惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Blog — PlanetScale
Blog — PlanetScale
B
Blog
A
About on SuperTechFans
大猫的无限游戏
大猫的无限游戏
爱范儿
爱范儿
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
H
Help Net Security
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园 - 三生石上(FineUI控件)
有赞技术团队
有赞技术团队
酷 壳 – CoolShell
酷 壳 – CoolShell
WordPress大学
WordPress大学
IT之家
IT之家
D
Docker
Google DeepMind News
Google DeepMind News
罗磊的独立博客
T
The Blog of Author Tim Ferriss
aimingoo的专栏
aimingoo的专栏
博客园 - 叶小钗
Recent Announcements
Recent Announcements
阮一峰的网络日志
阮一峰的网络日志
D
DataBreaches.Net
博客园 - 司徒正美
Engineering at Meta
Engineering at Meta

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Fractional and Integer Order Sobolev Spaces for Compact M...
Elsiddig Awadelkarim, David Bolin, Alexandre B. Simas · 2025-12-15 · via math.PR updates on arXiv.org

Given a compact metric graph $Γ$ and the Laplacian $Δ_Γ$ coupled with standard (Kirchhoff) vertex conditions, solutions to fractional elliptic partial differential equations of the form $(κ^2 - Δ_Γ)^{α/2}u=f$ on $Γ$ exhibit a distinctive regularity structure: even-order derivatives are continuous across vertices, while odd-order derivatives may be discontinuous. This non-standard smoothness property precludes the direct application of classical tools from real functional analysis. Because of this, we introduce and systematically study new families of Sobolev spaces tailored to this setting. We define these spaces, denoted $W^{α,p}(Γ)$ and $H^α(Γ)$, to respect the continuity constraints on even-order derivatives at vertices, while permitting discontinuities in odd-order derivatives. We establish their fundamental properties, including characterizations, embedding theorems into Hölder and Lebesgue spaces, and compactness results. A central contribution in this investigation is the derivation of uniform bounds on the supremum norm of eigenfunctions for a class of Laplacians on metric graphs, a result of independent interest. Finally, we demonstrate that these spaces provide a natural framework for analyzing the regularity of solutions to fractional elliptic PDEs and SPDEs driven by Gaussian white noise on metric graphs, in particular, establishing a general characterization of the domain of the fractional powers of $(κ^2-Δ_Γ)$ and $(κ^2-\nabla(a\nabla))$ in terms of the Sobolev spaces we introduce, thereby extending all previously known characterizations in the literature, and improving the regularity results previously obtained to their sharp counterparts (with general fractional powers). We also show that these spaces are fundamental to the characterization of Gaussian free fields on metric graphs.