Journal ArticleDOI
A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic process by a translation process
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TLDR
A new iterative methodology is developed that estimates a non-Gaussian PSDF that is compatible with the prescribed non- Gaussian PDF, and closely approximates the prescribed incompatible non- Gaia PSDF.About:
This article is published in Probabilistic Engineering Mechanics.The article was published on 2011-10-01. It has received 125 citations till now. The article focuses on the topics: Gaussian random field & Gaussian function.read more
Citations
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Reliability analysis with scarce information: Comparing alternative approaches in a geotechnical engineering context
TL;DR: In this article, the problem of dealing with scarce information in a reliability analysis is investigated in a geotechnical engineering context, and the potential of imprecise probabilities is discussed as an option for combining probabilistic and non-probabilistic information.
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Towards efficient uncertainty quantification in complex and large-scale biomechanical problems based on a Bayesian multi-fidelity scheme
TL;DR: An uncertainty quantification framework based on multi-fidelity sampling and Bayesian formulations is presented, providing first estimates on the variability of these mechanical quantities due to an uncertain constitutive parameter, and revealing the potential error made by assuming population averaged mean values in patient-specific simulations of abdominal aortic aneurysms.
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Modeling strongly non-Gaussian non-stationary stochastic processes using the Iterative Translation Approximation Method and Karhunen-Loève expansion
Hwanpyo Kim,Michael D. Shields +1 more
TL;DR: A new model for non-stationary and non-Gaussian stochastic processes is presented that improves the ITAM by upgrading directly the autocorrelation function and improves the accuracy of the resulting process while maintaining computational efficiency.
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An explicit method for simulating non-Gaussian and non-stationary stochastic processes by Karhunen-Loève and polynomial chaos expansion
TL;DR: A new method is developed for explicitly representing and synthesizing non-Gaussian and non-stationary stochastic processes that have been specified by their covariance function and marginal cumulative distribution function, where the covariance of the resulting process automatically matches the target covariance.
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A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic vector process by a translation process with applications in wind velocity simulation
TL;DR: A novel iterative methodology is presented that simply and efficiently estimates a non-Gaussian CSDM that is compatible with the prescribed non- Gaussian PDFs and closely approximates the prescribed incompatibleNon-GaRussian CSDM.
References
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Book
RANDOM DATA Analysis and Measurement Procedures
TL;DR: A revised and expanded edition of this classic reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems, is presented in this article.
Journal ArticleDOI
Random Data Analysis and Measurement Procedures
TL;DR: Krystek as discussed by the authors provides a comprehensive and self-contained overview of random data analysis, including derivations of the key relationships in probability and random-process theory not usually found to such extent in a book of this kind.
Journal ArticleDOI
Digital simulation of random processes and its applications
Masanobu Shinozuka,C.-M. Jan +1 more
TL;DR: In this article, the authors presented an efficient method for digital simulation of general homogeneous processes as a series of cosine functions with weighted amplitudes, almost evenly spaced frequencies, and random phase angles.
Journal ArticleDOI
Simulation of Stochastic Processes by Spectral Representation
Book
Stochastic Calculus: Applications in Science and Engineering
TL;DR: This chapter discusses Stochastic Processes, a type of probability theory, and Monte Carlo Simulation, which addresses the problem of uncertainty in deterministic systems.