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We study the initial-boundary value problem for a nonlinear wave equation given by [...] where p > 2, q > l, K, lambda are given constants and uo, u1, F are given functions, the unknown function u(x,t) and the unknown boundary value P (t) satisfy the following nonlinear integral equation [...] where K1, alpha, beta are given constants and g, k arę given functions. In Part 1 we prove a theorem of existence and uniqueness of a weak solution (u, P) of problem (1), (2). The proof is based on the Faedo-Galerkin method associated with a priori estimates, weak convergence and compactness techniques. In Part 3 we obtain an asymptotic expansion of the solution (u, P) of the problem (1), (2) up to order N+1 in three small parameters K, lambda, K1.
Przedstawiono metodę teoretyczną weryfikacji modeli mikromieszania i zastosowano do zbadania znanych z literatury przedmiotu modeli mikromieszania. Metoda polega na wykorzystaniu rozkładu asymptotycznego fluktuacji stężenia inertnego trasera, który przyjmuje postać rozkładu normalnego.
A method for theoretical verification of micromixing models is presented and applied to micromixing models known from the subject literature. Presented approach is based on the assumption that the PDF of a passive, conserved scalar relaxes asymptotically to the Gaussian form.
Content available remote Asymptotic distribution of the mutual variogram estimate
The article deals with the problem of a statistical analysis of time series connected with the estimation of mutual variogram, a measure of spatial correlation. G. Matheron [1] has coined the term variogram, although earlier appearances of this function can be found in the scientific literature [2, 3]. The problem of estimating the mutual variogram and examination the statistical properties of this statistics has been considered in [1, 4, 5]. We present the limiting expressions of the first two moments and the higher order cumulants of the mutual variogram estimate of the second-order-stationary stochastic process with discrete time. These expressions are then used to prove the theorem concerning the asymptotic distribution of the mutual variogram estimate. The approach is similar to the approach taken in the time series literature, and the reader is referred to D. Brillinger [6] for theorems regarding the asymptotic distribution of the spectral density estimate of a time series.
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