A computerized method for creating a function model based on a non-parametric, data-based model, e.g., a Gaussian process model, includes: providing training data including measuring points having one or multiple input variables, the measuring points each being assigned an output value of an output variable; providing a basic function; modifying the training data with the aid of difference formation between the function values of the basic function and the output values at the measuring points of the training data; creating the data-based model based on the modified training data; and providing the function model as a function of the data-based model and the basic function.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computerized method for generating a function model based on a non-parametric, data-based model, comprising: providing training data including measuring points having at least one input variable, the measuring points each being assigned an output value of an output variable; providing a basic function; modifying the training data with the aid of difference formation between the function values of the basic function and the output values at the measuring points of the training data; generating the data-based model based on the modified training data; and generating the function model as a function of the data-based model and the basic function. 2. The method as recited in claim 1, wherein a validity range is predefined, the function values being determined with the aid of the function model within the validity range and with the aid of one of the basic function or another predefined, constant function outside of the validity range. 3. The method as recited in claim 2, wherein the validity range is ascertained as a bounding box from the training data, the bounding box defining an input variable space with axis-parallel edges, and all measuring points of the training data being located within the bounding box. 4. The method as recited in claim 3, wherein the basic function is predefined as (i) one of a linear or constant function and (ii) one of a parametric model, a non-parametric model, or a physical model. 5. The method as recited in claim 3, wherein the basic function is one of (i) predefined as a D-dimensional hyperplane, or (ii) defined by predefining target values at target value points, the basic function being defined by a number D+1 target values for a number of D input variables, the basic function corresponding to a mean value function which results from a multi-linear interpolation between the target variables. 6. The method as recited in claim 3, wherein the another predefined function corresponds to a constant function f(x)=f(min(xmax, max(xmin,x))) for a measuring point x, the operators “max” and “min” being used dimensionally and the vectors xmax and xmin including the upper and lower margins of the input variable space for each input variable. 7. A computing device for generating a function model based on a non-parametric, data-based model, the computing device comprising: a processor configured to: provide training data including measuring points having at least one input variable, the measuring points each being assigned an output value of an output variable; provide a basic function; modify the training data with the aid of difference formation between the function values of the basic function and the output values at the measuring points of the training data; generate the data-based model based on the modified training data; and generate the function model as a function of the data-based model and the basic function. 8. 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 generating a function model based on a non-parametric, data-based model, the method comprising: providing training data including measuring points having at least one input variable, the measuring points each being assigned an output value of an output variable; providing a basic function; modifying the training data with the aid of difference formation between the function values of the basic function and the output values at the measuring points of the training data; generating the data-based model based on the modified training data; and generating the function model as a function of the data-based model and the basic function.