The Simulation Metamodel

Friedman, Linda Weiser

doi:10.1007/978-1-4613-1299-4

Cited by 46 publications

(34 citation statements)

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“…Sometimes a simpler analytic model may be used as an auxiliary to the simulation model. This auxiliary model is often referred to as a metamodel [2]. Metamodels may have different goals: model simplification and interpretation [3], optimization [4], what-if analysis 151, and generalization to models of the same type.…”

Section: H Arshammentioning

confidence: 99%

“…Sometimes a simple analytical model may be used as an auxiliary to the simulation model. This auxiliary model is often referred to as a metamodel [2]. In this treatment, we have to simulate the system for some different values of (v) and then use a "goodnessof-fit" regression.…”

Section: A Polynomial Metamodel Based On a Single-run Simulationmentioning

confidence: 99%

“…The numerical result based on (2) is a solution to a direct problem: Given the underlying pdf with a particular parameter value vo, estimate the output function J(vo). The direct problem is widely used in stochastic systems analysis.…”

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confidence: 99%

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Input parameters to achieve target performance in stochastic systems: a simulation-based approach

Arsham

1999

Inverse Problems in Engineering

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Communicated by G. S. DulikravichThe use of simulation as an engineering tool to design complex stochastic systems is often inhibited by cost. Extensive computer processing is needed to solve the inverse problem which deals with the calculation of a design parameter given a desired target for the performance measure of a given system. The designer simulates the process numerically and obtains an approximation for that same output. The goal is to match the numerical and experimental results as closely as possible by varying the values of input parameters in the numerical simulation. The most obvious difficulty in solving the inverse problem is that one cannot simply calculate a straightforward solution and be done. Since the output has to be matched by varying the input, an iterative method of solution is implied. This paper proposes a "stochastic approximation" algorithm to estimate the necessary controllable input parameters within a desired accuracy given a target value for the performance function. The proposed solution algorithm is based on Newton's methods using a singlerun simulation approach to estimate the needed derivative. The proposed approach may be viewed as an optimization scheme, where a loss function must be minimized. The solution algorithm properties and the validity of the estimates are examined by applying it to some reliability and queueing systems with known analytical solutions.

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Section: H Arshammentioning

confidence: 99%

Section: A Polynomial Metamodel Based On a Single-run Simulationmentioning

confidence: 99%

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Input parameters to achieve target performance in stochastic systems: a simulation-based approach

Arsham

1999

Inverse Problems in Engineering

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“…regression models (Kleijnen, 1979), ANNs (Fonseca et al, 2003), radial basis functions (Hussain et al, 2002), Kriging or spatial correlation models (Kleijnen, 2008;Ankenman et al, 2010), response surfaces (Kleijnen et al, 2000), game theory models (Poropudas and Virtanen, 2010) and proper orthogonal decomposition (Buljak, 2012). Those models are used for simplifying and interpreting simulation models (Friedman, 1996), conducting sensitivity and what-if analyses as well as optimizing the simulation output (Cheng and Currie, 2004). In geotechnical problems, ANNs have been applied as meta models trained with numerical simulations and used for the prediction of the deformations induced by geotechnical interventions (Duan and Sterling, 2002;Tsekouras et al, 2008;Pichler et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Meta models (Kleijnen, 2008;Friedman, 1996) are used to represent the dependency between the simulation input and output, learning the multiple relations between different parameters and mapping them to a given output. Different methods are used to provide compact representations for the simulation model, e.g.…”

Section: Introductionmentioning

confidence: 99%

Model update and real-time steering of tunnel boring machines using simulation-based meta models

Ninić

Meschke

2015

Tunnelling and Underground Space Technology

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a b s t r a c tA method for the simulation supported steering of the mechanized tunneling process in real time during construction is proposed. To enable real-time predictions of tunneling induced surface settlements, meta models trained a priori from a comprehensive process-oriented computational simulation model for mechanized tunneling for a certain project section of interest are introduced. For the generation of the meta models, Artificial Neural Networks (ANN) are employed in conjunction with Particle Swarm Optimization (PSO) for the model update according to monitoring data obtained during construction and for the optimization of machine parameters to keep surface settlements below a given tolerance. To provide a rich data base for the training of the meta model, the finite element simulation model for tunneling is integrated within an automatic data generator for setting up, running and postprocessing the numerical simulations for a prescribed range of parameters. Using the PSO-ANN for the inverse analysis, i.e. identification of model parameters according to monitoring results obtained during tunnel advance, allows the update of the model to the actual geological conditions in real time. The same ANN in conjunction with the PSO is also used for the determination of optimal steering parameters based on target values for settlements in the forthcoming excavation steps. The paper shows the performance of the proposed simulation-based model update and computational steering procedure by means of a prototype application to a straight tunnel advance in a non-homogeneous soil with two soil layers separated by an inclined boundary.

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A metamodeling approach for the simulation of energy and media demand for the brewing industry

Bär

Voigt

2021

J Adv Manuf & Process

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Due to economic, social and technical trends, as well as rising energy costs, the food and beverage industry is challenged to work energy‐efficiently. Small and medium‐sized enterprises in particular lack of both time and knowledge to identify and implement suitable energy efficiency measures. With the help of simulation, decision‐makers can pursue numerous approaches and make well‐founded decisions. A metamodel based on four modeling pillars (physical‐, process‐, article/recipe‐, and production plan model) for an entire production system concerning the forecast of energy and media consumption is presented. The metamodel is implemented in a user‐friendly modeling tool, which enables a simple hybrid modeling without prequalification of the user. Subsequently, a standardized data exchange file, as basis for a simulation model, is generated. For validation, various use cases were modeled and the tool was validated using a systematic test. In addition, two simulation studies were performed to show that the presented approach provides the opportunity to create a holistic model in terms of forecasting energy and media consumption.

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The Simulation Metamodel

Cited by 46 publications

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Input parameters to achieve target performance in stochastic systems: a simulation-based approach

Input parameters to achieve target performance in stochastic systems: a simulation-based approach

Model update and real-time steering of tunnel boring machines using simulation-based meta models

A metamodeling approach for the simulation of energy and media demand for the brewing industry

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