scholarly journals Sensitivity analysis for variational inclusions by Wiener-Hopf equation techniques

1999 ◽  
Vol 12 (3) ◽  
pp. 223-232 ◽  
Author(s):  
Abdellatif Moudafi ◽  
Muhammad Aslam Noor
Keyword(s):  
Sensitivity Analysis ◽  
Variational Inequalities ◽  
Unique Solution ◽  
Lipschitz Continuity ◽  
Variational Inclusions ◽  
Analysis Framework ◽  
Hopf Equation ◽  
The Given

In this paper, we extend the sensitivity analysis framework developed recently for variational inequalities by Noor and Yen to variational inclusions relying on Wiener-Hopf equation techniques. We prove the continuity and the Lipschitz continuity of the locally unique solution to parametric variational inclusions without assuming differentiability of the given data.

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 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 Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones

2018 ◽  
Vol 146 (7) ◽  
pp. 2065-2088 ◽  
Author(s):  
Fei He ◽  
Derek J. Posselt ◽  
Naveen N. Narisetty ◽  
Colin M. Zarzycki ◽  
Vijayan N. Nair
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Radiative Forcing ◽  
Initial Conditions ◽  
Sea Surface ◽  
Precipitation Rate ◽  
Analysis Framework ◽  
Control Factors

Abstract This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

3D Object Modeling Using Sketches

2013 ◽  
pp. 238-256
Author(s):  
Sofia Kyratzi ◽  
Nickolas S. Sapidis
Keyword(s):  
Unique Solution ◽  
User Interaction ◽  
Solid Model ◽  
Object Modeling ◽  
Step Process ◽  
3D Object ◽  
Partial View ◽  
The Given ◽  
3D Object Modeling

In this paper, the authors present a new algorithm for constructing a solid model when the given input is only one partial-view sketch (“natural sketch”). This algorithm is a two-step process, where first a complete (wireframe) sketch is derived, which is then transformed into a 3D polyhedron. The paper details topological and geometric aspects of the process, as well as the essential “user-interaction” components dealing with cases where the sketch-to-solid problem does not have a unique solution.

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