A Least Squares Radial Basis Function Partition of Unity Method for Solving PDEs
This work improves the numerical stability and efficiency of RBF-PUM for solving PDEs, addressing key bottlenecks in the existing collocation approach.
The authors propose a least squares radial basis function partition of unity method (RBF-PUM) for solving PDEs, which removes sensitivity to node layout and controls conditioning via oversampling. Numerical experiments show the least squares formulation is 5-10 times faster than collocation for the same accuracy.
Recently, collocation based radial basis function (RBF) partition of unity methods (PUM) for solving partial differential equations have been formulated and investigated numerically and theoretically. When combined with stable evaluation methods such as the RBF-QR method, high order convergence rates can be achieved and sustained under refinement. However, some numerical issues remain. The method is sensitive to the node layout, and condition numbers increase with the refinement level. Here, we propose a modified formulation based on least squares approximation. We show that the sensitivity to node layout is removed and that conditioning can be controlled through oversampling. We derive theoretical error estimates both for the collocation and least squares RBF-PUM. Numerical experiments are performed for the Poisson equation in two and three space dimensions for regular and irregular geometries. The convergence experiments confirm the theoretical estimates, and the least squares formulation is shown to be 5-10 times faster than the collocation formulation for the same accuracy.