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

D
Docker
阮一峰的网络日志
阮一峰的网络日志
T
Tailwind CSS Blog
博客园 - 【当耐特】
量子位
博客园 - 叶小钗
有赞技术团队
有赞技术团队
Jina AI
Jina AI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - Franky
博客园 - 司徒正美
爱范儿
爱范儿
美团技术团队
小众软件
小众软件
酷 壳 – CoolShell
酷 壳 – CoolShell
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
V
V2EX
罗磊的独立博客
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Last Week in AI
Last Week in AI
Hugging Face - Blog
Hugging Face - Blog
I
InfoQ
D
DataBreaches.Net
宝玉的分享
宝玉的分享

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
The W-Operator: A Volterra Fractional Time Operator with ...
[Submitted on 6 Jan 2026 (v1), last revised 2 Jun 2026 (this ver · 2026-06-03 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:We introduce a new two-parameter fractional time operator with Volterra structure, denoted by ${}^{W}D_{t}^{\alpha,\beta}$, defined through the Laplace symbol \[ \Phi_{\alpha,\beta}(s) = \frac{s^\alpha}{\bigl(1+(1-\alpha)s^{\alpha-1}\bigr)^\beta}, \qquad 0<\alpha<1, \ \beta\ge0. \] The operator preserves the Caputo-type high-frequency behavior while allowing a controlled modification of the low-frequency regime via $\beta$. We develop an explicit symbolic/Volterra theory: Prabhakar-type kernels, a left-inverse Volterra integral, and a fractional fundamental theorem of calculus. A central contribution is a sharp clarification of the Bernstein structure of the symbol. We show that the natural factorization $\Phi_{\alpha,\beta}(s)=s^\alpha h_\alpha(s)^\beta$ does not fit the classical Bernstein product mechanism for any $\beta>0$. Nevertheless, by a direct complete-monotonicity argument on $\Phi'_{\alpha,\beta}$, we prove the exact Bernstein threshold \[ \Phi_{\alpha,\beta}\in\mathcal{BF} \quad\Longleftrightarrow\quad 0\le\beta\le1. \] where $\mathcal{BF}$ denotes the class of Bernstein functions
\noindent For $\beta>1$, the Bernstein property fails by a low-frequency asymptotic convexity obstruction. This shows that the Bernstein nature of the natural range $0\le\beta\le1$ is genuine but is not produced by the standard product mechanism. We then establish well-posedness of abstract W-fractional Cauchy problems with sectorial generators by resolvent estimates and Laplace inversion, yielding a W-resolvent family with temporal regularity and smoothing properties. As an illustration, we apply the theory to a W-fractional diffusion model and discuss the effect of $\beta$ on the relaxation of spectral modes.

Submission history

From: Mohamed Wakrim [view email]
[v1] Tue, 6 Jan 2026 10:04:24 UTC (402 KB)
[v2] Tue, 2 Jun 2026 15:29:38 UTC (404 KB)