Meshfree Methods on Manifolds for Hydrodynamic Flows on Curved Surfaces: A Generalized Moving Least-Squares (GMLS) Approach
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Abstract (arXiv)
We utilize generalized moving least squares (GMLS) to develop meshfree techniques for discretizing hydrodynamic flow problems on manifolds. We use exterior calculus to formulate incompressible hydrodynamic equations in the Stokesian regime and handle the divergence-free constraints via a generalized vector potential. This provides less coordinate-centric descriptions and enables the development of efficient numerical methods and splitting schemes for the fourth-order governing equations in terms of a system of second-order elliptic operators. Using a Hodge decomposition, we develop methods for manifolds having spherical topology. We show the methods exhibit high-order convergence rates for solving hydrodynamic flows on curved surfaces. The methods also provide general high-order approximations for the metric, curvature, and other geometric quantities of the manifold and associated exterior calculus operators. The approaches also can be utilized to develop high-order solvers for other scalar-valued and vector-valued problems on manifolds.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2019.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 2.
7 papers.
- 2022GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductionscited by 7
- 2023SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamicscited by 4
- 2023Linear Regression on Manifold Structured Data: the Impact of Extrinsic Geometry on Solutionscited by 3
- 2024Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operatorscited by 2
- 2025Transferable Foundation Models for Geometric Tasks on Point Cloud Representations: Geometric Neural Operatorscited by 1
- 2026Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networkscited by 1
- 2026Extending Neural Operators: Robust Handling of Functions Beyond the Training Setcited by 0
- 2026Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks25 Mar 2026
- 2026Extending Neural Operators: Robust Handling of Functions Beyond the Training Set4 Mar 2026
- 2025Transferable Foundation Models for Geometric Tasks on Point Cloud Representations: Geometric Neural Operators6 Mar 2025
- 2024Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators16 Apr 2024
- 2023Linear Regression on Manifold Structured Data: the Impact of Extrinsic Geometry on Solutions5 Jul 2023
- 2023SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics7 Feb 2023
- 2022GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions10 Jun 2022