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Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The methods of choice are basically discussed and references are given as well as challenges in the context of meta-modelling and sensitivities are shown. An incremental knowledge gain is provided by a step-bystep application of the numerical methods, whereas resulting consequences for further applications are highlighted. Furthermore, a visualisation method aiming at an easy design guideline is proposed. These visual decision maps incorporate the uncertainty coming from the reduction of dimensionality and can be applied in early stage of design. [ABSTRACT FROM AUTHOR]

Abstract The meta-modelling approach based on an adaptive sparse grid interpolator is proposed for tackling the identification problem of parametric hysteresis models for steels with different microstructures. Parametric models of Jiles-Atherton and Mel'gui, respectively, have been considered in this work. The main advantage of the present approach is the separation of the calculation procedure in a computationally demanding off-line phase, which has to be carried out only once, and a very fast on-line evaluation. This decomposition is particularly interesting when a large amount of successive evaluations has to be carried out. Especially in the case that we are interested in a particular family of ferromagnetic materials (e.g. steels subjected to different treatments), where the sought parameters are lying in a specific interval, a single meta-model may be sufficient to be used for the study of a wide range of specimens. The steel samples considered in this study have been obtained from industrially produced low carbon steel, 84% cold rolled, and isothermally annealed in laboratory. [ABSTRACT FROM AUTHOR]

Systems Biology Graphical Notation (SBGN) is a standard graphical language for representing biological and biochemical processes and interactions. Even that SBGN Process Description-an SBGN sub-language-facilitates biological systems representation, it allows only qualitative descriptions of metabolic pathways and does not provide quantitative analytic environment. However such descriptions are essential for behavioural analysis. This analysis can be possible using computational formalisms such as Hybrid Functional Petri Net (HFPN) which is a Petri net extension dedicated to study and verify biopathways. However, biologists use generally graphical notations such as SBGN. To address this paradox, we propose, in this paper, an SBGN-PD to HFPN transformation based on Model Driven Engineering (MDE). Furthermore, we illustrate this transformation on the Glycolysis pathway and verify the transformation resulting HFPN model by comparing it with the existing HFPN model through simulation. [ABSTRACT FROM AUTHOR]

Operating high-speed trains imposes excessive vibrations to bridges raising concerns about their safety. In this context, it was shown that some conventional design methods such as those related to the running safety suffer from a vague scientific background questioning their reliability or optimality. Therefore, the current article is devoted to updating the conventional design methodology, using Reliability-Based Design Optimization (RBDO) to propose the minimum allowable mass and stiffness which assures satisfying the target reliability. These proposed minimum design values can conceptually replace the conventional partial safety factor-based design method for running safety without the need for dynamic analysis. If the mass and stiffness resulting from the control of other limit states meet the proposed minimum values, the desired target reliability for running safety will be assured. This is achieved by adaptively training Kriging meta-models as a surrogate for the computational models decoupling the RBDO problem. In this regard, a new stopping criteria is proposed using mis-classification ratio of the cross-validated model; which reduces the generalization error of the trained meta-model and consequently the estimated failure probability. Moreover, due to the dependence of the design variables, the Copula concept is used to refine the augmented space and reformulate the RBDO problem. • A novel and straightforward dynamic design method is proposed for railway bridges. • The Copula concept is adopted to reformulate RBDO problems with dependent variables. • A new stopping criterion is proposed for adaptive training of surrogate models. • An extensive data is collected for variables in dynamic behavior of railway bridges. • Proper PDFs are assigned for contributing random variables of railway bridges. [ABSTRACT FROM AUTHOR]

Sequential bifurcation (or SB) is an efficient and effective factor-screening method; i.e., SB quickly identifies the important factors (inputs) in experiments with simulation models that have very many factors—provided the SB assumptions are valid. The specific SB assumptions are: (i) a second-order polynomial is an adequate approximation (a valid metamodel) of the input/output function of the underlying simulation model; (ii) the directions (signs) of the first-order effects are known (so the first-order polynomial approximation is monotonic); (iii) so-called “heredity” applies; i.e., if a specific input has a “small” first-order effect, then this input has “small” second order effects. Moreover, SB assumes Gaussian simulation outputs if the simulation model is stochastic (random). A generalization of SB called “multiresponse SB” (or MSB) uses the same assumptions, but allows multiple types of simulation responses (outputs). In this article, we develop heuristic practical methods for testing whether these assumptions hold, and we evaluate these methods through Monte Carlo experiments and a case study (namely, a Chinese logistics network). [ABSTRACT FROM AUTHOR]

