Uncertainty Analysis of Curing-Induced Dimensional Variability of Composite Structures Utilizing Physics-Guided Gaussian Process Meta-modeling

2021 ◽  
pp. 114816
Author(s):  
Kai Zhou ◽  
Ryan Enos ◽  
Dianyun Zhang ◽  
Jiong Tang
Keyword(s):  
Uncertainty Analysis ◽  
Gaussian Process ◽  
Composite Structures ◽  
Meta Modeling

Uncertainty analysis of composite structures

2000 ◽  
Vol 185 (2-4) ◽  
pp. 413-432 ◽  
Author(s):  
Ahmed K. Noor ◽  
James H. Starnes ◽  
Jeanne M. Peters
Keyword(s):  
Uncertainty Analysis ◽  
Composite Structures

scholarly journals Uncertainty analysis based on sensitivity applied to angle-ply composite structures

2007 ◽  
Vol 92 (10) ◽  
pp. 1353-1362 ◽  
Author(s):  
Carlos Conceição António ◽  
Luísa N. Hoffbauer
Keyword(s):  
Uncertainty Analysis ◽  
Composite Structures

Frequency Response-Based Uncertainty Analysis of Vibration System Utilizing Multiple Response Gaussian Process

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wangbai Pan ◽  
Guoan Tang ◽  
Jiong Tang
Keyword(s):  
Uncertainty Analysis ◽  
Gaussian Process ◽  
Frequency Response ◽  
Model Updating ◽  
Uncertainty Propagation ◽  
Multiple Response ◽  
Vibration System ◽  
Vector Fitting ◽  
Fitting Method ◽  
Small Set

This research concerns the uncertainty analysis and quantification of the vibration system utilizing the frequency response function (FRF) representation with statistical metamodeling. Different from previous statistical metamodels that are built for individual frequency points, in this research we take advantage of the inherent correlation of FRF values at different frequency points and resort to the multiple response Gaussian process (MRGP) approach. To enable the analysis, vector fitting method is adopted to represent an FRF using a reduced set of parameters with high accuracy. Owing to the efficiency and accuracy of the statistical metamodel with a small set of parameters, Bayesian inference can then be incorporated to realize model updating and uncertainty identification as new measurement/evidence is acquired. The MRGP metamodel developed under this new framework can be used effectively for two-way uncertainty propagation analysis, i.e., FRF prediction and uncertainty identification. Case studies are conducted for illustration and verification.

scholarly journals Sensitivity and uncertainty analysis of two human atrial cardiac cell models using Gaussian process emulators

2019 ◽  
Author(s):  
Sam Coveney ◽  
Richard H. Clayton
Keyword(s):  
Cell Membrane ◽  
Action Potential ◽  
Uncertainty Analysis ◽  
Gaussian Process ◽  
Model Parameters ◽  
Cardiac Cell ◽  
Cell Models ◽  
First Order ◽  
Sensitivity Indices ◽  
Sensitivity And Uncertainty Analysis

AbstractCardiac cell models reconstruct the action potential and calcium dynamics of cardiac myocytes, and are becoming widely used research tools. These models are highly detailed, with many parameters in the equations that describe current flow through ion channels, pumps, and exchangers in the cell membrane, and so it is difficult to link changes in model inputs to model behaviours. The aim of the present study was to undertake sensitivity and uncertainty analysis of two models of the human atrial action potential. We used Gaussian processes to emulate the way that 11 features of the action potential and calcium transient produced by each model depended on a set of. The emulators were trained by maximising likelihood conditional on a set of design data, obtained from 300 model evaluations. For each model evaluation, the set of inputs was obtained from uniform distributions centred on the default values for each parameter, using latin-hypercube sampling. First order and total effect sensitivity indices were calculated for each combination of input and output. First order indices were well correlated with the square root of sensitivity indices obtained by partial least squares regression of the design data. The sensitivity indices highlighted a difference in the balance of inward and outward currents during the plateau phase of the action potential in each model, with the consequence that changes to one parameter can have opposite effects in the two models. Overall the interactions among inputs were not as important as the first order effects, indicating that model parameters tend to have independent effects on the model outputs. This study has shown that Gaussian process emulators are an effective tool for sensitivity and uncertainty analysis of cardiac cell models.Author summaryThe time course of the cardiac action potential is determined by the balance of inward and outward currents across the cell membrane, and these in turn depend on dynamic behaviour of ion channels, pumps and exchangers in the cell membrane. Cardiac cell models reconstruct the action potential by representing transmembrane current as a set of stiff and nonlinear ordinary differential equations. These models capture biophysical detail, but are complex and have large numbers of parameters, so cause and effect relationships are difficult to identify. In recent years there has been an increasing interest in uncertainty and variability in computational models, and a number of tools have been developed. In this study we have used one of these tools, Gaussian process emulators, to compare and contrast two models of the human atrial action potential. We obtained sensitivity indices based on the proportion of variance in a model output that is accounted for by variance in each of the model parameters. These sensitivity indices highlighted the model parameters that had the most influence on the model outputs, and provided a means to make a quantitative comparison between the models.

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