Systematic determination of representative patterns for serial products

Abstract

To provide a computer-implemented method of determining representative parameterized simulation models of a parameterizable analytical simulation model for a product, in particular a steer-by-wire steering system and / or a steering system for highly automated driving.SOLUTION: A method disclosed herein comprises determining a predetermined number of parameterized simulation models based on a dissimilarity metric, the dissimilarity metric defining a distance between two respective parameterized simulation models, where a plurality of representative parameterized simulation models is obtained as a result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] Conventional technology Serially produced products, such as steering systems, especially mass-produced products, are subject to variations in product parameters, such as friction, elasticity and / or inertia, due to manufacturing tolerances and precision.

[0002] Furthermore, serially produced products are subject to additional parameter variations due to, for example, wear and / or aging due to environmental influences. All value ranges and value combinations that occur in practice of the parameter variations of serially produced products form the so-called "operational design domain" (ODD), or roughly in German "Betriebsbereich fuer die Auslegung", of a serially produced product. [Background technology]

[0003] Typically, a simulation model (or model for short) of a product includes only its essential or relevant characteristics, and therefore, deviations between the real behavior and the modeled behavior occur due to simplifications. Parameter variations within the ODD cause additional deviations for each individual serially manufactured product between its real behavior and the corresponding simulation model, which often focuses on the representativeness of the product with nominal parameters. This simulation model, including model deviations or model uncertainties due to simplifications and variations within the entire ODD, often forms the basis for developing closed-loop control of the product and / or for simulation-based product release. Currently, because a complete characterization of the model uncertainty of a serially manufactured product is generally too laborious, simulation models are compared to a small number of real product patterns (or patterns for short).

[0004] Typically, the product patterns are as follows: Patterns with minimum, maximum and / or nominal product parameters that are most important according to the expert opinion, for example; ·Patterns with product parameters defined by experts is selected.

[0005] Therefore, it is not guaranteed that the selected product patterns are sufficiently representative of the entire ODD and thus allow for characterization of model uncertainty due to simplifications and variations within the ODD. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Book “Essentials of Robust Control”, by Kemin Zhou and John C. Doyle, 1st edition, Pearson, 1997, ISBN: 9780135258332, Chapter 17 [Non-patent document 2] Publication “Frequency domain uncertainty and the graph topology”, Glenn Vinnicombe, IEEE Transactions on Automatic Control, vol. 38, no. 9, pp. 1371-1383, Sept. 1993, DOI: 10.1109 / 9.237648 Summary of the Invention [Problem to be solved by the invention]

[0007] The problem to be solved underlying this disclosure can be seen to be providing a method that allows for the selection of manufacturing patterns that are sufficiently representative for, for example, serially produced products. A further problem to be solved can be seen to be allowing for the characterization of model uncertainty due to, for example, simplifications and variations in the ODD.

[0008] Compared to conventional steering, steer-by-wire (SbW) steering systems and / or steering systems for highly automated driving (HAD) have more stringent prescriptive requirements for product release. To ensure that the more stringent release requirements for (mass) serially produced SbW and HAD steering systems do not inordinately increase the costs of real-world testing and trials compared to conventional steering, the industry is focusing on simulation-based release processes. For such simulation-based releases, a validated and / or verified simulation model of the steering system with known model uncertainties is important.

[0009] In principle, several different approaches are known for validating and / or verifying different aspects of a simulation model or the entire model. However, no approach currently exists that allows for a systematic or complete characterization of model uncertainty due to simplifications and variations within the ODD of a (mass) serially manufactured product. In particular, no method exists for systematically or optimally selecting, for example, a few product patterns that are representative of the entire ODD with quantifiable residual uncertainty, and thereby allow for a systematic characterization of model uncertainty within the ODD.

[0010] Furthermore, steering systems are subject to non-negligible parameter variations and are often operated in closed-loop control circuits. Therefore, for steering systems, robust controllers are often designed using established techniques in control engineering. However, this design methodology presupposes a model with known uncertainties for the product. For controller design, there is currently no technique for constructing one or more models with known uncertainties that also have a known range of representativeness within the ODD and are both representative for the entire ODD.

