Results 181 to 190 of about 767,107 (236)
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Reliability Engineering and System Safety, 2020
For many engineering problems, it is important to know which random input variables have significant influence on relevant outputs, since, for example, these inputs are of special interest in optimisation tasks or their uncertainty can significantly ...
C. Hübler
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For many engineering problems, it is important to know which random input variables have significant influence on relevant outputs, since, for example, these inputs are of special interest in optimisation tasks or their uncertainty can significantly ...
C. Hübler
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A Stochastic Collocation Algorithm with Multifidelity Models
SIAM Journal on Scientific Computing, 2014We present a numerical method for utilizing stochastic models with differing fideli- ties to approximate parameterized functions. A representative case is where a high-fidelity and a low-fidelity model are available. The low-fidelity model represents a coarse and rather crude ap- proximation to the underlying physical system.
Akil Narayan +2 more
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A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
SIAM Journal on Numerical Analysis, 2007I. Babuska, F. Nobile, R. Tempone
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Evaluation of Designed Distributions for Stochastic Collocation Methods
AIAA SCITECH 2023 Forum, 2023Edwin E. Forster +2 more
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Uncertainty Quantification of RF Circuits Using Stochastic Collocation Techniques
IEEE Electromagnetic Compatibility Magazine, 2022This paper presents a study on the Polynomial Chaos based approach for uncertainty quantification. It discusses employing different polynomial chaos based techniques for uncertainty quantification of RF circuits.
Aksh Chordia, J. Tripathi
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STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION
International Journal for Uncertainty Quantification, 2012Summary: The idea of \(\ell_1\)-minimization is the basis of the widely adopted compressive sensing method for function approximation. In this paper, we extend its application to high-dimensional stochastic collocation methods. To facilitate practical implementation, we employ orthogonal polynomials, particularly Legendre polynomials, as basis ...
Yan, Liang, Guo, Ling, Xiu, Dongbin
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Uncertainty Quantification of a CMOS Oscillator using Stochastic Collocation Techniques
2021 IEEE International Joint EMC/SI/PI and EMC Europe Symposium, 2021In recent years, stochastic techniques have emerged as computationally superior techniques for Uncertainty Quantification (UQ). This paper focuses on the application of different stochastic techniques based on Stochastic Collocation (SC) for UQ.
Aksh Chordia, J. Tripathi
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Global Sensitivity Analysis for Stochastic Collocation
51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference<BR> 18th AIAA/ASME/AHS Adaptive Structures Conference<BR> 12th, 2010Non-intrusive stochastic expansion methods for uncertainty quantication (UQ) has received a great deal of attention the past decade because of their rigorous mathematical foundations and their ability to e ciently accurately characterize the probablilistic metrics of complex engineering systems.
Gary Tang +2 more
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Unscented transform and stochastic collocation methods for stochastic electromagnetic compatibility
CEM'11 Computational Electromagnetics International Workshop, 2011This paper deals with the current growing interest concerning the use of stochastic techniques for electromagnetic compatability (EMC) issues. Various methods allow to face this problem: obviously, we may focus on the Monte Carlo (MC) formalism but other techniques have been implemented more recently (the unscented transform, UT, or stochastic ...
Sebastien Lallechere +3 more
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Energy, 2020
In the present paper, the Nested Sparse-grid Stochastic Collocation Method (NSSCM) is utilized to investigate the uncertain effects of stochastic blade stagger angles on the aerodynamic performance of the turbine blade.
Wang Kun +3 more
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In the present paper, the Nested Sparse-grid Stochastic Collocation Method (NSSCM) is utilized to investigate the uncertain effects of stochastic blade stagger angles on the aerodynamic performance of the turbine blade.
Wang Kun +3 more
semanticscholar +1 more source