scholarly journals Certified metamodels for sensitivity indices estimation

2012 ◽  
Vol 35 ◽  
pp. 234-238 ◽  
Author(s):  
Alexandre Janon ◽  
Maëlle Nodet ◽  
Clémentine Prieur
Keyword(s):  
Sensitivity Indices

scholarly journals Sensitivity Analysis for Microscopic Crowd Simulation

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 162
Author(s):  
Marion Gödel ◽  
Rainer Fischer ◽  
Gerta Köster
Keyword(s):  
Sensitivity Analysis ◽  
Low Cost ◽  
Input Parameter ◽  
Dimensional Subspace ◽  
Crowd Simulation ◽  
Pedestrian Dynamics ◽  
Sensitivity Indices ◽  
Expensive Problems ◽  
Influential Parameters ◽  
Typical Scenario

Microscopic crowd simulation can help to enhance the safety of pedestrians in situations that range from museum visits to music festivals. To obtain a useful prediction, the input parameters must be chosen carefully. In many cases, a lack of knowledge or limited measurement accuracy add uncertainty to the input. In addition, for meaningful parameter studies, we first need to identify the most influential parameters of our parametric computer models. The field of uncertainty quantification offers standardized and fully automatized methods that we believe to be beneficial for pedestrian dynamics. In addition, many methods come at a comparatively low cost, even for computationally expensive problems. This allows for their application to larger scenarios. We aim to identify and adapt fitting methods to microscopic crowd simulation in order to explore their potential in pedestrian dynamics. In this work, we first perform a variance-based sensitivity analysis using Sobol’ indices and then crosscheck the results by a derivative-based measure, the activity scores. We apply both methods to a typical scenario in crowd simulation, a bottleneck. Because constrictions can lead to high crowd densities and delays in evacuations, several experiments and simulation studies have been conducted for this setting. We show qualitative agreement between the results of both methods. Additionally, we identify a one-dimensional subspace in the input parameter space and discuss its impact on the simulation. Moreover, we analyze and interpret the sensitivity indices with respect to the bottleneck scenario.

Product-Oriented Sensitivity Analysis for Multistation Compliant Assemblies

2006 ◽  
Vol 129 (8) ◽  
pp. 844-851 ◽  
Author(s):  
Jianpeng Yue ◽  
Jaime A. Camelio ◽  
Melida Chin ◽  
Wayne Cai
Keyword(s):  
Product Performance ◽  
Sensitivity Index ◽  
Final Assembly ◽  
Sensitivity Indices ◽  
Dimensional Variation ◽  
Product And Process ◽  
Pattern Sensitivity ◽  
Sheet Metal Assembly

Dimensional variation in assembled products directly affects product performance. To reduce dimensional variation, it is necessary that an assembly be robust. A robust assembly is less sensitive to input variation from the product and process components, such as incoming parts, subassemblies, fixtures, and welding guns. In order to effectively understand the sensitivity of an assembly to input variation, an appropriate set of metrics must be defined. In this paper, three product-oriented indices, including pattern sensitivity index, component sensitivity index, and station sensitivity index, are defined. These indices can be utilized to measure the variation influence of a pattern, an individual part, and/or component, and components at a particular station to the dimensional quality of a final assembly. Additionally, the relationships among these sensitivity indices are established. Based on these relationships, the ranges of the sensitivity indices are derived. Finally, a case study of a sheet metal assembly is presented and discussed to illustrate the applicability of these metrics.

scholarly journals Chronic kidney disease and obesity bias surrogate estimates of insulin sensitivity compared with the hyperinsulinemic euglycemic clamp

2017 ◽  
Vol 312 (3) ◽  
pp. E175-E182 ◽  
Author(s):  
Iram Ahmad ◽  
Leila R. Zelnick ◽  
Nicole R. Robinson ◽  
Adriana M. Hung ◽  
Bryan Kestenbaum ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Insulin Sensitivity ◽  
Kidney Disease ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Euglycemic Clamp ◽  
Insulin Kinetics ◽  
Hyperinsulinemic Euglycemic Clamp ◽  
Sensitivity Indices ◽  
Organ Specific

Insulin sensitivity can be measured by procedures such as the hyperinsulinemic euglycemic clamp or by using surrogate indices. Chronic kidney disease (CKD) and obesity may differentially affect these measurements because of changes in insulin kinetics and organ-specific effects on insulin sensitivity. In a cross-sectional study of 59 subjects with nondiabetic CKD [estimated glomerular filtration rate: (GFR) <60 ml·min−1·1.73 m2] and 39 matched healthy controls, we quantified insulin sensitivity by clamp (SIclamp), oral glucose tolerance test, and fasting glucose and insulin. We compared surrogate insulin sensitivity indices to SIclamp using descriptive statistics, graphical analyses, correlation coefficients, and linear regression. Mean age was 62.6 yr; 48% of the participants were female, and 77% were Caucasian. Insulin sensitivity indices were 8–38% lower in participants with vs. without CKD and 13–59% lower in obese compared with nonobese participants. Correlations of surrogate indices with SIclamp did not differ significantly by CKD or obesity status. Adjusting for SIclamp in addition to demographic factors, Matsuda index was 15% lower in participants with vs. without CKD ( P = 0.09) and 36% lower in participants with vs. without obesity ( P = 0.0001), whereas 1/HOMA-IR was 23% lower in participants with vs. without CKD ( P = 0.02) and 46% lower in participants with vs. without obesity ( P < 0.0001). We conclude that CKD and obesity do not significantly alter correlations of surrogate insulin sensitivity indices with SIclamp, but they do bias surrogate measurements of insulin sensitivity toward lower values. This bias may be due to differences in insulin kinetics or organ-specific responses to insulin.