[0011] Therefore, a further problem to be solved underlying this disclosure can be seen as constructing one or more models with known uncertainties for controller design that also have a known range of representativeness within the ODD, and that are both representative for the entire ODD.

[0012] In systems theory, different indices are known to quantify the dissimilarity of two systems - in the following, systems and products can be treated as equivalent - and to compare them. In the following, the gap index, the v-gap index and the L2 index will be described.

[0013] The gap index quantifies the dissimilarity of the open-loop input / output characteristics of two systems P1 and P2 with respect to their stability and performance characteristics under closed-loop control using a scalar in the real interval [0,1]. An index result close to 0 means that the two systems are very similar, and that the respective P1-stabilizing controllers will also stabilize system P2 with similar closed-loop control performance. An index result of 0 means that the considered systems P1 and P2 exhibit exactly the same behavior. On the other hand, an index result close to or equal to 1 means that systems P1 and P2 are very dissimilar. Furthermore, the gap index can be used to describe the robust stability of closed-loop control circuits with model uncertainty. An explicit controller design is required for the evaluation of the gap index. Details about the definition and properties of the gap index are given in Chapter 17 of the book "Essentials of Robust Control", by Kemin Zhou and John C. Doyle, 1st edition, Pearson, 1997, ISBN: 9780135258332.

[0014] The system-theoretic description and implications of the ν-gap index are very similar to those of the gap index, but the two theories are fundamentally differently defined. To evaluate the ν-gap index, controller design is not required, but a rotational speed study of the systems P1 and P2 to be compared is required. Details about the definition and properties of the ν-gap index can be found in Chapter 17 of the book "Essentials of Robust Control" by Kemin Zhou and John C. Doyle, 1st edition, Pearson, 1997, ISBN: 9780135258332, or in the publication "Frequency Domain Uncertainty and the Graph Topology" by Glenn Vinnicombe, IEEE Transactions on Automatic Control, vol. 38, no. 9, pp. 1371-1383, September 1993, DOI: 10.1109 / 9.237648.

[0015] The definition of the L2 index corresponds to the ν-gap index without the use of rotational frequency testing. For this reason, although the descriptions and implications of both indices are similar, the L2 index has less theoretical persuasive power. For details on the definition and properties of the L2 index, see Chapter 17 of the book "Essentials of Robust Control" by Kemin Zhou and John C. Doyle, 1st edition, Pearson, 1997, ISBN: 9780135258332.

[0016] All three dissimilarity measures introduced have the following well-known properties: These indices quantify the dissimilarity of two systems with respect to stability and performance characteristics under closed-loop controlled operation, based on their closed-loop uncontrolled input / output behavior. The index results can be used to determine whether two systems P1 and P2 are sufficiently similar so that P1 can be considered representative for P2. The indicator results may be interpreted as the (system-theoretic) distance between the systems being compared. For all systems to be compared, L2 result ≦ ν-gap result ≦ gap result holds. [Means for solving the problem]

[0017] Disclosure of the Invention A first general aspect of the present disclosure relates to a computer-implemented method for determining a representative parametrized simulation model of a parametrizable analytical simulation model for a product, particularly for a steer-by-wire steering system and / or a steering system for highly automated driving.

[0018] The method includes identifying a predetermined number of parameterized simulation models based on a dissimilarity metric, where the dissimilarity metric between each two parameterized simulation models defines a distance, resulting in a plurality of representative parameterized simulation models.

[0019] A second general aspect of the present disclosure relates to a computer system configured to perform a computer-implemented method according to the first general aspect (or an embodiment thereof) for determining a representative parametrized simulation model of a parametrizable analytical simulation model for a product.

[0020] A third general aspect of the present disclosure relates to a computer program configured to perform the computer-implemented method according to the first general aspect (or an embodiment thereof) for determining a representative parametrized simulation model of a parametrizable analytical simulation model for a product.

