Adjusted Sparse Tensor Product Spectral Galerkin Method for Solving Pseudodifferential Equations on the Sphere with Random Input Data

2019 ◽  
Vol 166 (1) ◽  
pp. 187-214
Author(s):  
Duong Thanh Pham ◽  
Dinh Dũng
Keyword(s):  
Tensor Product ◽  
Galerkin Method ◽  
Input Data ◽  
Random Input ◽  
Pseudodifferential Equations ◽  
Spectral Galerkin Method ◽  
Random Input Data

scholarly journals Spectral Stochastic Infinite Element Method in Vibroacoustics

2020 ◽  
Vol 28 (02) ◽  
pp. 2050009
Author(s):  
Felix Kronowetter ◽  
Lennart Moheit ◽  
Martin Eser ◽  
Kian K. Sepahvand ◽  
Steffen Marburg
Keyword(s):  
Input Data ◽  
Solution Technique ◽  
Element Formulation ◽  
Random Input ◽  
Infinite Element ◽  
Time Harmonic ◽  
Generalized Polynomial ◽  
Infinite Element Method ◽  
Random Input Data ◽  
Element Method

A novel method to solve exterior Helmholtz problems in the case of multipole excitation and random input data is developed. The infinite element method is applied to compute the sound pressure field in the exterior fluid domain. The consideration of random input data leads to a stochastic infinite element formulation. The generalized polynomial chaos expansion of the random data results in the spectral stochastic infinite element method. As a solution technique, the non-intrusive collocation method is chosen. The performance of the spectral stochastic infinite element method is demonstrated for a time-harmonic problem and an eigenfrequency study.

scholarly journals Computational Probabilistic Analysis and Transformation Method in the Earth Remote Research

2020 ◽  
Vol 223 ◽  
pp. 02001
Author(s):  
Boris Dobronets ◽  
Olga Popova ◽  
Alexey Merko
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Probabilistic Analysis ◽  
Input Data ◽  
Joint Probability ◽  
Transformation Method ◽  
Joint Probability Density Function ◽  
Random Input ◽  
Random Input Data ◽  
Joint Probability Density

The article deals with the issues of numerical modeling of problems with random input data. Finding the joint probability density function of the vector of output parameters is considered. It is proposed to use computational probabilistic analysis and the transformation method. A numerical example of the joint probability density function of the vector of a solution of a system of nonlinear equations with random input data is given.

Sign in / Sign up

Export Citation Format

Share Document