R
Roger Ghanem
Researcher at University of Southern California
Publications - 265
Citations - 18922
Roger Ghanem is an academic researcher from University of Southern California. The author has contributed to research in topics: Polynomial chaos & Probabilistic logic. The author has an hindex of 58, co-authored 254 publications receiving 17427 citations. Previous affiliations of Roger Ghanem include Rice University & University at Buffalo.
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Stochastic Finite Elements: A Spectral Approach
Roger Ghanem,Pol D. Spanos +1 more
TL;DR: In this article, a representation of stochastic processes and response statistics are represented by finite element method and response representation, respectively, and numerical examples are provided for each of them.
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Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
Christian Soize,Roger Ghanem +1 more
TL;DR: This paper clarifies the mathematical structure of this measure space and its relationship to the underlying spaces associated with each of the basic random variables.
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Stochastic Finite Element Expansion for Random Media
Pol D. Spanos,Roger Ghanem +1 more
TL;DR: In this paper, a new method for the solution of problems involving material variability is proposed, which makes use of the Karhunen-Loeve expansion to represent the random material property.
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Uncertainty propagation using Wiener-Haar expansions
TL;DR: In this article, an uncertainty quantification scheme based on generalized polynomial chaos (PC) representations is constructed, which is applied to a model problem involving a simplified dynamical system and to the classical problem of Rayleigh-Benard instability.
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Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes
Bert Debusschere,Habib N. Najm,Philippe Pierre Pebay,Omar M. Knio,Roger Ghanem,Olivier Le Maitre +5 more
TL;DR: This paper gives an overview of the use of polynomial chaos (PC) expansions to represent stochastic processes in numerical simulations and finds that the integration method offers a robust and accurate approach for evaluating nonpolynomial functions, even when very high-order information is present in the PC expansions.