Stochastic processes

Polynomial chaos

Polynomial chaos (PC), also called polynomial chaos expansion (PCE) and Wiener chaos expansion, is a method for representing a random variable in terms of a polynomial function of other random variables. The polynomials are chosen to be orthogonal with respect to the joint probability distribution of these random variables. PCE can be used, e.g., to determine the evolution of uncertainty in a dynamical system when there is probabilistic uncertainty in the system parameters. Note that despite its name, PCE has no immediate connections to chaos theory. PCE was first introduced in 1938 by Norbert Wiener using Hermite polynomials to model stochastic processes with Gaussian random variables. It was introduced to the physics and engineering community by R. Ghanem and P. D. Spanos in 1991 and generalized to other orthogonal polynomial families by D. Xiu and G. E. Karniadakis in 2002. Mathematically rigorous proofs of existence and convergence of generalized PCE were given by O. G. Ernst and coworkers in 2012. PCE has found widespread use in engineering and the applied sciences because it makes it possible to efficiently deal with probabilistic uncertainty in the parameters of a system. It is widely used in stochastic finite element analysis and as a surrogate model to facilitate uncertainty quantification analyses. (Wikipedia).

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Chaos9 Research today

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From playlist Chaos English

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Credit roll for Chaos

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From playlist Chaos 日本語

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Generiek Chaos

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From playlist Chaos nederlands

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Générique Chaos

Générique de Chaos www.chaos-math.org

From playlist Chaos français

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Chaos6 Chaos en het hoefijzer

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From playlist Chaos nederlands

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Chaotic Dynamical Systems

This video introduces chaotic dynamical systems, which exhibit sensitive dependence on initial conditions. These systems are ubiquitous in natural and engineering systems, from turbulent fluids to the motion of objects in the solar system. Here, we discuss how to recognize chaos and how

From playlist Engineering Math: Differential Equations and Dynamical Systems

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Chaos9 A pesquisa, hoje

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From playlist Chaos Português

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Nicolas Perkowski: Lecture #2

This is the second lecture on "A Markovian perspective on some singular SPDEs" taught by Professor Nicolas Perkowski. For more materials and slides visit: https://sites.google.com/view/oneworld-pderandom/home

From playlist Summer School on PDE & Randomness

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Spin Glass Phase at Zero Temperature in the Edwards--Anderson Model by Sourav Chatterjee

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From playlist TOPICS IN HIGH DIMENSIONAL PROBABILITY

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Sabine Jansen - Duality, intertwining and orthogonal polynomials for continuum...

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From playlist Large-scale limits of interacting particle systems

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CERIAS Security: Chaos,Complexity, Cybernetics and Therminator 2/6

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From playlist The CERIAS Security Seminars 2006

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Eric Perlmutter - Harnessing SL(2, Z) in Super Yang–Mills and Gravity

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From playlist 10e séminaire ITZYKSON – Valeurs zêta multiples et fonctions modulaires de graphes en théorie des cordes

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Corinna Ulcigrai - 1/6 Parabolic dynamics and renormalization: an introduction

Parabolic dynamical systems are mathematical models of the many phenomena which display a "slow" form of chaotic evolution, in the sense that nearby trajectories diverge polynomially in time. In contrast with hyperbolic and elliptic dynamical systems, there is no general theory which desc

From playlist Corinna Ulcigrai - Parabolic dynamics and renormalization: an introduction

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Constraints on classical Gravitational Scattering amplitudes (Lecture 3) by Shiraz Minwalla

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From playlist Recent Developments in S-matrix Theory (Online)

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From playlist Chaos English

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MAE5790-19 One dimensional maps

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From playlist Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University

Related pages

Uncertainty quantification | Dynamical system | Variance-based sensitivity analysis | Functional (mathematics) | Prior probability | Surrogate model | Stationary increments | Fractional Brownian motion | Student's t-distribution | Hermite polynomials | Sensitivity analysis | Uncertainty | Gamma function | Chaos theory | Probability distribution | Stochastic | Kriging | Stochastic partial differential equation | Basis (linear algebra) | Random variable | Hilbert space | Finite element method | Proper orthogonal decomposition | Orthogonal polynomials | Gaussian process | Bayesian inference | Cameron–Martin theorem