This paper addresses the needs of generic and shared PSS conceptual modelling approaches. The development of conceptual models for PSS is considered in this research as the basis to develop an integrated approach for PSS design. The objective of the paper is to present a generic PSS-dedicated meta-model, resulting from a procedure of integration of various conceptual models developed in the scientific literature of PSS design methods. The procedure of knowledge extraction and integration is explained and the generic PSS meta-model is presented, together with an associated modelling procedure. These results constitute a path forward towards more generic PSS design methods. [ABSTRACT FROM AUTHOR]

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Meta-models play a cornerstone role in Model-Driven Engineering as they are used to define the abstract syntax of modelling languages, and so models and all sorts of model transformations depend on them. However, there are scarce tools and methods supporting their Validation and Verification (V&V), which are essential activities for the proper engineering of meta-models. In order to fill this gap, we propose two complementary meta-model V&V languages. The first one has similar philosophy to the xUnit framework, as it enables the definition of meta-model unit test suites comprising model fragments and assertions on their (in-)correctness. The second one is directed to express and verify expected properties of a meta-model, including domain and design properties, quality criteria and platform-specific requirements. As a proof of concept, we have developed tooling for both languages in the Eclipse platform, and illustrate its use within an example-driven approach for meta-model construction. The expressiveness of our languages is demonstrated by their application to build a library of meta-model quality issues, which has been evaluated over the ATL zoo of meta-models and some OMG specifications. The results show that integrated support for meta-model V&V (as the one we propose here) is urgently needed in meta-modelling environments. [ABSTRACT FROM AUTHOR]

Context: Several different computer-assisted management systems of computer interpretable guidelines (CIGs) have been developed by the Artificial Intelligence in Medicine community. Each CIG system is characterized by a specific formalism to represent CIGs, and usually provides a manager to acquire, consult and execute them. Though there are several commonalities between most formalisms in the literature, each formalism has its own peculiarities.Objective: The goal of our work is to provide a flexible support to the extension or definition of CIGs formalisms, and of their acquisition and execution engines. Instead of defining "yet another CIG formalism and its manager", we propose META-GLARE (META Guideline Acquisition, Representation, and Execution), a "meta"-system to define new CIG systems.Method and Materials: In this paper, META-GLARE, a meta-system to define new CIG systems, is presented. We try to capture the commonalities among current CIG approaches, by providing (i) a general manager for the acquisition, consultation and execution of hierarchical graphs (representing the control flow of actions in CIGs), parameterized over the types of nodes and of arcs constituting it, and (ii) a library of different elementary components of guidelines nodes (actions) and arcs, in which each type definition involves the specification of how objects of this type can be acquired, consulted and executed. We provide generality and flexibility, by allowing free aggregations of such elementary components to define new primitive node and arc types.Results: We have drawn several experiments, in which we have used META-GLARE to build a CIG system (Experiment 1 in Section 8), or to extend it (Experiments 2 and 3). Such experiments show that META-GLARE provides a useful and easy-to-use support to such tasks. For instance, re-building the Guideline Acquisition, Representation, and Execution (GLARE) system using META-GLARE required less than one day (Experiment 1).Conclusions: META-GLARE is a meta-system for CIGs supporting fast prototyping. Since META-GLARE provides acquisition and execution engines that are parametric over the specific CIG formalism, it supports easy update and construction of CIG systems. [ABSTRACT FROM AUTHOR]

The transition from a product–oriented business towards a PSS-oriented business, known in the scientific literature as ‘servitization’ involves complex changes for decision-makers. Over the past years, the scientific literature has provided consistent advances in PSS decision-support systems including PSS modelling. However, concerning PSS modelling languages or formalisms, most initiatives remain context dependent; to date only a small a piece of literature addresses the need for reproducibility of PSS modelling methods. The objective of this paper is to make a first step forward in this direction, by proposing an iterative procedure dedicated to build generic meta-models and by applying it to generate a first proposal of PSS meta-model, expected to be re-usable in several distinct modelling and decision-making contexts. [ABSTRACT FROM AUTHOR]

