Decision-Theoretic Sensitivity Analysis for Complex Computer Models

Technometrics ◽  
2009 ◽  
Vol 51 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Jeremy E. Oakley
Keyword(s):  
Sensitivity Analysis ◽  
Computer Models ◽  
Complex Computer

Simple approach to emulating complex computer models for global sensitivity analysis

2015 ◽  
Vol 74 ◽  
pp. 140-155 ◽  
Author(s):  
Bryan Stanfill ◽  
Henrike Mielenz ◽  
David Clifford ◽  
Peter Thorburn
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Computer Models ◽  
Simple Approach ◽  
Global Sensitivity ◽  
Complex Computer

Emulated Multivariate Global Sensitivity Analysis for Complex Computer Models Applied to Agricultural Simulators

2018 ◽  
Vol 24 (1) ◽  
pp. 130-153 ◽  
Author(s):  
Daniel W. Gladish ◽  
Ross Darnell ◽  
Peter J. Thorburn ◽  
Bhakti Haldankar
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Computer Models ◽  
Global Sensitivity ◽  
Complex Computer

Parameter Screening in Statistical Dynamic Computer Model Calibration Using Global Sensitivities

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Dorin Drignei ◽  
Zissimos P. Mourelatos
Keyword(s):  
Sensitivity Analysis ◽  
Computer Model ◽  
Model Calibration ◽  
Simulation Models ◽  
Computer Models ◽  
Automotive Application ◽  
Transient Signals ◽  
Sensitivity Indices ◽  
Calibration Algorithm ◽  
Computationally Intensive

Computer, or simulation, models are ubiquitous in science and engineering. Two research topics in building computer models, generally treated separately, are sensitivity analysis and computer model calibration. In sensitivity analysis, one quantifies the effect of each input factor on outputs, whereas in calibration, one finds the values of input factors that provide the best match to a set of test data. In this article, we show a connection between these two seemingly separate concepts for problems with transient signals. We use global sensitivity analysis for computer models with transient signals to screen out inactive input factors, thus making the calibration algorithm numerically more stable. We show that the computer model does not vary with respect to parameters having zero total sensitivity indices, indicating that such parameters are impossible to calibrate and must be screened out. Because the computer model can be computationally intensive, we construct a fast statistical surrogate of the computer model which is used for both sensitivity analysis and computer model calibration. We illustrate our approach with both a simple example and an automotive application involving a road load data acquisition (RLDA) computer model.

Uncertainty Quantification of Complement Sensitivity Indices in Dynamic Computer Models

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Dorin Drignei ◽  
Zissimos Mourelatos ◽  
Zhen Hu
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Computer Models ◽  
Time Dependent ◽  
Sensitivity Indices ◽  
Type Information

This paper addresses the sensitivity analysis of time-dependent computer models. Often, in practice, we partition the inputs into a subset of inputs relevant to the application studied, and a complement subset of nuisance inputs that are not of interest. We propose sensitivity measures for the relevant inputs of such dynamic computer models. The subset of nuisance inputs is used to create replication-type information to help quantify the uncertainty of sensitivity measures (or indices) for the relevant inputs. The method is first demonstrated on an analytical example. Then we use the proposed method in an application about the safety of restraint systems in light tactical vehicles. The method indicates that chest deflection curves are more sensitive to the addition of pretensioners and load limiters than to the type of seatbelt.

Sensitivity Analysis of Computer Models

2015 ◽  
pp. 1-12
Author(s):  
Alessandro Fassò
Keyword(s):  
Sensitivity Analysis ◽  
Computer Models

scholarly journals Known Boundary Emulation of Complex Computer Models

2019 ◽  
Vol 7 (3) ◽  
pp. 838-876 ◽  
Author(s):  
Ian Vernon ◽  
Samuel E. Jackson ◽  
Jonathan A. Cumming
Keyword(s):  
Computer Models ◽  
Complex Computer

scholarly journals The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources

2018 ◽  
Vol 11 (8) ◽  
pp. 3159-3185 ◽  
Author(s):  
Anthony P. Walker ◽  
Ming Ye ◽  
Dan Lu ◽  
Martin G. De Kauwe ◽  
Lianhong Gu ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Epistemic Uncertainty ◽  
Computer Models ◽  
System Model ◽  
Model Description ◽  
Model Structure ◽  
Multiple Sources ◽  
Specific System ◽  
Process Representation ◽  
Parameter Values

Abstract. Computer models are ubiquitous tools used to represent systems across many scientific and engineering domains. For any given system, many computer models exist, each built on different assumptions and demonstrating variability in the ways in which these systems can be represented. This variability is known as epistemic uncertainty, i.e. uncertainty in our knowledge of how these systems operate. Two primary sources of epistemic uncertainty are (1) uncertain parameter values and (2) uncertain mathematical representations of the processes that comprise the system. Many formal methods exist to analyse parameter-based epistemic uncertainty, while process-representation-based epistemic uncertainty is often analysed post hoc, incompletely, informally, or is ignored. In this model description paper we present the multi-assumption architecture and testbed (MAAT v1.0) designed to formally and completely analyse process-representation-based epistemic uncertainty. MAAT is a modular modelling code that can simply and efficiently vary model structure (process representation), allowing for the generation and running of large model ensembles that vary in process representation, parameters, parameter values, and environmental conditions during a single execution of the code. MAAT v1.0 approaches epistemic uncertainty through sensitivity analysis, assigning variability in model output to processes (process representation and parameters) or to individual parameters. In this model description paper we describe MAAT and, by using a simple groundwater model example, verify that the sensitivity analysis algorithms have been correctly implemented. The main system model currently coded in MAAT is a unified, leaf-scale enzyme kinetic model of C3 photosynthesis. In the Appendix we describe the photosynthesis model and the unification of multiple representations of photosynthetic processes. The numerical solution to leaf-scale photosynthesis is verified and examples of process variability in temperature response functions are provided. For rapid application to new systems, the MAAT algorithms for efficient variation of model structure and sensitivity analysis are agnostic of the specific system model employed. Therefore MAAT provides a tool for the development of novel or toy models in many domains, i.e. not only photosynthesis, facilitating rapid informal and formal comparison of alternative modelling approaches.

scholarly journals Global sensitivity analysis of stochastic computer models with joint metamodels

2011 ◽  
Vol 22 (3) ◽  
pp. 833-847 ◽  
Author(s):  
Amandine Marrel ◽  
Bertrand Iooss ◽  
Sébastien Da Veiga ◽  
Mathieu Ribatet
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Computer Models ◽  
Global Sensitivity
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