[0021] A fourth general aspect of the present disclosure relates to a computer-readable medium or signal storing and / or including a computer program according to the third general aspect (or an embodiment thereof).

[0022] According to a method according to a first general aspect (or an embodiment thereof) proposed herein, a small number of product patterns can be systematically selected based on model-based criteria such that they are representative of the entire ODD of a serially produced product with quantifiable residual uncertainty.

[0023] In particular, the method (or embodiments thereof) according to the first general aspect proposed herein offers the following advantages over the prior art: A systematic approach to selecting product patterns that are representative of the overall ODD; Quantifiable residual uncertainty of the product pattern across the ODD, · Systematic characterization of model uncertainty due to simplifications and variations within the ODD is possible; · The calculable representativeness range of each product pattern within the entire ODD, · Systematically derivable requirements for manufacturing accuracy of product patterns; · Systematically derivable requirements for the design of robust closed-loop control of the product; and / or Search for product parameters or combinations of product parameters that significantly change the product's behavior can be achieved.

[0024] The method according to the first general aspect (or an embodiment thereof) proposed herein may be used during the design and / or system development (i.e. after the design stage) when developing a real product - for example in a steer-by-wire (SbW) steering system.

[0025] For example, during the design phase, the method may be used to systematically select (e.g., a small number of) product patterns that are representative for all realistic value ranges and value combinations of parameter variations in serially produced products (i.e., for the entire ODD). Furthermore, intermediate results of the method can be used to discover individual product parameters or combinations of product parameters that have a particularly strong influence on product behavior.

[0026] In subsequent system development, the uncertainty between the real-world product behavior and the modeled behavior of the product within the overall ODD can be systematically characterized based on representative product patterns, due to parameter variations and simplifications during modeling. The simulation model, including the characterized model uncertainty, can then be used for the development of the product's closed-loop control and / or for simulation-based product release.

[0027] Additionally, for example, in system development, any number of controller design models (i.e., one or more representative models for controller design) including associated model uncertainties, each with a known range of representativeness within the ODD and together representative for the entire ODD, can be systematically constructed using the methods proposed herein. These controller design models may be used, for example, to systematically develop gain-scheduled closed-loop control for a product that takes into account the known model uncertainties and ranges of representativeness.

[0028] The method according to the first general aspect (or an embodiment thereof) proposed herein may be used in the context of a process for simulation-based release, in particular of SbW steering systems and HAD steering systems. In this case, it is necessary to select a product pattern that is representative for the entire ODD, for example for validation and / or verification of a simulation model for the steering system. Furthermore, the method proposed herein may in principle also be used for model validation and / or verification and / or in the course of simulation-based release for other (mass) serially produced products.

[0029] The method according to the first general aspect (or embodiments thereof) proposed herein may be implemented fully or partly analytically, thereby avoiding laborious numerical minimization of distances. In embodiments, a parameterized simulation model representative of a parameterizable simulation model can often be accurately determined. [Brief explanation of the drawings]

[0030] [Figure 1] FIG. 1 illustrates a schematic diagram of an exemplary embodiment of a computer-implemented method for determining a representative parameterized simulation model of a parametrizable analytical simulation model for a product, in particular for a steer-by-wire steering system and / or a steering system for highly automated driving. [Figure 2] FIG. 1 illustrates an exemplary embodiment of a computer-implemented method. [Figure 3a] FIG. 3b illustrates the exemplary ODD of FIG. 3b, where three identified representative parameterized simulation models are representative for any of the exemplary multiple parameterized simulation models in the ODD. [Figure 3b]FIG. 1 illustrates an exemplary ODD with three identified representative parameterized simulation models. DETAILED DESCRIPTION OF THE INVENTION

[0031] Detailed Description The method 100 proposed in this disclosure may be directed to enabling the selection of a product pattern that is sufficiently representative for a series of manufactured products. Alternatively or additionally, the method 100 may be directed to enabling characterization of model uncertainty due to simplifications and variations in the ODD. Alternatively or additionally, the method 100 may be directed to building one or more models with known uncertainties for controller design that also have known ranges of representativeness within the ODD and that are both representative for the entire ODD.

