Pith. sign in

REVIEW 1 cited by

The Direct Radial Basis Function Partition of Unity (D-RBF-PU) Method for Solving PDEs

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2009.07175 v2 pith:QIKUKSQK submitted 2020-09-15 math.NA cs.NA

classification math.NAcs.NA
keywords methoddirectpartitionunitybasisd-rbf-puderivativesfaster
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this paper, a new localized radial basis function (RBF) method based on partition of unity (PU) is proposed for solving boundary and initial-boundary value problems. The new method is benefited from a direct discretization approach and is called the `direct RBF partition of unity (D-RBF-PU)' method. Thanks to avoiding all derivatives of PU weight functions as well as all lower derivatives of local approximants, the new method is faster and simpler than the standard RBF-PU method. Besides, the discontinuous PU weight functions can now be utilized to develop the method in a more efficient and less expensive way. Alternatively, the new method is an RBF-generated finite difference (RBF-FD) method in a PU setting which is much faster and in some situations more accurate than the original RBF-FD. The polyharmonic splines are used for local approximations, and the error and stability issues are considered. Some numerical experiments on irregular 2D and 3D domains, as well as cost comparison tests, are performed to support the theoretical analysis and to show the efficiency of the new method.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 110 citations worldwide. Full citation record

  1. PMODE: Theoretically Grounded and Modular Mixture Modeling

    cs.LG 2025-08 conditional novelty 6.0 of 10

    PMODE partitions data and fits per-subset density estimators, achieving near-optimal mixture rates for L1, L2, and KL objectives; its MV-PMODE variant scales to thousands of dimensions and rivals deep anomaly detectio...

Pith tools