Journal ArticleDOI
Digital Generation of Non‐Gaussian Stochastic Fields
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TLDR
In this paper, a method by which sample fields of a multidimensional non-Gaussian homogeneous stochastic field can be generated is developed, where the method first generates Gaussian sample fields and then maps them into non -Gaussian samples with the aid of an iterative procedure.Abstract:
A method by which sample fields of a multidimensional non‐Gaussian homogeneous stochastic field can be generated is developed. The method first generates Gaussian sample fields and then maps them into non‐Gaussian sample fields with the aid of an iterative procedure. Numerical examples indicate that the procedure is very efficient and generated sample fields satisfy the target spectral density and probability distribution function accurately. The proposed method has a wide range of applicability to engineering problems involving stochastic fields where the Gaussian assumption is not appropriate.read more
Citations
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Journal ArticleDOI
High-Order Collocation Methods for Differential Equations with Random Inputs
Dongbin Xiu,Jan S. Hesthaven +1 more
TL;DR: A high-order stochastic collocation approach is proposed, which takes advantage of an assumption of smoothness of the solution in random space to achieve fast convergence and requires only repetitive runs of an existing deterministic solver, similar to Monte Carlo methods.
Journal ArticleDOI
The stochastic finite element method: Past, present and future
TL;DR: A state-of-the-art review of past and recent developments in the SFEM area and indicating future directions as well as some open issues to be examined by the computational mechanics community in the future are provided.
Journal Article
Fast numerical methods for stochastic computations: A review
TL;DR: This paper presents a review of the current state-of-the-art of numerical methods for stochastic computations, with a particular emphasis on those based on generalized polynomial chaos (gPC) methodology.
Journal ArticleDOI
Simulation of Ergodic Multivariate Stochastic Processes
TL;DR: In this paper, a simulation algorithm is proposed to generate ergodic sample functions of a stationary, multivariate stochastic process according to its prescribed cross-spectral density matrix.
Journal ArticleDOI
Non-stationary stochastic vector processes: seismic ground motion applications
TL;DR: A spectral-representation-based simulation algorithm is used in this paper to generate sample functions of a non-stationary, multi-variate stochastic process with evolutionary power, according to its prescribed non- stationary cross-spectral density matrix.
References
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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
Crossings of non-gaussian translation processes
TL;DR: It is shown that translation processes can have any marginal distribution and autocorrelation function and that approximations proposed previously for the mean upcrossing rate of non‐Gaussian processes can be unsatisfactory.
Book ChapterDOI
Stochastic Fields and their Digital Simulation
TL;DR: A special case of Eq. 3.1 emerges as a one-dimensional and uni-variate stochastic field f(x) if both n and m are set equal to unity as discussed by the authors.
Journal ArticleDOI
Extremes of Wave Forces
TL;DR: In this paper, the authors developed probabilistic descriptors for Morison-type wave forces based on the actual distribution of these forces and on the hypothesis that wave forces follow Gaussian distributions.