Key message : We present simple models of forest net primary production (NPP) in Germany that show increasing productivity, especially in mountainous areas, under warming unless water becomes a limiting factor. They can be used for spatially explicit, rapid climate impact assessment. Context : Climate impact studies largely rely on process-based forest models generally requiring detailed input data which are not everywhere available. Aims : This study aims to derive simple models with low data requirements which allow calculation of NPP and analysis of climate impacts using many climate scenarios at a large amount of sites. Methods : We fitted regression functions to the output of simulation experiments conducted with the process-based forest model 4C at 2342 climate stations in Germany for four main tree species on four different soil types and two time periods, 1951-2006 and 2031-2060. Results : The regression functions showed a reasonable fit to measured NPP datasets. Temperature increase of up to 3 K leads to positive effects on NPP. In water-limited regions, this positive effect is dependent on the length of drought periods. The highest NPP increase occurs in mountainous regions. Conclusion : Rapid analyses, using reduced models as presented here, can complement more detailed analyses with process-based models. Especially for dry sites, we recommend further study of climate impacts with process-based models or detailed measurements. [ABSTRACT FROM AUTHOR]

Despite significant advances in Clinical Decision Support Systems, they have not been extensively used in nursing practice to date. One key problem is the failure of these systems to fully support actionable nursing practices that guide nurse decision-making. In addition, current workflow-related systems have failed to consider the specific workflow challenges associated with acute-care nursing. In response to these challenges, we describe a novel three-stage approach that builds and evaluates a meta-model that addresses key requirements of multi-level guideline-based clinical nursing-specific decision support. This research-in-progress presents the first two stages of this approach, highlighting the importance of meta-modelling as a tool to identify the essential system-centric information that underpins acute-care nursing practice. [ABSTRACT FROM PUBLISHER]

In this paper, crushing characteristics of small-sized conical tubes called miniature frusta under axial loading have been studied. Finite-element model is developed using non-linear explicit code LS_DYNA to investigate the non-symmetrical fold patterns as well as material and geometrical non-linearity of frusta. Numerical simulation is first validated by confirming the results using experimental test data. Effects of shell thickness, semi-epical angle of cone and frusta's length on energy absorption characteristics are then studied by carrying out a parametric study. Based on the crushing parameters of numerous examples, a simplified analytical model as a meta-model for the mean crushing force of miniature frusta is presented using a Genetic Algorithm optimization for finding the meta-model coefficients. Miniature frusta show promising behaviour in lightweight design and crash analysis as their response results in low peak force and efficient-specific energy absorption. Obtained results from the developed meta-model showed good concurrence with finite elements method (FEM) model. [ABSTRACT FROM AUTHOR]

This work is motivated by a real-world case study where it is necessary to integrate and relate existing ontologies through meta-modelling . For this, we introduce the Description Logic SHIQM which is obtained from SHIQ by adding statements that equate individuals to concepts in a knowledge base. In this new extension, concepts can be individuals of another concept (called meta-concept ) which itself can be an individual of yet another concept (called meta–meta-concept ) and so on. We define an algorithm that checks consistency of SHIQM by modifying the Tableau algorithm for SHIQ . From the practical point of view, this has the advantage that we can reuse the code of existing OWL reasoners. From the theoretical point of view, it has a similar advantage of reuse. We make use of the existing results and proofs that lead to correctness of the algorithm for SHIQ in order to prove correctness of the algorithm for SHIQM . [ABSTRACT FROM AUTHOR]

The intensive use of models in model-driven engineering (MDE) raises the need to develop meta-models with different aims, such as the construction of textual and visual modelling languages and the specification of source and target ends of model-to-model transformations. While domain experts have the knowledge about the concepts of the domain, they usually lack the skills to build meta-models. Moreover, meta-models typically need to be tailored according to their future usage and specific implementation platform, which demands knowledge available only to engineers with great expertise in specific MDE platforms. These issues hinder a wider adoption of MDE both by domain experts and software engineers. In order to alleviate this situation, we propose an interactive, iterative approach to meta-model construction, enabling the specification of example model fragments by domain experts, with the possibility of using informal drawing tools like Dia or yED. These fragments can be annotated with hints about the intention or needs for certain elements. A meta-model is then automatically induced, which can be refactored in an interactive way, and then compiled into an implementation meta-model using profiles and patterns for different platforms and purposes. Our approach includes the use of a virtual assistant, which provides suggestions for improving the meta-model based on well-known refactorings, and a validation mode, enabling the validation of the meta-model by means of examples. [ABSTRACT FROM AUTHOR]

