Leevan Ling

We present a systematic investigation of atlas-based radial basis function finite difference (RBFFD) methods for solving elliptic partial differential equations on smooth, compact surfaces represented as point clouds. This approach organizes point cloud data according to coordinate charts and computes differentiation weights collectively within each chart, contrasting with traditional RBFFD methods that construct independent stencils at each point. While the theoretical convergence framework has been established, practical implementation strategies and convergence behavior under different refinement regimes remain unexplored. We address these gaps by formalizing implementation strategies for both intrinsic and extrinsic formulations, including practical algorithms for atlas construction and local parameterization from point cloud data. We conduct convergence studies under two refinement scenarios: simultaneous refinement validates theoretical convergence rates, while the fixed-atlas regime reveals accuracy saturation due to geometric discretization error. We compare different local parameterization approaches and investigate weighted variants to improve method performance. Numerical experiments on various complex surfaces demonstrate that atlas-based RBFFD methods accurately solve elliptic problems without requiring explicit surface parameterizations or mesh connectivity.

We introduce a Trajectory-Based RBF Collocation (TBRBF) method for solving surface advection-diffusion equations on smooth, compact manifolds. TBRBF decouples advection and diffusion by applying a characteristic treatment with a Kansa-type RBF collocation method for diffusion PDE, which yields an operator-split characteristic (OSC) system comprising a characteristic ODE and a diffusion PDE. We rigorously prove the equivalence between the OSC system and the original surface PDE on manifolds by embedding the latter into a narrow band domain. Using an intrinsic approach, we construct a time-continuous embedded PDE with push-forward operators in each chart of the atlas and establish its equivalence with the OSC system in the narrow band. Restricting the solution back to the manifold recovers the OSC system on manifolds, ensuring that the method introduces no operator splitting error. Extensive numerical experiments confirm the robust stability and accuracy of the proposed method.

We extend sharp direct and inverse approximation statements for kernel-based methods for finitely smooth kernels, i.e. those whose native spaces are norm-equivalent to Sobolev spaces. In particular, our inverse results are now formulated for a broad class of approximation schemes beyond interpolation, extending existing theory. Building on these results, we propose a novel Sobolev Algorithm for Local Smoothness Analysis (SALSA) for detecting local smoothness properties of target data, including their degree of smoothness and non-smoothness. The method is rigorously grounded based on the sharp direct and inverse statements. Numerical experiments in various settings highlight the effectiveness of the proposed algorithm.

This paper explores an efficient Lagrangian approach for evolving point cloud data on smooth manifolds. In this preliminary study, we focus on analyzing plane curves, and our ultimate goal is to provide an alternative to the conventional radial basis function (RBF) approach for manifolds in higher dimensions. In particular, we use the B-Spline as the basis function for all local interpolations. Just like RBF and other smooth basis functions, B-Splines enable the approximation of geometric features such as normal vectors and curvature. Once properly set up, the advantage of using B-Splines is that their coefficients carry geometric meanings. This allows the coefficients to be manipulated like points, facilitates rapid updates of the interpolant, and eliminates the need for frequent re-interpolation. Consequently, the removal and insertion of point cloud data become seamless processes, particularly advantageous in regions experiencing significant fluctuations in point density. The numerical results demonstrate the convergence of geometric quantities and the effectiveness of our approach. Finally, we show simulations of curvature flows whose speeds depend on the solutions of coupled reaction--diffusion systems for pattern formation.