Search arXiv⌕ Search

arXiv · 2610.39642

Coefficient Recombination and Error Estimates for the L2 Method for Subdiffusion on Nonuniform Meshes

Abstract

The L2 discretization of the Caputo derivative is attractive for its high-order accuracy, but its analysis on nonuniform temporal meshes is complicated due to the lack of a monotone structure in the discrete coefficients. This difficulty is particularly significant for deriving sharp $L^2$ error estimates in the presence of the typical weak singularity of subdiffusion solutions near the initial time. In this work, we develop a coefficient-recombination framework for the L2 method on nonuniform meshes. Under the adjacent step-ratio condition $0.86\le ρ_k\le 1.16$, $k\ge2$, we prove that the recombined L2 coefficients are positive and monotone. A key ingredient is a cross-row recombination technique: with the recombination parameter associated with a fixed time level, all coefficient rows retain positivity and monotonicity, which allows us to close an induction for the $L^2$ error analysis. For solutions exhibiting the standard initial weak singularity, we derive an $L^2$ error estimate on nonuniform meshes satisfying the step-ratio condition. On a modified graded mesh with grading parameter $r>1$, the estimate yields the convergence rate $\mathcal{O}\!\left(N^{-\min\{rα,\,3-α\}}\right)$, and hence recovers the optimal order $\mathcal{O}(N^{-(3-α)})$ whenever $r\ge(3-α)/α$. Numerical experiments on random nonmonotone meshes and modified graded meshes confirm the theoretical estimates.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chaoyu Quan, Shangyu Wang, Shijie Wang, Jiang Yang. 2026-10-29. Coefficient Recombination and Error Estimates for the L2 Method for Subdiffusion on Nonuniform Meshes. https://arxiv.org/abs/2610.39642

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A continuous scale space of diffeomorphisms

In this paper, we define and study a nested family of reproducing kernel Hilbert spaces of vector fields that is indexed by a range of scales, from which we construct a reproducing kernel Hilbert space of scale-dependent vector fields. We provide a characterization of the reproducing kernel of that space, with numerical approximations ensuring quick evaluations when this kernel does not have a closed form. We then introduce a multiscale version of the large deformation diffeomorphic metric mapping (LDDMM) problem and prove the existence of solutions. Finally, we provide numerical experiments performing landmark matching using multiscale LDDMM.

math.NA↗

Hierarchical Matrices with Tucker Blocks: Construction and Forward Evaluation

In this paper, we introduce hierarchical matrices with Tucker blocks (HMTB) for data-sparse approximation of matrices arising from nonoscillatory and mildly oscillatory kernels. Building on the hierarchical matrix framework, HMTB matrices replace conventional low-rank blocks with Tucker representations. Using high-dimensional interpolation and tensor compression and contractions, we develop algorithms for constructing HMTB matrices and performing matrix-vector multiplication. For an $N \times N$ matrix, these algorithms have linear and quasi-linear complexities in $N$, respectively. Numerical comparisons with an interpolation-based $\mathcal{H}$-matrix implementation demonstrate storage savings and, in several cases, reduced construction times, while maintaining competitive forward-evaluation costs. The framework is also applicable to quasi-uniform grids.

math.NA↗

Data-driven model order reduction for wave propagation in materials with damage and nonlinearities

In this work, we consider wave propagation in materials characterized by nonlinear properties or damage. To accelerate the simulations of the resulting high-dimensional problems, we apply model order reduction methods. Depending on the knowledge of the underlying equations and the availability of their discrete operators, intrusive methods (here projection-based approaches based on proper orthogonal decomposition (POD)) or non-instrusive methods (here data-driven approaches including dynamic mode decomposition (DMD) and operator inference (OpInf)) can be used. We recall the theoretical foundations of the methods and apply them to the problem of wave propagation. In three different numerical examples, we evaluate the performance of the reduction techniques.

math.NA↗