Managing complex emergency situations is a challenging task, mainly due to the heterogeneity of the partners involved and the critical nature of such events. Whatever approach is adopted to support this objective, one unavoidable issue is knowledge management. In the context of our research project, gathering, formalising and exploiting all the knowledge and information about a given crisis situation is a critical requirement. This paper presents some research results concerning this specific topic: from a theoretical point of view, the generic dimensions of crisis characterisation are defined, while from a technical point of view, we describe a software solution able to collect that knowledge (based on meta-models and ontologies). This is used to confront the characteristics of the situation (context) with characteristics of the resources (relief system) in order to design a suitable response. Finally, an illustrative example concerning a crash between a tanker truck and a train is described. [ABSTRACT FROM AUTHOR]

A prediction of a chemical property or activity is subject to uncertainty. Which type of uncertainties to consider, whether to account for them in a differentiated manner and with which methods, depends on the practical context. In chemical modelling, general guidance of the assessment of uncertainty is hindered by the high variety in underlying modelling algorithms, high-dimensionality problems, the acknowledgement of both qualitative and quantitative dimensions of uncertainty, and the fact that statistics offers alternative principles for uncertainty quantification. Here, a view of the assessment of uncertainty in predictions is presented with the aim to overcome these issues. The assessment sets out to quantify uncertainty representing error in predictions and is based on probability modelling of errors where uncertainty is measured by Bayesian probabilities. Even though well motivated, the choice to use Bayesian probabilities is a challenge to statistics and chemical modelling. Fully Bayesian modelling, Bayesian meta-modelling and bootstrapping are discussed as possible approaches. Deciding how to assess uncertainty is an active choice, and should not be constrained by traditions or lack of validated and reliable ways of doing it. [ABSTRACT FROM AUTHOR]

Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a scenario which formulates the problem of optimizing a computationally expensive black-box functions. In such problems, there will often exist candidate designs which cause the simulation to fail, and this can degrade the optimization effectiveness. To address this issue, this paper proposes a new optimization algorithm which incorporates classifiers into the optimization search. The classifiers predict which candidate design are expected to cause the simulation to fail, and their prediction is used to bias the search towards valid designs, namely, for which the simulation is expected to succeed. However, the effectiveness of this approach strongly depends on the type of metamodels and classifiers being used, but due to the high cost of evaluating the simulation-based objective function it may be impractical to identify by numerical experiments the most suitable types of each. Leveraging on these issues, the proposed algorithm offers two main contributions: (a) it uses ensembles of both metamodels and classifiers to benefit from a diversity of predictions of different metamodels and classifiers, and (b) to improve the search effectiveness, it continuously adapts the ensembles' topology during the search. The performance of the proposed algorithm was evaluated using an engineering problem of airfoil shape optimization. Performance analysis of the proposed algorithm using an engineering problem of airfoil shape optimization shows that: (a) incorporating classifiers into the search was an effective approach to handle simulation failures (b) using ensembles of metamodels and classifiers, and updating their topology during the search, improved the search effectiveness in comparison to using a single metamodel and classifier, and (c) it is beneficial to update the topology of the metamodel ensemble in all problem types, and it is beneficial to update the classifier ensemble topology in problems where simulation failures are prevalent. [ABSTRACT FROM AUTHOR]

SUMMARY A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (to real-time interactive speed) can be achieved. Using a multilevel fractional factorial design, the parameter space of the biomechanical system was probed from a set of sample points chosen to satisfy maximal rank optimality and volume filling. The input-output relationship at these sampled points was then statistically emulated using linear and nonlinear, cross-validated, partial least squares regression models. It was demonstrated that these surrogate models can mimic facial biomechanics efficiently and reliably in real-time. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper examines the relationships between the marginal abatement costs ( MAC) of greenhouse gas ( GHG) emissions on dairy farms and factors such as herd size, milk yield and available farm labour, on the one hand, and prices, GHG indicators and GHG reduction levels, on the other. A two-stage Heckman procedure is used to estimate these relationships from a systematically designed set of simulations with a highly detailed mixed integer bio-economic farm-level model. The resulting meta-models are then used to analyse how MAC vary across farm-level conditions and GHG measures. We find that simpler GHG indicators lead to significantly higher MAC, and that MAC strongly increase beyond a 1-5% emission reduction, depending on farm attributes and the chosen indicator. MAC decrease rapidly with increasing farm size, but the effect levels off beyond a herd size of 40 cows. As expected, the main factors driving gross margins per dairy cow also significantly influence mitigation costs. Our results indicate high variability of MAC on real life farms. In contrast to time consuming simulations with the complex mixed integer bio-economic programming model, the meta-models allow the distribution of MAC in a farm population to be efficiently derived and thus could be used to upscale to regional or sector level. [ABSTRACT FROM AUTHOR]

