Uncertainty and Sensitivity Analysis for Models of Complex Systems

Helton, Jon C.

doi:10.1007/978-3-540-77362-7_9

Cited by 31 publications

(25 citation statements)

References 78 publications

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“…Sensitivity analysis can be applied to reduce the system [14]. There are total and local methods for analyzing the dependence of the solution of a set of kinetic equations on the reaction rate constants [18][19][20]. Unlike local sensitivity study, the global sensitivity analysis allows to analyze the behavior of kinetic curves over the entire region of parameters, quantitatively estimates the effect of certain variables and their groups, and distinguishes important and unimportant parameters of the model [21].…”

Section: The Sobol's Sensitivity Analysismentioning

confidence: 99%

Few-Step Kinetic Model of Gaseous Autocatalytic Ethane Pyrolysis and Its Evaluation by Means of Uncertainty and Sensitivity Analysis

Safiullina

Stoyanovskaya²,

Стадніченко³

et al. 2014

Chemical Product and Process Modeling

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A kinetic scheme of radical chain reactions in autocatalytic pyrolysis of ethane was studied using a sensitivity analysis method, bringing in the experimental data. In the gas-phase kinetic experiments, ethane pyrolysis was carried out in laboratory reactors with the reaction mixture heated by CO 2 laser irradiation. It was shown that the scheme with autocatalytic routes includes as few steps as possible and adequately describes the ethane pyrolysis with high ethylene yield at 900-1,150 K. Admissible variation ranges of preexponential factors and activation energies for the kinetic model of the reactions were found using the Monte Carlo statistical method. Reducibility of the scheme was examined by means of the Sobol's variance based strategy applied for the sensitivity analysis evaluation.

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Section: The Sobol's Sensitivity Analysismentioning

confidence: 99%

Few-Step Kinetic Model of Gaseous Autocatalytic Ethane Pyrolysis and Its Evaluation by Means of Uncertainty and Sensitivity Analysis

Safiullina

Stoyanovskaya²,

Стадніченко³

et al. 2014

Chemical Product and Process Modeling

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“…The analysis of multi‐run simulations by analyzing ensembles of potentially complex data has become an important yet challenging visualization topic [WP09]. Key issues include providing an overview of the result space as well as relating the parameter to the result space and vice versa for sensitivity analysis and uncertainty analysis [Hel08]. If the result space consists of – or has been reduced to – one or more scalar dimensions, common multivariate visualization techniques like scatterplots and parallel coordinates can be applied [BPFG11].…”

Section: Related Workmentioning

confidence: 99%

“…Multi-run simulations are important for studying complex systems in science and engineering [Hel08]. Depending on the type of simulation, results may have different characteristics.…”

Section: Introductionmentioning

confidence: 99%

Comparative Visual Analysis of 2D Function Ensembles

Piringer

Pajer

Berger

et al. 2012

Computer Graphics Forum

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In the development process of powertrain systems, 2D function ensembles frequently occur in the context of multi‐run simulations. An analysis has many facets, including distributions of extracted features, comparisons between ensemble members and target functions, and details‐on‐demand. The primary contribution of this paper is a design study of an interactive approach for a comparative visual analysis of 2D function ensembles. The design focuses on a tight integration of domain‐oriented and member‐oriented visualization techniques, and it seeks to preserve the mental model of 2D functions on multiple levels of detail. In this context, we propose a novel focus+context approach for visualizations relying on data‐driven placement which is based on labeling. We also extend work on feature‐preserving downsampling of 2D functions. Our design supports a comparison of 2D functions based on juxtaposition, overlay, and explicit differences. It also enables an analysis in terms of extracted scalar features and 1D aggregations. An evaluation illustrates a workflow in our application context. User feedback indicates a time saving of 70% for common tasks and a qualitative gain for the entire development process.

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“…In practice, often only the aggregated data is further analyzed using statistical tools and static visualizations [Cra05, Hel08]. However, we integrate both the multi‐run and aggregated data part in an interactive visual analysis process where they are related to each other.…”

Section: A Moment‐based Scheme For Visual Analysismentioning

confidence: 99%

Brushing Moments in Interactive Visual Analysis

Kehrer

Filzmoser²,

Hauser

2010

Computer Graphics Forum

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We present a systematic study of opportunities for the interactive visual analysis of multi-dimensional scientific data that is based on the integration of statistical aggregations along selected independent data dimensions in a framework of coordinated multiple views (with linking and brushing). Traditional and robust estimates of the four statistical moments (mean, variance, skewness, and kurtosis) as well as measures of outlyingness are integrated in an iterative visual analysis process. Brushing particular statistics, the analyst can investigate data characteristics such as trends and outliers. We present a categorization of beneficial combinations of attributes in 2D scatterplots: (a) k th vs. (k + 1) th statistical moment of a traditional or robust estimate, (b) traditional vs. robust version of the same moment, (c) two different robust estimates of the same moment. We propose selected view transformations to iteratively construct this multitude of informative views as well as to enhance the depiction of the statistical properties in scatterplots and quantile plots. In the framework, we interrelate the original distributional data and the aggregated statistics, which allows the analyst to work with both data representations simultaneously. We demonstrate our approach in the context of two visual analysis scenarios of multi-run climate simulations.

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Uncertainty and Sensitivity Analysis for Models of Complex Systems

Cited by 31 publications

References 78 publications

Few-Step Kinetic Model of Gaseous Autocatalytic Ethane Pyrolysis and Its Evaluation by Means of Uncertainty and Sensitivity Analysis

Few-Step Kinetic Model of Gaseous Autocatalytic Ethane Pyrolysis and Its Evaluation by Means of Uncertainty and Sensitivity Analysis

Comparative Visual Analysis of 2D Function Ensembles

Brushing Moments in Interactive Visual Analysis

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