[0032] In the following, we describe a model-based systematic selection of representative product patterns.

[0033] Method 100 may be directed to first determining a representative parameterized simulation model of a parameterizable analytical simulation model. Alternatively or additionally, method 100 may be directed to determining a representative parameter sample in an ODD. Alternatively or additionally, method 100 may be directed to selecting a representative product pattern.

[0034] Selecting the representative product pattern may be based on a determined representative parameterized simulation model of the parametrizable analytical simulation model and / or on representative parameter samples within the ODD.

[0035] The method 100 is fully or partially analytical and assumes that a parametrizable simulation model exists in analytical form.

[0036] First, a computer-implemented method 100 for determining a representative parameterized simulation model of a parameterizable analytical simulation model for a product is disclosed. The product may be, in particular, a serially produced product, i.e., manufactured in a continuous production manner. Although method 100 may also be used for non-serial or low-volume products, method 100 is particularly useful when it is desired to produce multiple homogeneous products that may vary (e.g., due to manufacturing and / or materials). The multiple homogeneous products may include, for example, more than 1e5 products per year, more than 5e5 products per year, or more than 1e6 products per year.

[0037] The product may be, for example, a steer-by-wire steering system. Alternatively or additionally, the product may be a steering system for automated driving, in particular for highly automated driving.

[0038] For example, as shown generally in FIG. 1 , method 100 includes identifying 140 a predetermined number of parameterized simulation models based on dissimilarity metrics, where the dissimilarity metrics between each two parameterized simulation models define a distance, resulting in a plurality of representative parameterized simulation models.

[0039] The dissimilarity index may be based on a gap index, a v-gap index, and / or an L2 index. In particular, the dissimilarity index may be a gap index, a v-gap index, or an L2 index. Alternatively, the dissimilarity index may be based on a combination of the gap index, the v-gap index, and / or the L2 index. The dissimilarity index may output a quantitative measure for the dissimilarity of a pair of parameterized simulation models, i.e., a quantitative measure for how similar or dissimilar the two parameterized simulation models of a pair are. To that extent, the dissimilarity index may also be referred to as a similarity index or a comparison index. The quantitative measure output by the dissimilarity index may be referred to as a distance. If the two parameterized simulation models of a pair are similar, the distance may be small. In particular, if the two parameterized simulation models of a pair are identical (i.e., maximally similar), the distance may be zero. On the other hand, if the two parameterized simulation models in a pair are dissimilar, the distance can be increased.

[0040] Each of the two parameterized simulation models may be defined by evaluating a parameterizable analytical simulation model for two parameter samples within an operational design domain (ODD) for the product. The ODD may be defined by a numerical and / or analytical description.

[0041] Method 100 or step 140 may further include transforming the parametrizable analytical simulation model to obtain one corresponding parameterized simulation model for any ODD sample. Such a step is referred to in the exemplary embodiment of FIG. 2 as "transforming the PAM to obtain one PUM for each ODD point," where PAM represents the parameterizable analytical simulation model, PUM represents a parameterized simulation model of the parameterizable analytical simulation model, and ODD point represents a parameter sample within the ODD.

[0042] Method 100 or step 140 may further include constructing a dissimilarity index function for any pair of parameterized simulation models within the entire ODD. Such a function may be a concatenation of dissimilarity indexes for two transformed parametrizable analytical simulation models. Such a step is referred to in the exemplary embodiment of FIG. 2 as "constructing an index function for any pair of PUMs within the entire ODD," where the index function is simply referred to as a dissimilarity index function.

[0043] The actual identification 140 of a predetermined number of parameterized simulation models based on the dissimilarity index is referred to in the exemplary embodiment of FIG. 2 as “searching for representative PUMs and / or ODD points based on the index function,” where the representative PUMs represent a plurality of representative parameterized simulation models and the ODD points again represent ODD samples.