Model-driven engineering (MDE) is a software engineering paradigm that proposes an active use of models during the development process. This paradigm is inherently type-centric, in the sense that models and their manipulation are defined over the types of specific meta-models. This fact hinders the reuse of existing MDE artefacts with other meta-models in new contexts, even if all these meta-models share common characteristics. To increase the reuse opportunities of MDE artefacts, we propose a paradigm shift from type-centric to requirement-centric specifications by bringing genericity into models, meta-models and model management operations. For this purpose, we introduce so-called concepts gathering structural and behavioural requirements for models and meta-models. In this way, model management operations are defined over concepts, enabling the application of the operations to any meta-model satisfying the requirements imposed by the concept. Model templates rely on concepts to define suitable interfaces, hence enabling the definition of reusable model components. Finally, similar to mixin layers, templates can be defined at the meta-model level as well, to define languages in a modular way, as well as layers of functionality to be plugged-in into other meta-models. These ideas have been implemented in MetaDepth, a multi-level meta-modelling tool that integrates action languages from the Epsilon family for model management and code generation. [ABSTRACT FROM AUTHOR]

Abstract: Moment-independent sensitivity methods are attracting increasing attention among practitioners, since they provide a thorough way of investigating the sensitivity of model output under uncertainty. However, their estimation is challenging, especially in the presence of computationally intensive models. We argue that replacement of the original model by a metamodel can contribute in lowering the computation burden. A numerical estimation procedure is set forth. The procedure is first tested on analytical cases with increased structural complexity. We utilize the emulator proposed in . Results show that the emulator allows an accurate estimation of density-based sensitivity measures, when the main structural features of the original model are captured. However, performance deteriorates for a model with interactions of order higher than 2. For this test case, also a kriging emulator is investigated, but no gain in performance is registered. However, an accurate estimation is obtained by applying a logarithmic transformation of the model output for both the kriging and emulators. These findings are then applied to the investigation of a benchmark environmental case study, the LevelE model. Results show that use of the metamodel allows an efficient estimation of moment-independent sensitivity measures while leading to a notable reduction in computational burden. [Copyright &y& Elsevier]

The potential machinability for Alloy 718 (Inconel 718) is examined in terms of five material characteristics considered to play a key role in the machinability: ductility (elongation to fracture), strain hardening (ultimate tensile strength over yield strength), thermal conductivity, yield strength and abrasiveness (amount of carbides). The material characteristics are simulated with the software JMatPro from Sente software. The effects of composition, grain size, hardness (size of the precipitated intermetallic particles for given volume fraction), heat treatment, temperature and strain rate have been modelled and statistically evaluated. Combining thermodynamics-based modelling (JMatPro), design of experiments and statistical analysis (Minitab), and machinability polar diagram, a concept on methodology to assess variations in material specifications and to optimise these specifications with respect to potential machinability has been developed. The mechanical properties, predicted from the meta-modelling are found to be affected by the same parameters: hardness (intermetallic particles characteristics), grain size, amount of aluminium, strain rate and temperature. The abrasiveness should only be affected by the amount of carbon. Simulated material characteristics for two different types of turbine discs were compared with measured tool wear from production environment machining experiments. Variations in material characteristics between the discs were small as well as the critical tool wear, revealing a robust metal cutting process. [ABSTRACT FROM AUTHOR]

Meta-modelling plays an important role in model driven software development. In this paper, a graphic extension of BNF (GEBNF) is proposed to define the abstract syntax of graphic modelling languages. From a GEBNF syntax definition, a formal predicate logic language can be induced so that meta-modelling can be performed formally by specifying a predicate on the domain of syntactically valid models. In this paper, we investigate the theoretical foundation of this meta-modelling approach. We formally define the semantics of GEBNF and its induced predicate logic languages, then apply Goguen and Burstall's institution theory to prove that they form a sound and valid formal specification language for meta-modelling. [ABSTRACT FROM AUTHOR]