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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

Trend · papers per month

count% of corpus
0111 · Mar 201719922001200920172026
4 results for “superconvergence”
Superconvergence of differential structure for finite element methods on perturbed surface meshesmath.NA

Paper studies superconvergence on surface meshes using gradient recovery.

problem Proving superconvergence on deviated surfaces.
method Introduces geometric supercloseness and an algorithmic framework for gradient recovery.
result Validates theoretical results with numerical examples.
superconvergencedifferential structurefinite element methodssurface meshesgradient recovery
2019-10-30abs ↗pdf ↗
Parametric Polynomial Preserving Recovery on Manifoldsmath.NA

This paper investigates gradient recovery schemes for data defined on discretized manifolds. The proposed method, parametric polynomial preserving recovery (PPPR), does not require the tangent spaces of the exact manifolds, and they have been assumed for some significant gradient recovery methods in the literature. Ano…

2017-03-19abs ↗pdf ↗
On the improved convergence of lifted distributional Gauss curvature from Regge elementsmath.NA

The paper improves convergence rates of curvature approximations using Regge elements.

problem Improving convergence rates of curvature approximations using Regge elements.
method Investigates the interplay between polynomial degree of curvature lifting and metric tensor degree in Regge finite element space.
result Higher convergence rates are achieved by reducing the polynomial degree of curvature lifting and using linear Regge elements.
Regge elementscurvature approximationconvergence ratesfinite element methodssuperconvergence
2024-01-23abs ↗pdf ↗
Signal reconstruction using determinantal samplingstat.ML

This paper improves signal reconstruction using determinantal sampling from random nodes.

problem Approximating square-integrable functions from random node evaluations.
method Combines determinantal point processes and mixtures thereof for RKHS-adapted approximations.
result Proves mean-square guarantees in L2L^2 norm and shows faster convergence rates.
signal reconstructiondeterminantal samplingRKHSpoint processessuperconvergence
2023-10-13abs ↗pdf ↗