Tensor neural network approximation reduces high-dimensional nonlocal diffusion integrals to low-dimensional ones via separability, with L2 error estimates for Dirichlet and Neumann conditions and tests up to dimension 20.
Enforce the Dirichlet boundary condition by volume constraint in Point Integral method
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abstract
Recently, Shi and Sun proposed Point Integral method (PIM) to discretize Laplace-Beltrami operator on point cloud. In PIM, Neumann boundary is nature, but Dirichlet boundary needs some special treatment. In our previous work, we use Robin boundary to approximate Dirichlet boundary. In this paper, we introduce another approach to deal with the Dirichlet boundary condition in point integral method using the volume constraint proposed by Du et.al.
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math.NA 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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ND-TNN: Tensor-Neural-Network Approximation for High-Dimensional Nonlocal Diffusion Models
Tensor neural network approximation reduces high-dimensional nonlocal diffusion integrals to low-dimensional ones via separability, with L2 error estimates for Dirichlet and Neumann conditions and tests up to dimension 20.