Wavelet based Adaptive RBF Method for Nearly Singular Poisson-Type Problems on Irregular Domains

We present a wavelet based adaptive scheme and investigate the efficiency of this scheme for solving nearly singular potential PDEs over irregularly shaped domains. For a problem defined over Ω ∈ ℜd, the boundary of an irregularly shaped domain, Γ, is defined as a boundary curve that is a product of a Heaviside function along the normal direction and a piecewise continuous tangential curve. The link between the original wavelet based adaptive method presented in Libre, Emdadi, Kansa, Shekarchi, and Rahimian (2008, 2009) or LEKSR method and the generalized one is given through the use of simple Heaviside masking procedure. In addition level dependent thresholding were introduced to improve the efficiency and convergence rate of the solution. We will show how the generalized wavelet based adaptive method can be applied for detecting nearly singularities in Poisson type PDEs over irregular domains. The numerical examples have illustrated that the proposed method is powerful to analyze the Poisson type PDEs with rapid changes in gradients and nearly singularities

A Fast Adaptive Wavelet Scheme in RBF Collocation for Nearly Singular Potential PDEs

We present a wavelet based adaptive scheme and investigate the efficiency of this scheme for solving nearly singular potential PDEs. Multiresolution wavelet analysis (MRWA) provides a firm mathematical foundation by projecting the solution of PDE onto a nested sequence of approximation spaces. The wavelet coefficients then were used as an estimation of the sensible regions for node adaptation. The proposed adaptation scheme requires negligible calculation time due to the existence of the fast DiscreteWavelet Transform (DWT). Certain aspects of the proposed adaptive scheme are discussed through numerical examples. It has been shown that the proposed adaptive scheme can detect the singularities both in the domain and near the boundaries. Moreover, the proposed adaptive scheme can be utilized for capturing the regions with high gradient both in the solution and its spatial derivatives. Due to the simplicity of the proposed method, it can be efficiently applied to large scale nearly singular engineering problems

Stable PDE Solution Methods for Large Multiquadric Shape Parameters

We present a new method based upon the paper of Volokh and Vilney (2000) that produces highly accurate and stable solutions to very ill-conditioned multiquadric (MQ) radial basis function (RBF) asymmetric collocation methods for partial differential equations (PDEs). We demonstrate that the modified Volokh-Vilney algorithm that we name the improved truncated singular value decomposition (IT-SVD) produces highly accurate and stable numerical solutions for large values of a constant MQ shape parameter, c, that exceeds the critical value of c based upon Gaussian elimination

A Stabilized RBF Collocation Scheme for Neumann Type Boundary Value Problems

The numerical solution of partial differential equations (PDEs) with Neumann boundary conditions (BCs) resulted from strong form collocation scheme are typically much poorer in accuracy compared to those with pure Dirichlet BCs. In this paper, we show numerically that the reason of the reduced accuracy is that Neumann BC requires the approximation of the spatial derivatives at Neumann boundaries which are significantly less accurate than approximation of main function. Therefore, we utilize boundary treatment schemes that based upon increasing the accuracy of spatial derivatives at boundaries. Increased accuracy of the spatial derivative approximation can be achieved by h-refmement reducing the spacing between discretization points or by increasing the multiquadric shape parameter, c. Increasing the MQ shape parameter is very computationally cost effective, but leads to increased ill-conditioning. We have implemented an improved version of the truncated singular value decomposition (IT-SVD) originated by Volokh and Vilnay (2000) that projects very small singular values into the null space, producing a well conditioned system of equations. To assess the proposed refinement scheme, elliptic PDEs with different boundary conditions are analyzed. Comparisons that made with analytical solution reveal superior accuracy and computational efficiency of the IT-SVD solutions

International Conference Nuclear Energy for New Europe 2006 Solving One-Dimensional Phase Change Problems with Moving Grid Method and Mesh Free Radial Basis Functions

ABSTRACT Many heat-transfer problems involve a change of phase of material due to solidification or melting. Applications include: the safety studies of nuclear reactors (molten core concrete interaction), the drilling of high ice-content soil, the storage of thermal energy, etc. These problems are often called Stefan's or moving boundary value problems. Mathematically, the interface motion is expressed implicitly in an equation for the conservation of thermal energy at the interface (Stefan's conditions). This introduces a non-linear character to the system which treats each problem somewhat uniquely. The exact solution of phase change problems is limited exclusively to the cases in which e.g. the heat transfer regions are infinite or semiinfinite one dimensional-space. Therefore, solution is obtained either by approximate analytical solution or by numerical methods. Finite-difference methods and finite-element techniques have been used extensively for numerical solution of moving boundary problems. Recently, the numerical methods have focused on the idea of using a mesh-free methodology for the numerical solution of partial differential equations based on radial basis functions. In our case we will study solid-solid transformation. The numerical solutions will be compared with analytical solutions. Actually, in our work we will examine usefulness of radial basis functions (especially multiquadric-MQ) for one-dimensional Stefan's problems. The position of the moving boundary will be simulated by moving grid method. The resultant system of RBF-PDE will be solved by affine space decomposition

A Multiresolution Prewavelet-Based Adaptive Refinement Scheme for RBF Approximations of Nearly Singular Problems

A common difficulty in applying radial basis function (RBF) methods to nearly singular problems is either convergence failure or a very slow convergence rate. Motivated by the close connection between RBFs and wavelets, we have demonstrated the efficiency of adaptive distributions based on multiresolution wavelet decomposition for the RBF approximation of nearly singular problems. RBFs are prewavelets per se, RBFs are not orthonormalized. Wavelet decomposition provides a firm mathematical base for projecting a complicated function into a nested sequence of subspaces each of which has a different level of resolution. So, one can analyze the highly localized feature regions of a function at a high level of resolution and simultaneously use a low level of resolution for analyzing the function at flat regions. We also utilize the variable shape parameter scheme to prevent the growth of the well-known ill-conditioning problem in globally supported RBFs. The performance of the proposed method is illustrated in one- and two-dimensional numerical examples with internal or boundary near singularities. Our numerical results show that super convergent behavior can be achieved if the adaptive scheme is utilized to produce the compressed distribution