[0044] The representative parameterized simulation models may be defined such that the distance from each parameterized simulation model (within the ODD) to each representative parameterized simulation model (also in the ODD) that is closest to the parameterized simulation model (in terms of the dissimilarity metric) is minimal. For example, these distances may be minimal in a predetermined finite-dimensional norm. For example, before using the finite-dimensional norm, the distances may be modified, particularly weighted, based on knowledge already existing at the time of performing method 100, e.g., about the product and / or its ODD.

[0045] In this case, a minimum distance can be identified, in particular a minimum distance in a finite-dimensional norm. The minimization can be performed in a mathematically exact or approximate sense. For example, it may be sufficient to find a local minimum instead of a global minimum. Thus, identifying 140 a predetermined number of representative parameterized simulation models can be performed either exactly or approximately based on a dissimilarity metric.

[0046] The predetermined finite-dimensional norm may be, for example, the p-norm for any p in the interval [1, +Inf], where +Inf represents positive infinity. In particular, the p-norm may be, for example, the sum norm (i.e., p=1). That is, in that case, the sum of all distances may be the smallest. In another example, the p-norm may be the Euclidean norm (i.e., p=2). That is, in that case, the Euclidean distance may be the smallest. In yet another example, the p-norm may be the maximum norm (p=Inf). That is, in the case of the maximum norm, multiple representative parameterized simulation models may be defined such that the maximum value of all distances from each parameterized simulation model to each representative parameterized simulation model closest to the simulation model is the smallest.

[0047] Identifying 140 the predetermined number of parameterized simulation models (resulting in a plurality of representative parameterized simulation models) may be based on analytical optimization. Alternatively or additionally, identifying 140 the predetermined number of parameterized simulation models may be based on approximate optimization. Alternatively or additionally, identifying 140 the predetermined number of parameterized simulation models may be based on numerical optimization. In particular, identifying 140 the predetermined number of parameterized simulation models may be based on a combination of analytical optimization, approximate optimization, and / or numerical optimization. The analytical optimization, approximate optimization, and / or numerical optimization may be performed on the ODD (i.e., on the entire ODD). This means, for example, that in identifying 140 the predetermined number of parameterized simulation models, each parameter sample in the ODD is considered as a possible representative parameter sample (or its corresponding representative parameterized simulation model). It can also mean that each combination of parameter samples in the ODD is considered as a possible representative parameter sample (or as their corresponding representative parameterized simulation model), and it can also mean that the parameter samples corresponding to each of the two parameterized simulation models include all pairs of parameter samples in the ODD as a whole.

[0048] Alternatively, identifying 140 a predetermined number of parameterized simulation models may be relevant to only a portion of the ODD (e.g., for a particular problem or for subsequent investigation of the ODD).

[0049] 1, the method 100 may further include determining 141 a measure for representativeness of the representative parameterized simulation model based on a predetermined number and / or a minimum distance, particularly a minimum distance in a finite-dimensional norm. The measure for representativeness may include, for example, a quantifiable residual uncertainty of the product pattern with respect to ODD.

[0050] 1, method 100 may further include adjusting 150, particularly increasing, the predetermined number if the measure of representativeness does not meet a predetermined criterion. For example, the predetermined criterion may be met if the measure of representativeness is sufficiently large. After adjusting 150, particularly increasing, the predetermined number, method 100 may be repeated, and more particularly, for example, until the measure of representativeness meets the predetermined criterion (i.e., for example, until the measure of representativeness is sufficiently large).

[0051] For example, as optionally shown schematically in FIG. 1 , method 100 may further include determining 160 each parameter sample associated with the representative parameterized simulation model as a representative parameter sample of the parametrizable analytical simulation model.

