Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Hu, Jie; Wang, Yan; Cheng, Aiguo; Zhong, Zhihua

doi:10.1115/1.4029513

Cited by 4 publications

(2 citation statements)

References 60 publications

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“…Subsequently, this work was extended to more general set-based representations [34]. Hu et al proposed a method that uses generalized interval to solve for the feasible set [35,36]. The NUMER-ICA [37] modeling language in particular guarantees correctness, completeness, and certainty.…”

Section: Generalized Inverse Phase Stability Problem As a Continuous Constraint Satisfaction Problemmentioning

confidence: 99%

A Constraint Satisfaction Algorithm for the Generalized Inverse Phase Stability Problem

Galván

Malak

Gibbons

et al. 2016

Journal of Mechanical Design

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Researchers have used the (calculation of phase diagram) CALPHAD method to solve the forward phase stability problem of mapping from specific thermodynamic conditions (material composition, temperature, pressure, etc.) to the associated phase constitution. Recently, optimization has been used to solve the inverse problem: mapping specific phase constitutions to the thermodynamic conditions that give rise to them. These pointwise results, however, are of limited value since they do not provide information about the forces driving the point to equilibrium. In this paper, we investigate the problem of mapping a desirable region in the phase constitution space to corresponding regions in the space of thermodynamic conditions. We term this problem the generalized inverse phase stability problem (GIPSP) and model the problem as a continuous constraint satisfaction problem (CCSP). In this paper, we propose a new CCSP algorithm tailored for the GIPSP. We investigate the performance of the algorithm on Fe–Ti binary alloy system using ThermoCalc with the TCFE7 database against a related algorithm. The algorithm is able to generate solutions for this problem with high performance.

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Section: Generalized Inverse Phase Stability Problem As a Continuous Constraint Satisfaction Problemmentioning

confidence: 99%

A Constraint Satisfaction Algorithm for the Generalized Inverse Phase Stability Problem

Galván

Malak

Gibbons

et al. 2016

Journal of Mechanical Design

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“…Sensitivity analysis can allow us to determine which parameters are the most influential factor on the model output, and select the most relevant parameters to be adjusted in less computational time [102,103]. A common method to perform the sensitivity analysis is to vary each parameter while fixing the other parameters, one factor at a time, to check the impact of the parameter on output [83,104].…”

Section: Sensitivity Analysis Of Model Parametersmentioning

confidence: 99%

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

et al. 2016

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Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

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Model-Form Calibration in Drift-Diffusion Simulation Using Fractional Derivatives

Wang

2016

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering

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In modeling and simulation, model-form uncertainty arises from the lack of knowledge and simplification during the modeling process and numerical treatment for ease of computation. Traditional uncertainty quantification (UQ) approaches are based on assumptions of stochasticity in real, reciprocal, or functional spaces to make them computationally tractable. This makes the prediction of important quantities of interest, such as rare events, difficult. In this paper, a new approach to capture model-form uncertainty is proposed. It is based on fractional calculus, and its flexibility allows us to model a family of non-Gaussian processes, which provides a more generic description of the physical world. A generalized fractional Fokker–Planck equation (fFPE) is used to describe the drift-diffusion processes under long-range correlations and memory effects. A new model-calibration approach based on the maximum mutual information is proposed to reduce model-form uncertainty, where an optimization procedure is taken.

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Sensitivity Analysis in Quantified Interval Constraint Satisfaction Problems

Cited by 4 publications

References 60 publications

A Constraint Satisfaction Algorithm for the Generalized Inverse Phase Stability Problem

A Constraint Satisfaction Algorithm for the Generalized Inverse Phase Stability Problem

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

Model-Form Calibration in Drift-Diffusion Simulation Using Fractional Derivatives

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