Frontend
Two nodally bound-preserving and mass-conservative discontinuous Galerkin methods for reaction-diffusion problems
arXiv cs.NA0 views
arXiv:2611.13588v1 Announce Type: new Abstract: We propose and analyze two high-order discontinuous Galerkin methods that preserve prescribed nodal bounds and total mass for reaction-diffusion problems with anisotropic and heterogeneous diffusion. Both methods combine a weighted interior-penalty prediction step with a convex correction in either the dG energy or the $L^2$ metric. We construct a pointwise bound-preserving and cellwise mass-preserving comparison function with optimal local approximation properties. This construction yields optimal dG-energy error estimates for the energy correction and, under a dual regularity assumption, optimal $L^2$ and dG-energy error estimates for the $L^2$ correction on quasi-uniform meshes. We further establish a general a posteriori estimate that combines a predictor estimator with the computable correction norm. The resulting estimators are globally reliable and efficient up to data oscillation, with parameter-dependent efficiency constants. Numerical experiments compare the two corrections and illustrate their use in adaptive computations.
Source:https://arxiv.org/abs/2611.13588PreviousMultiscale LegONet: Learning Coefficient-Dependent Local Spaces for Composable Multiscale Finite Elements with Localized Error BoundsNextLearning the Brownian Motion in Machine Learning for Parametric Parabolic PDEs: A Backward Quadrature Approach
Related posts
JSONDecodeError: Expecting value at char 0? Read the Error Position Before You Touch the JSON
0dev.to
Класс пожарной опасности здания как модель данных: почему один enum на здание — ошибка
0dev.to
CFAR Detection on Non-Coherently Integrated Angular Maps for mmWave Radio SLAM
0arXiv eess.SP
Reconciling Strict SLOs with Low Cost under Fine-Grained Load Fluctuations
0arXiv cs.DC
Unified Far-field and Near-field Codebook Design for XL-array Systems
0arXiv math.IT
Uncertainty-Aware Hybrid Offline Reinforcement Learning with Online Action Selection Enabled by Sparse Gaussian-Process Critics
0arXiv eess.SY