arXiv subjects

Daniel R. Venn

Publications and source records attributed to Daniel R. Venn.

3 recordsLinked to original sources

arXiv2026

High-Order Meshfree Surface Integration, Including Singular Integrands

We develop and test high-order methods for integration on surface point clouds. The task of integrating a function on a surface arises in a range of applications in engineering and the sciences, particularly those involving various integral methods for partial differential equations. Mesh-based methods require a curved mesh for high-order convergence, which can be difficult to reliably obtain on many surfaces, and most meshfree methods require the ability to integrate a set of functions (such as radial basis functions) exactly on the domain of interest; these integrals are generally not known in closed form on most surfaces. We describe two methods for integrating on arbitrary, piecewise-smooth surfaces with or without boundary. Our approaches do not require a particular arrangement of points or an initial triangulation of the surface, making them completely meshfree. We also show how the methods can be extended to handle singular integrals while maintaining high accuracy without changing the point density near singularities.

math.NA↗

arXiv2024

A Meshfree Method for Eigenvalues of Differential Operators on Surfaces, Including Steklov Problems

We present and study techniques for investigating the spectra of linear differential operators on surfaces and flat domains using symmetric meshfree methods: meshfree methods that arise from finding norm-minimizing Hermite-Birkhoff interpolants in a Hilbert space. Meshfree methods are desirable for surface problems due to the increased difficulties associated with mesh creation and refinement on curved surfaces. While meshfree methods have been used for solving a wide range of partial differential equations (PDEs) in recent years, the spectra of operators discretized using radial basis functions (RBFs) often suffer from the presence of non-physical eigenvalues (spurious modes). This makes many RBF methods unhelpful for eigenvalue problems. We provide rigorously justified processes for finding eigenvalues based on results concerning the norm of the solution in its native space; specifically, only PDEs with solutions in the native space produce numerical solutions with bounded norms as the fill distance approaches zero. For certain problems, we prove that eigenvalue and eigenfunction estimates converge at a high-order rate. The technique we present is general enough to work for a wide variety of problems, including Steklov problems, where the eigenvalue parameter is in the boundary condition. Numerical experiments for a mix of standard and Steklov eigenproblems on surfaces with and without boundary, as well as flat domains, are presented, including a Steklov-Helmholtz problem.

math.NA↗

arXiv2024

Underdetermined Fourier Extensions for Surface Partial Differential Equations

We analyze and test using Fourier extensions that minimize a Hilbert space norm for the purpose of solving partial differential equations (PDEs) on surfaces. In particular, we prove that the approach is arbitrarily high-order and also show a general result relating boundedness, solvability, and convergence that can be used to find eigenvalues. The method works by extending a solution to a surface PDE into a box-shaped domain so that the differential operators of the extended function agree with the surface differential operators, as in the Closest Point Method. This differs from approaches that require a basis for the surface of interest, which may not be available. Numerical experiments are also provided, demonstrating super-algebraic convergence. Current high-order methods for surface PDEs are often limited to a small class of surfaces or use radial basis functions (RBFs). Our approach offers certain advantages related to conditioning, generality, and ease of implementation. The method is meshfree and works on arbitrary surfaces (closed or non-closed) defined by point clouds with minimal conditions.

math.NA↗