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

J
Java Code Geeks
美团技术团队
Microsoft Azure Blog
Microsoft Azure Blog
V
Visual Studio Blog
Jina AI
Jina AI
博客园_首页
M
MIT News - Artificial intelligence
D
DataBreaches.Net
L
LangChain Blog
宝玉的分享
宝玉的分享
F
Fortinet All Blogs
A
About on SuperTechFans
月光博客
月光博客
Stack Overflow Blog
Stack Overflow Blog
Google DeepMind News
Google DeepMind News
N
Netflix TechBlog - Medium
Y
Y Combinator Blog
腾讯CDC
Vercel News
Vercel News
雷峰网
雷峰网
GbyAI
GbyAI
aimingoo的专栏
aimingoo的专栏
阮一峰的网络日志
阮一峰的网络日志
博客园 - 【当耐特】

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
Mean-square attractors for non-autonomous Caputo fraction...
[Submitted on 14 Feb 2026 (v1), last revised 10 Jul 2026 (this v · 2026-02-14 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper investigates the existence of mean-square attractors for a class of non-autonomous Caputo fractional stochastic differential equations of order $\alpha\in (\frac{1}{2},1)$, with a driving system on a compact base space $P$ and tempered fractional noise. We first construct a mean-square semi-dynamical system on $\mathfrak{C} \times P$ that carries a skew-product semi-flow structure, where $\mathfrak{C}=C(\mathbb{R}^{+}, L^{2}(\Omega, \mathcal{F}; \mathbb{R}^d))$ denotes the space of continuous functions from $ \mathbb{R}^{+}$ into $L^2(\Omega, \mathcal{F}; \mathbb{R}^d)$. A global forward attracting set is then established in the weak mean-square topology. Moreover, by endowing the function space $\mathfrak{C}_{w}=C(\mathbb{R}^{+}, L^{2}_w(\Omega, \mathcal{F}; \mathbb{H}))$ with an appropriate topology that renders it complete, we show that the skew-product semi-flow possesses a bounded and closed mean-square attractor within $\mathfrak{C}_{w} \times P$. It is worth emphasizing that completeness plays a crucial role here: without this property, the attractor need not exist.

Submission history

From: Lijuan Zhang [view email]
[v1] Sat, 14 Feb 2026 02:37:11 UTC (17 KB)
[v2] Fri, 10 Jul 2026 05:36:50 UTC (318 KB)