A method for post-adaption of an at least partially data-based function model which corresponds to a sum of a basis function model, e.g., a data-based basis function model, and an additive fault model, includes: providing the basis function model; recording training data; ascertaining the data-based additive fault model based on difference training data which represent differences between the measured values of the training data and the function values of the data-based basis function model at the measuring points of the training data; and modifying the training data and/or the additive fault model so that function values of the data-based function model remain within a predefined adaption range.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for post-adaption of an at least partially data-based function model which corresponds to a sum of a data-based basis function model and an additive fault model, comprising: providing the data-based basis function model; recording training data; ascertaining the additive fault model based on difference training data which represent differences between measured values of the training data and the function values of the data-based basis function model at measuring points of the training data; and modifying at least one of the training data and the additive fault model so that function values of the data-based basis function model remain within a predefined adaption range. 2. The method as recited in claim 1, wherein the measured values of the training data are modified by limitation to one of a predefined upper limiting value or a predefined lower limiting value. 3. The method as recited in claim 2, wherein the one of the predefined upper limiting value or the predefined lower limiting value depends on the measuring point assigned to the corresponding measured value, the measuring point being dependent on a predefined limiting model. 4. The method as recited in claim 2, wherein a limiting function is applied to the additive fault model before the additive fault model is added to the data-based basis function model. 5. The method as recited in claim 4, wherein the application of the limiting function causes the maximal function value of the additive fault model and the minimal function value of the additive fault model to form the upper and lower limiting values of the adaption range. 6. The method as recited in claim 4, wherein the limiting function corresponds to one of a Fermi function, a smoothstep function or a smootherstep function. 7. The method as recited in claim 4, wherein the limiting function corresponds to a linear function having a slope of 1 within an adaption exclusion range situated within the adaption range. 8. The method as recited in claim 4, wherein the basis function model is at least partially designed as a Gaussian process model which is defined by predefined hyperparameters and node data. 9. An arithmetic unit configured for post-adaption of an at least partially data-based function model which corresponds to a sum of a data-based basis function model and an additive fault model, comprising: means for providing the data-based basis function model; means for recording training data; means for ascertaining the additive fault model based on difference training data which represent differences between measured values of the training data and the function values of the data-based basis function model at measuring points of the training data; and means for modifying at least one of the training data and the additive fault model so that function values of the data-based basis function model remain within a predefined adaption range. 10. A non-transitory computer-readable data storage medium storing a computer program having program codes which, when executed on a computer, performs a method for post-adaption of an at least partially data-based function model which corresponds to a sum of a data-based basis function model and an additive fault model, the method comprising: providing the data-based basis function model; recording training data; ascertaining the additive fault model based on difference training data which represent differences between measured values of the training data and the function values of the data-based basis function model at measuring points of the training data; and modifying at least one of the training data and the additive fault model so that function values of the data-based basis function model remain within a predefined adaption range.