[0052] Also, as optionally shown schematically in, for example, FIG. 1 , method 100 may further include determining 161 a product pattern associated with a representative parameterized simulation model, resulting in a plurality of representative product patterns. Determining 161 a product pattern associated with a representative parameterized simulation model may be based, among other things, on representative parameter samples of a parameterizable analytical simulation model. Determining 161 a plurality of representative product patterns may be performed, among other things, based on a similarity relationship, for example, by comparison with a database of stored parameter samples of real product patterns. If the similarity is not satisfactory, one or more instructions on how to produce a sufficiently satisfactory representative product pattern from the real product pattern (e.g., by brushing, shimming, etc.) may be output.

[0053] Steps 160 and 161 are referred to in the exemplary embodiment of Figure 2 as "defining a representative pattern based on representative PUMs and / or ODD points," where again, the representative PUMs represent a plurality of representative parameterized simulation models, and the ODD points represent ODD samples.

[0054] For example, as optionally shown schematically in FIG. 1 , the method 100 may further include configuring 170 a closed-loop control of the product based on at least one representative parameterized simulation model. Configuring 170 the closed-loop control of the product may, in particular, be based on a plurality of representative parameterized simulation models. This may allow for designing and / or configuring a robust closed-loop control of the product. In particular, this may allow for a particularly robust and reliable configuration of the closed-loop control of the product. This may, in particular, increase the safety of the product.

[0055] Also, as optionally shown schematically in, for example, FIG. 1 , the method 100 may further include checking 171 one or more requirements for the product based on at least one representative parameterized simulation model. Checking 171 one or more requirements for the product may be based in particular on a plurality of representative parameterized simulation models. This allows for a robust and reliable construction of the product. This also allows for, in particular, increased safety of the product.

[0056] For example, as optionally shown schematically in FIG. 1 , the method 100 may further include identifying 172 one or more of the representative parameterized simulation models and / or representative parameter samples that have a greater impact on the product and / or its behavior (e.g., steering behavior). This may be performed, for example, by sensitivity analysis. This may improve understanding of the product and its behavior. This may be particularly useful in the case of relatively high-dimensional ODDs, since in that case the ODDs can no longer be examined directly.

[0057] 2 shows an exemplary embodiment of the method 100, which may include, for example, the following steps: "Transform PAM to get one PUM for each ODD point" "Construct an indicator function for any PUM pair within the entire ODD" "Searching for representative PUM and / or ODD points based on indicator functions" "Define a representative pattern based on representative PUM and / or ODD points" The steps can be performed in this order.

[0058] FIG. 3a illustrates the exemplary ODD of FIG. 3b, where three identified representative parameterized simulation models are representative for any of the exemplary multiple parameterized simulation models in the ODD.

[0059] FIG. 3b shows an exemplary ODD with three identified representative parameterized simulation models (or, more precisely, their corresponding parameter samples), each indicated by a cross within the ODD. Thus, three are identified here as representative parameterized simulation models or their parameter samples within the ODD 140, 160, more specifically, identified so that the distance (by dissimilarity metric) from each parameterized simulation model to its nearest representative parameterized simulation model is minimal 140, 160. In FIG. 3a, some of these arbitrary parameterized simulation models or their corresponding parameter samples are shown as solid dots within the ODD for illustrative purposes only.

[0060] The ODD is a two-dimensional Cartesian product space in this example, i.e., it is formed by two parameters (one parameter in the x direction and the other parameter in the y direction), and these two parameters can each take values ​​in an interval. In general, the ODD may not be a Cartesian product space and may be of (arbitrary) variety. The dimensionality of the ODD may be arbitrary.

[0061] In this example, the distance (by a dissimilarity metric) from each point in the ODD (or more precisely, its parameterized simulation model) to the representative parameterized simulation model that is closest to the parameter sample in question can be shown in 3D, i.e., in the z direction perpendicular to the drawing plane. In 2D, instead, several isolines are shown based on this magnitude in the z direction. For example, three solid lines form an isoline, i.e., have the same z value. Furthermore, three further isolines are shown—extending around the cross. In addition to these isolines, the boundaries of the influence range of each representative parameterized simulation model (dashed lines intersecting at a single point) are also drawn.

[0062] Also disclosed is a computer system configured to perform the computer-implemented method 100 for determining a representative parameterized simulation model of a parametrizable analytical simulation model for a product. The computer system may include a processor and / or a main memory.

