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This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels. Keywords Simul...

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are inherently dynamic because of the large variations in the influent wastewater flow rate, concentration and composition. Moreover, these variations are to a large extent not possible to control. The adaptive behaviour of the involved microorganisms imposes further difficulties in terms of time-varying process parameters. Mathematical models and computer simulations are essential to describe, predict and control the complicated interactions of the processes. The number of reactions and organism species that are involved in the system may be very large. An accurate description of such systems can therefore result in highly complex models, which may not be very useful from a practical, operational point of view. The thesis contains a thorough discussion on aspects concerning the mathematical modelling of the activated sludge, sedimentation and biofilm processes. A reduced order dynamic model, describing an activated sludge process

During the development of car engines, regression models that are based on machine learning techniques are increasingly important for tasks which require a prediction of results in real-time. While the validation of a model is a key part of its identification process, existing computation- or visualization-based techniques do not adequately support all aspects of model validation. The main contribution of this paper is an interactive approach called HyperMoVal that is designed to support multiple tasks related to model validation: 1) comparing known and predicted results, 2) analyzing regions with a bad fit, 3) assessing the physical plausibility of models also outside regions covered by validation data, and 4) comparing multiple models. The key idea is to visually relate one or more n-dimensional scalar functions to known validation data within a combined visualization. HyperMoVal lays out multiple 2D and 3D sub-projections of the n-dimensional function space around a focal point. We describe how linking HyperMoVal to other views further extends the possibilities for model validation. Based on this integration, we discuss steps towards supporting the entire workflow of identifying regression models. An evaluation illustrates a typical workflow in the application context of car-engine design and reports general feedback of domain experts and users of our approach. These results indicate that our approach significantly accelerates the identification of regression models and increases the confidence in the overall engineering process.

Simulation is introduced in terms of its different forms and uses, but the focus on discrete event modeling for systems analysis is dominant as it has been during the evolution of the technique within operations research and the management sciences. This evolutionary trace of over almost fifty years notes the importance of bidirectional influences with computer science, probability and statistics, and mathematics. No area within the scope of operations research and the management sciences has been affected more by advances in computing technology than simulation. This assertion is affirmed in the review of progress in those technical areas that collectively define the art and science of simulation. A holistic description of the field must include the roles of professional societies, conferences and symposia, and publications. The closing citation of a scientific value judgment from over 30 years in the past hopefully provides a stimulus for contemplating what lies ahead in the next 50 years.

Many systems rely on the ability to rollback (or restore) parts of the system state to undo or recover from undesired or erroneous computations. Examples of such systems include fault tolerant systems with checkpointing, editors with undo capabilities, transaction and data base systems and optimistically synchronized parallel and distributed simulations. An essential part of such systems is the state saving mechanism. It should not only allow efficient state saving, but also support efficient state restoration in case of roll back. Furthermore, it is often a requirement that this mechanism is transparent to the user. In this paper we present a method to implement a transparent incremental state saving mechanism in an optimistically synchronized parallel discrete event simulation system based on the Time Warp mechanism. The usefulness of this approach is demonstrated by simulations of large, detailed, realistic FCA and a DCA-like cellular phone systems. 1. Introduction Many systems rel...

Subjective methods for operational validity are presented that use graphical displays of histograms, box plots, and behavior graphs. These methods allow the data to be correlated, have any statistical distribution, and be limited in the number of observations. Model data are used for the reference distribution (instead of a theoretical distribution such as the t or F) and for reference to compare the system data against. These methods are very general and can be used in validating different types of models. 1

Abstract. Many advances in grid resource management are still required to realize the grid computing vision of the integration of a worldwide computing infrastructure for scientific use. The pressure for advances is increased by the fast evolution of single, large clusters, which are the primary technological alternative to grids. However, advances in grid resource management cannot be achieved without an appropriate toolbox, of which simulation environments form an essential part. The current grid simulation environments still lack important workload and system modeling features, and research productivity features such as automated experiment setup and management. In this paper we address these issues through the design and a reference implementation of DGSim, a framework for simulating grid resource management architectures. DGSim introduces the concepts of grid evolution and of job selection policy, and extends towards realism the current body of knowledge on grid inter-operation, on grid dynamics, and on workload modeling. We also show through two real use cases how DGSim canbeusedto compare grid resource management architectures.

Linking a GIS to a spatially distributed, physically-based environmental model offers many advantages. However, the implementation of such linkages is generally problematic. Many problems arise because the relationship between the reality being represented by the mathematical model and the data model used to organize the spatial data in the GIS has not been rigorously defined. In particular, while many environmental models are based on theories that assume continuity and incorporate physical fields as independent variables, current GISs can only represent continuous phenomena in a variety of discrete data models. This document develops and outlines a strategy in which field variables are used to enable modelers to work directly with the spatial data as spatially continuous phenomena. Field variables are declared like other data types in standard computing languages. Specifications...