scholarly journals Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models

2019 ◽  
Vol 11 (9) ◽  
pp. 2896-2909 ◽  
Author(s):  
Xueli Huo ◽  
Hoshin Gupta ◽  
Guo‐Yue Niu ◽  
Wei Gong ◽  
Qingyun Duan
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Environmental Systems ◽  
Spatially Distributed ◽  
Systems Models ◽  
Computationally Intensive

Parameter Sensitivity Analysis of a Large-Scale System With Several Components Interacting in Series

1989 ◽  
Author(s):  
H. Torab
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Optimization Problems ◽  
Parameter Sensitivity ◽  
Engineering Optimization ◽  
Parameter Sensitivity Analysis ◽  
Large Scale Systems ◽  
In Series ◽  
Special Case ◽  
Engineering Optimization Problems

Abstract Parameter sensitivity for large-scale systems that include several components which interface in series is presented. Large-scale systems can be divided into components or sub-systems to avoid excessive calculations in determining their optimum design. Model Coordination Method of Decomposition (MCMD) is one of the most commonly used methods to solve large-scale engineering optimization problems. In the Model Coordination Method of Decomposition, the vector of coordinating variables can be partitioned into two sub-vectors for systems with several components interacting in series. The first sub-vector consists of those variables that are common among all or most of the elements. The other sub-vector consists of those variables that are common between only two components that are in series. This study focuses on a parameter sensitivity analysis for this special case using MCMD.

scholarly journals Parameter Sensitivity Analysis of a Stochastic Ca2+ Spark Model using Novel Computational Methods

2012 ◽  
Vol 102 (3) ◽  
pp. 100a-101a
Author(s):  
Young-Seon Lee ◽  
Ona Liu ◽  
Bjorn C. Knollmann ◽  
Eric A. Sobie
Keyword(s):  
Sensitivity Analysis ◽  
Computational Methods ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis
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