Global sensitivity analysis of computer models with functional inputs

Global sensitivity analysis is used to quantify the influence of uncertain model inputs on the response variability of a numerical model. The common quantitative methods are appropriate with computer codes having scalar model inputs. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called Sobol's indices, when some model inputs are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary metamodeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked generalized linear models (GLMs) or generalized additive models (GAMs). The “mean model” allows to estimate the sensitivity indices of each scalar model inputs, while the “dispersion model” allows to derive the total sensitivity index of the functional model inputs. The proposed approach is compared to some classical sensitivity analysis methodologies on an analytical function. Lastly, the new methodology is applied to an industrial computer code that simulates the nuclear fuel irradiation.     

Model-driven engineering: A survey supported by the unified conceptual model

During the last decade a new trend of approaches has emerged, which considers models not just documentation artefacts, but also central artefacts in the software engineering field, allowing the creation or automatic execution of software systems starting from those models. These proposals have been classified generically as Model-Driven Engineering (MDE) and share common concepts and terms that need to be abstracted, discussed and understood. This paper presents a survey on MDE based on a unified conceptual model that clearly identifies and relates these essential concepts, namely the concepts of system, model, metamodel, modeling language, transformations, software platform, and software product. In addition, this paper discusses the terminologies relating MDE, MDD, MDA and others. This survey is based on earlier work, however, contrary to those, it intends to give a simple, broader and integrated view of the essential concepts and respective terminology commonly involved in the MDE, answering to key questions such as: What is a model? What is the relation between a model and a metamodel? What are the key facets of a modeling language? How can I use models in the context of a software development process? What are the relations between models and source code artefacts and software platforms? and What are the relations between MDE, MDD, MDA and other MD approaches?     

Towards vulnerability minimization of grassland soil organic matter using metamodels

Vulnerability is the degree to which a human or environmental system is likely to experience harm due to a perturbation or a stress. This paper aims at proposing a generic quantitative method for climate change vulnerability assessment and to illustrate it on the particular case of the steady-state soil organic matter (SOM) of grassland thanks to PaSim, a mechanistic biochemical model. Based on literature review, we first present a model of concepts related to climate change vulnerability, and then we give our numerical method for vulnerability assessment. We documented all the different steps of our approach (from building of the initial design of experiments, to assessment of vulnerability with adaptation, through generating response surfaces and searching for vulnerability minima with different optimization methods). This study showed that steady-state SOM content will globally increase in future and that their vulnerability will decrease (due to higher increase of average values compared to the increased variability). Moreover, the analysis of the found vulnerability minima suggests both a reduction of vulnerability of SOM of adapted system and an increase of the gain by adaptation.     

Synthesis of verification models in multidisciplinary design of complex engineered systems

In designing complex engineered systems, various experiments and simulations are needed for the verification of design hypotheses. Because the conducted verification is multidisciplinary, synthesis of these verification results should be based on a neutral model regarding the domains and disciplines. This paper introduces a metamodel, to which the roles and designers’ intentions (i.e., design situation) of various verification models are referred, and uses it for analyzing the synthesis process. The design of a paper-handling device is used for an analysis example, which clarifies advantages and drawbacks in modeling the synthesis process with such a metamodel.     

Multiobjective crashworthiness optimization design of functionally graded foam-filled tapered tube based on dynamic ensemble metamodel

Foam-filled thin-walled structures have recently gained attention with increasing interest due to their excellent energy absorption capacity. In this study, a new type of foam-filled thin-walled structure called as functionally graded foam-filled tapered tube (FGFTT) is proposed. FGFTT consists of graded density foam and thin-walled tapered tube. In order to investigate the energy absorption characteristics of FGFTTs, the numerical simulations for two kinds of FGFTTs subjected to axial dynamical loading are carried out by nonlinear finite element code LS-DYNA. In addition, a new kind of multiobjective crashworthiness optimization method employing the dynamic ensemble metamodeling method together with the multiobjective particle swarm optimization (MOPSO) algorithm is presented. This new kind of multiobjective crashworthiness optimization method is then used to implement the crashworthiness optimization design of FGFTTs. Meanwhile, the crashworthiness optimization designs of FGFTTs are implemented by using traditional multiobjective crashworthiness optimization method, which employs metamodels such as polynomial response surface (PRS), radial basis function (RBF), kriging (KRG), support vector regression (SVR) or the ensemble with the static design of experiment (DOE). Finally, by comparing the optimal designs of FGFTTs obtained by using the new multiobjective crashworthiness optimization method and the traditional one, the results show that the proposed new crashworthiness optimization method is more feasible.     

