Itô SDE--based Generator for a Class of Non-Gaussian Vector-valued Random Fields in Uncertainty Quantification

SIAM Journal on Scientific Computing ◽

10.1137/130948586 ◽

2014 ◽

Vol 36 (6) ◽

pp. A2763-A2786 ◽

Cited By ~ 14

Author(s):

Johann Guilleminot ◽

Christian Soize

Keyword(s):

Uncertainty Quantification ◽

Random Fields ◽

Gaussian Vector ◽

Non Gaussian ◽

Vector Valued

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Statistical inverse problems for non-Gaussian vector valued random fields with a set of experimental realizations

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures ◽

10.1201/b16387-407 ◽

2014 ◽

pp. 2811-2816

Author(s):

G Perrin ◽

C Soize ◽

D Duhamel ◽

C Funfschilling

Keyword(s):

Inverse Problems ◽

Random Fields ◽

Gaussian Vector ◽

Statistical Inverse Problems ◽

Non Gaussian ◽

Vector Valued

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DYNAMICAL BEHAVIOR OF TRAINS EXCITED BY A NON-GAUSSIAN VECTOR VALUED RANDOM FIELD

Proceedings of the 4th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering – COMPDYN 2013 ◽

10.7712/120113.4645.c1029 ◽

2014 ◽

Author(s):

G. Perrin ◽

C. Soize ◽

D. Duhamel ◽

C. Funfschilling

Keyword(s):

Random Field ◽

Dynamical Behavior ◽

Gaussian Vector ◽

Non Gaussian ◽

Vector Valued

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Copula-based multiple indicator kriging for non-Gaussian random fields

Spatial Statistics ◽

10.1016/j.spasta.2021.100524 ◽

2021 ◽

pp. 100524

Author(s):

Gaurav Agarwal ◽

Ying Sun ◽

Huixia J. Wang

Keyword(s):

Random Fields ◽

Gaussian Random Fields ◽

Indicator Kriging ◽

Multiple Indicator ◽

Non Gaussian

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Uncertainty Quantification of Random Fields Based on Spatially Sparse Data by Synthesizing Bayesian Compressive Sensing and Stochastic Harmonic Function

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2020.107377 ◽

2021 ◽

Vol 153 ◽

pp. 107377

Author(s):

Jingran He ◽

Jianbing Chen ◽

Xiaodan Ren ◽

Jie Li

Keyword(s):

Harmonic Function ◽

Compressive Sensing ◽

Uncertainty Quantification ◽

Random Fields ◽

Sparse Data

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NONPARAMETRIC APPROACH FOR NON-GAUSSIAN VECTOR STATIONARY PROCESSES

Time Series, Fuzzy Analysis and Miscellaneous Topics ◽

10.1515/9783110917833.375 ◽

2012 ◽

Author(s):

MASANOBU TANIGUCHI ◽

MADAN L. PURI ◽

MASAO KONDO

Keyword(s):

Stationary Processes ◽

Gaussian Vector ◽

Nonparametric Approach ◽

Non Gaussian

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Some Non-Gaussian Random Fields

An Innovation Approach to Random Fields ◽

10.1142/9789812565389_0006 ◽

2004 ◽

pp. 85-95

Keyword(s):

Random Fields ◽

Gaussian Random Fields ◽

Non Gaussian

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A biorthogonal decomposition for the identification and simulation of non-stationary and non-Gaussian random fields

Journal of Computational Physics ◽

10.1016/j.jcp.2016.02.067 ◽

2016 ◽

Vol 314 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

I. Zentner ◽

G. Ferré ◽

F. Poirion ◽

M. Benoit

Keyword(s):

Random Fields ◽

Gaussian Random Fields ◽

Biorthogonal Decomposition ◽

Non Gaussian

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Stochastic representation and dimension reduction for non-Gaussian random fields: review and reflection

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-013-0700-7 ◽

2013 ◽

Vol 27 (7) ◽

pp. 1621-1635 ◽

Cited By ~ 12

Author(s):

Heng Li ◽

Dongxiao Zhang

Keyword(s):

Dimension Reduction ◽

Random Fields ◽

Gaussian Random Fields ◽

Stochastic Representation ◽

Non Gaussian

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High-Dimensional Uncertainty Quantification of Electronic and Photonic IC With Non-Gaussian Correlated Process Variations

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2019.2925340 ◽

2020 ◽

Vol 39 (8) ◽

pp. 1649-1661 ◽

Cited By ~ 5

Author(s):

Chunfeng Cui ◽

Zheng Zhang

Keyword(s):

Uncertainty Quantification ◽

Process Variations ◽

High Dimensional ◽

Non Gaussian

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Reliability Assessment on Thickness Effect in Fatigue Crack Growth

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.297-300.1913 ◽

2005 ◽

Vol 297-300 ◽

pp. 1913-1918

Author(s):

Seon Jin Kim ◽

Yu Sik Kong ◽

Sang Woo Kwon

Keyword(s):

Fatigue Crack ◽

Fatigue Crack Growth ◽

Crack Growth ◽

Random Fields ◽

Crack Surface ◽

Simulation Method ◽

Thickness Effect ◽

Specimen Thickness ◽

Fatigue Crack Growth Life ◽

Non Gaussian

The evaluation of specimen thickness effect of fatigue crack growth life by the simulation of probabilistic fatigue crack growth is presented. In this paper, the material resistance to fatigue crack growth is treated as a spatial stochastic process, which varies randomly on the crack surface. Using the previous experimental data, the non-Gaussian (eventually Weibull, in this report) random fields simulation method is applied. This method is useful to estimate the probability distribution of fatigue crack growth life and the variability due to specimen thickness by simulating material resistance to fatigue crack growth along a crack path.

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