the basis of a plurality of shape data records and the sensitivity values captured for this purpose; calculating a sensitivity landscape on the basis of a shape data record for a physical shape to be determined with the aid of the calculation device (S5, S5'); and changing the shape data record for the physical shape to be determined on the basis of the calculated sensitivity landscape in such a manner that the predefined physical target property changes in the direction of a predetermined value (S6, S6'); and wherein the calculation device carries out pattern recognition on shape data records and outputs a sensitivity landscape. <EOS>