scholarly journals A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures

2021 ◽  
Vol 7 ◽  
Author(s):  
Nikolaos Tsokanas ◽  
Xujia Zhu ◽  
Giuseppe Abbiati ◽  
Stefano Marelli ◽  
Bruno Sudret ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Surrogate Model ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Hybrid Models ◽  
Analysis Framework ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Realistic Situation

Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically-simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling forms the hybrid model. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic, as nominally identical specimens are, in practice, never actually identical. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol’ sensitivity indices are computed for substructure quantity of interest response quantiles. Normally, several repetitions of every single sample of the inputs parameters would be required to replicate the response of a stochastic hybrid model. In this regard, a great advantage of the proposed framework is that the generalized lambda surrogate model does not require repeated evaluations of the same sample. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.

scholarly journals Inchoative discovery of plausible (un)explored synergistic combinatorial biological hypotheses for static/time series Wnt measurements via ranking search engine : BioSearch Engine Design

2018 ◽  
Author(s):  
shriprakash sinha
Keyword(s):  
Search Engine ◽  
Expert Advice ◽  
Support Vector ◽  
Advice Literature ◽  
Time Investment ◽  
Sensitivity Indices ◽  
Engine Design ◽  
Wnt Inhibitor ◽  
Wet Lab ◽  
Time Buffer

BACKGROUND Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major problem in biology is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. The search and wet lab testing of these combinations costs a lot in terms of time, investment and energy. In a recent development of the PORCN-WNT inhibitor ETC-1922159 for colorectal cancer, a list of down-regulated genes were recorded in a time buffer after the administration of the drug. The regulation of the genes were recorded individually but for a majority, it is still not known which higher (≥ 2) order combinations might be playing a greater role in the pathway. RESULTS The pipeline provides a prioritised list of important 2nd order combinations of a range of family of genes involved in the Wnt pathway. More specifically, it reveals the various unexplored FZD-WNT combinations that have been untested till now in the pathway. In relation to ETC-1922159 affected combinations, the down-regulation of LGR-RNF family after the drug treatment is evident in these rankings as it takes bottom priorities for LGR5-RNF43 combination. The LGR6-RNF43 takes higher ranking than LGR5-RNF43, indicating that it might not be playing a greater role as LGR5 during the Wnt enhancing signals. These rankings confirm the efficacy of the proposed search engine design. CONCLUSION A pipeline has been developed to prioritise an nth order combination of factors that affect a signaling pathway. It takes into account the sensitivity indices computed from variance based (SOBOL) and density-kernel based (HSIC) methods to estimate the influence of each factor or combination of factors. These are then fed as feature vectors into a powerful support vector ranking algorithm that produces a ranked list of the interactions/combinations.

scholarly journals A Global Sensitivity Analysis Framework for Hybrid Simulation

2021 ◽  
Author(s):  
Giuseppe Abbiati ◽  
Stefano Marelli ◽  
Nikolaos Tsokanas ◽  
Bruno Sudret ◽  
Bozidar Stojadinovic
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion ◽  
Analysis Framework ◽  
Global Sensitivity ◽  
Sensitivity Indices

Hybrid Simulation is a dynamic response simulation paradigm that merges physical experiments and computational models into a hybrid model. In earthquake engineering, it is used to investigate the response of structures to earthquake excitation. In the context of response to extreme loads, the structure, its boundary conditions, damping, and the ground motion excitation itself are all subjected to large parameter variability. However, in current seismic response testing practice, Hybrid Simulation campaigns rely on a few prototype structures with fixed parameters subjected to one or two ground motions of different intensity. While this approach effectively reveals structural weaknesses, it does not reveal the sensitivity of structure's response. This thus far missing information could support the planning of further experiments as well as drive modeling choices in subsequent analysis and evaluation phases of the structural design process.This paper describes a Global Sensitivity Analysis framework for Hybrid Simulation. This framework, based on Sobol' sensitivity indices, is used to quantify the sensitivity of the response of a structure tested using the Hybrid Simulation approach due to the variability of the prototype structure and the excitation parameters. Polynomial Chaos Expansion is used to surrogate the hybrid model response. Thereafter, Sobol' sensitivity indices are obtained as a by-product of polynomial coefficients, entailing a reduced number of Hybrid Simulations compared to a crude Monte Carlo approach. An experimental verification example highlights the excellent performance of Polynomial Chaos Expansion surrogates in terms of stable estimates of Sobol' sensitivity indices in the presence of noise caused by random experimental errors.

scholarly journals Sensitivity Analysis of 3-RPR Planar Parallel Manipulators

2009 ◽  
Vol 131 (12) ◽  
Author(s):  
Stéphane Caro ◽  
Nicolas Binaud ◽  
Philippe Wenger
Keyword(s):  
Sensitivity Analysis ◽  
Parallel Manipulators ◽  
Geometric Parameters ◽  
Sensitivity Coefficients ◽  
Sensitivity Indices ◽  
Moving Platform ◽  
Planar Parallel Manipulators

This paper deals with the sensitivity analysis of 3-RPR planar parallel manipulators (PPMs). First, the sensitivity coefficients of the pose of the manipulator moving platform to variations in the geometric parameters and in the actuated variables are expressed algebraically. Moreover, two aggregate sensitivity indices are determined, one related to the orientation of the manipulator moving platform and another one related to its position. Then, a methodology is proposed to compare 3-RPR PPMs with regard to their dexterity, workspace size and sensitivity. Finally, the sensitivity of a 3-RPR PPM is analyzed in detail and four 3-RPR PPMs are compared as illustrative examples.

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