[0063] Further disclosed is a computer program configured to perform the computer-implemented method 100 for determining a representative parameterized simulation model of a parameterizable analytical simulation model for a product. The computer program can exist, for example, in an interpretable or compiled form. The computer program may be (even partially) loaded into a computer's RAM for execution, for example, as a sequence of bits or bytes.

[0064] Further disclosed is a computer readable medium or signal storing and / or including the computer program. The medium may include, for example, one of RAM, ROM, EPROM, HDD, SSD, etc., on / in which the signal is stored.

Claims

1. 1. A computer-implemented method (100) for determining a representative parametrized simulation model of a parametrizable analytical simulation model for a product, in particular for a steer-by-wire steering system and / or a steering system for highly automated driving, comprising: The method (100) comprises: Identifying (140) a predetermined number of parameterized simulation models based on a dissimilarity measure, where the dissimilarity measure between each two parameterized simulation models defines a distance, resulting in a plurality of representative parameterized simulation models (140). A method comprising:

2. The dissimilarity index is based on a gap index, a ν-gap index, and / or an L2 index; The method (100) of claim 1.

3. The plurality of representative parameterized simulation models are defined such that the distance from each parameterized simulation model to the respective representative parameterized simulation model that is closest to the respective parameterized simulation model is minimum, particularly in a predetermined finite-dimensional norm.

3. The method (100) of claim 1 or 2.

4. The predetermined finite-dimensional norm is a p-norm for p in [1, +Inf], in particular a sum norm, a Euclidean norm, or a maximum norm. The method (100) of any one of claims 1 to 3.

5. The method (100) comprises: determining (141) a measure for the representativeness of the representative parameterized simulation model based on the predetermined number and / or a minimum distance, in particular a minimum distance in the finite-dimensional norm; Optionally, adjusting (150), in particular increasing, said predetermined number if said measure of representativeness does not sufficiently meet a predetermined criterion; 5. The method (100) of claim 3 or 4, comprising:

6. Each of the two parameterized simulation models is defined by evaluating the parametrizable analytical simulation model for two parameter samples within an operational design domain (ODD) for the product. The method (100) of any one of claims 1 to 5.

7. identifying 140 the predetermined number of parameterized simulation models based on analytical optimization, approximate optimization, and / or numerical optimization; The method (100) of any one of claims 1 to 6.

8. The analytical optimization, the approximate optimization, and / or the numerical optimization is performed on the ODD. The method (100) of claim 7.

9. The method (100) comprises: determining each parameter sample associated with the representative parameterized simulation model as the representative parameter sample of the parameterizable analytical simulation model (160); The method (100) of any one of claims 1 to 8, comprising:

10. The method (100) comprises: Determining (161) a product pattern associated with the representative parameterized simulation model, in particular resulting in a plurality of representative product patterns based on the representative parameter samples of the parameterizable analytical simulation model (161). The method (100) of any one of claims 1 to 9, comprising:

11. The method (100) comprises: configuring (170) a closed-loop control of said product based on at least one representative parameterized simulation model, optionally based on a plurality of representative parameterized simulation models; and / or Checking one or more requirements for said product based on at least one representative parameterized simulation model, and optionally based on a plurality of representative parameterized simulation models (171). The method (100) of any one of claims 1 to 10, comprising:

12. The method (100) comprises: Identifying one or more of the representative parameterized simulation models and / or the representative parameter samples that have a greater impact on the product (172). The method (100) of any one of claims 1 to 11, comprising:

13. 13. A computer system configured to perform the computer-implemented method (100) of any one of claims 1 to 12 for determining a representative parameterized simulation model of a parameterizable analytical simulation model for a product.

14. 13. A computer program configured to perform the computer-implemented method (100) of any one of claims 1 to 12 for determining a representative parameterized simulation model of a parameterizable analytical simulation model for a product.

15. A computer readable medium or signal storing and / or including a computer program according to claim 14.

Images