Computer-implemented design method for generating structure data sets (SD) specifying a technical product (TP), wherein a) for a multiplicity of design variants (KV) of the technical product (TP), in each case a training structure data set (SDT) specifying the respective design variant (KV) and also a training quality value (QT1, QT2, QT3) quantifying a predefined design criterion (K1, K2, K3) are read in as training data (TD), b) a Bayesian neural network (BNN) is trained, on the basis of the training data (TD), to determine an associated quality value (Q) together with an associated uncertainty indication (UC) on the basis of a structure data set (SD, SSD), c) a multiplicity of synthetic structure data sets (SSD) are generated and fed into the trained Bayesian neural network (BNN), d) a quality value (Q) with an associated uncertainty indication (UC) is generated for each of the synthetic structure data sets (SSD) by the trained Bayesian neural network (BNN), e) the generated uncertainty indications (UC) are compared with a predefined reliability indication (REL) and one of the synthetic structure data sets (SD) is selected depending thereon, f) the selected structure data set (SD) is output to a production system (PS) for the purpose of producing the technical product (TP), and g) the technical product (TP) is produced or processed by the production system (PS) on the basis of the selected structure data set (SD), wherein the production system (PS) is a manufacturing installation, a robot or a machine tool, and wherein a respective quality value (Q) indicates to what extent a requirement made of the technical product (TP) and concerning a design criterion is satisfied. a) for a multiplicity of design variants (KV) of the technical product (TP), in each case a training structure data set (SDT) specifying the respective design variant (KV) and also a training quality value (QT1, QT2, QT3) quantifying a predefined design criterion (K1, K2, K3) are read in as training data (TD), b) a Bayesian neural network (BNN) is trained, on the basis of the training data (TD), to determine an associated quality value (Q) together with an associated uncertainty indication (UC) on the basis of a structure data set (SD, SSD), c) a multiplicity of synthetic structure data sets (SSD) are generated and fed into the trained Bayesian neural network (BNN), d) a quality value (Q) with an associated uncertainty indication (UC) is generated for each of the synthetic structure data sets (SSD) by the trained Bayesian neural network (BNN), e) the generated uncertainty indications (UC) are compared with a predefined reliability indication (REL) and one of the synthetic structure data sets (SD) is selected depending thereon, f) the selected structure data set (SD) is output to a production system (PS) for the purpose of producing the technical product (TP), and g) the technical product (TP) is produced or processed by the production system (PS) on the basis of the selected structure data set (SD), wherein the production system (PS) is a manufacturing installation, a robot or a machine tool, and wherein a respective quality value (Q) indicates to what extent a requirement made of the technical product (TP) and concerning a design criterion is satisfied. <EOS>