A stabilized radial basis-finite difference (RBF-FD) method with hybrid kernels
For researchers in meshless numerical methods, this work offers a more stable and efficient RBF-FD variant, though it is an incremental improvement over existing RBF-FD stabilization techniques.
The paper proposes a stabilized RBF-FD method using a hybrid Gaussian-cubic kernel that improves matrix conditioning, enabling direct solvers and reducing computational cost. The method yields stable eigenvalue spectra regardless of stencil size or irregularity, and is applied to solve the 2D frequency-domain acoustic wave equation with absorbing boundary conditions.
Recent developments have made it possible to overcome grid-based limitations of finite difference (FD) methods by adopting the kernel-based meshless framework using radial basis functions (RBFs). Such an approach provides a meshless implementation and is referred to as the radial basis-generated finite difference (RBF-FD) method. In this paper, we propose a stabilized RBF-FD approach with a hybrid kernel, generated through a hybridization of the Gaussian and cubic RBF. This hybrid kernel was found to improve the condition of the system matrix, consequently, the linear system can be solved with direct solvers which leads to a significant reduction in the computational cost as compared to standard RBF-FD methods coupled with present stable algorithms. Unlike other RBF-FD approaches, the eigenvalue spectra of differentiation matrices were found to be stable irrespective of irregularity, and the size of the stencils. As an application, we solve the frequency-domain acoustic wave equation in a 2D half-space. In order to suppress spurious reflections from truncated computational boundaries, absorbing boundary conditions have been effectively implemented.