Kriging metamodel with modified nugget-effect: The heteroscedastic variance case

Metamodels are commonly used to approximate and analyze simulation models. However, in cases where the simulation output variances are non-zero and not constant, many of the current metamodels which assume homogeneity, fail to provide satisfactory estimation. In this paper, we present a kriging model with modified nugget-effect adapted for simulations with heterogeneous variances. The new model improves the estimations of the sensitivity parameters by explicitly accounting for location dependent non-constant variances and smoothes the kriging predictor’s output accordingly. We look into the effects of stochastic noise on the parameter estimation for the classic kriging model that assumes deterministic outputs and note that the stochastic noise increases the variability of the classic parameter estimation. The nugget-effect and proposed modified nugget-effect stabilize the estimated parameters and decrease the erratic behavior of the predictor by penalizing the likelihood function affected by stochastic noise. Several numerical examples suggest that the kriging model with modified nugget-effect outperforms the kriging model with nugget-effect and the classic kriging model in heteroscedastic cases.     

A capabilities-based model for adaptive organizations

Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member’s capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system’s organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent’s capabilities.     

Comparison study on the accuracy of metamodeling technique for non-convex functions

In order to increase the efficiency of design optimization, many efforts have been made on studying the metamodel techniques for effectively representing expensive and complex models. In this study, a comparison is conducted on the accuracy of several widely used meta-model techniques — moving least squares (MLS), Kriging, support vector regression (SVR) and radial basis functions (RBF) — which are able to approximate non-convex functions well. RMSE (root mean squared error) value is identified as a measure of the accuracy for this comparison. Each metamodel technique is used to approximate the six well-known mathematical functions and a resign of experiment (DOE) is generated by using the Latin hypercube design (LHD), which is also performed for each resulting metamodel. The results show that Kriging and MLS can create a more accurate metamodel than SVR and RBF with the mathematical functions tested. This paper was recommended for publication in revised form by Associate Editor Dae-Eun Kim Byeong-Soo Kim received B.S and M.S. degree in Mechanical Engineering from Hanyang University in 2006 and 2008, respectively. Mr. Kim is currently a Research Engineer at LG Electronics. Yong-Bin Lee received a B.S. and M.S. degree in Mechanical Engineering from Hanyang University in 2002 and 2004, respectively. He is currently a Ph.D. student in Hanyang University. Mr. Lee’s research interests are in the area of optimization, approximation, and design of experiments. Dong-Hoon Choi received a B.S. degree in Mechanical Engineering from Seoul National University in 1975. He then went on to receive his M.S. from KAIST in 1977 and Ph. D. degree from University of Wisconsin-Madison in 1986, respectively. Dr. Choi is currently a Professor at the School of Mechanical Engineering at Hanyang University in Seoul, Korea. He is currently the director of iDOT(the center of innovative design optimization technology). Prof. Choi’s research interests are in the area of optimization techniques: developing MDO methodology, developing optimization techniques to ensure a reliability of optimum solution, and developing approximation optimization technique, etc.     

Fit for purpose? Building and evaluating a fast,integrated model for exploring water policy pathways

Exploring adaptation pathways is an emerging approach for supporting decision making under uncertain changing conditions. An adaptation pathway is a sequence of policy actions to reach specified objectives. To develop adaptation pathways, interactions between environment and policy response need to be analysed over time for an ensemble of plausible futures. A fast, integrated model can facilitate this. Here, we describe the development and evaluation of such a model, an Integrated Assessment Metamodel (IAMM), to explore adaptation pathways in the Rhine delta for a decision problem currently faced by the Dutch Government. The theory-motivated metamodel is a simplified physically based model. Closed questions reflecting the required accuracy were used to evaluate the model's fitness. The results show that such a model fits the purpose of screening and ranking of policy options and pathways to support the strategic decision making. A complex model can subsequently be used to obtain more detailed information.     

专用ETL模式设计与实现

以指挥决策支持系统为背景,构建一个专门针对该系统设计的ETL组件,由此提出专用ETL组件的“ETL+元知识库+交互式环境”的构建模式。该组件采用基于CWM建模的方式来优化对ETL元数据的提取并缩短开发周期。基于领域知识的匹配转换及交互式可扩展环境具有提高数据清洗转换效率,实现复杂转换逻辑的优势,并为各个决策支持系统构建自己的专用ETL组件提供参考。