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

G
Google Developers Blog
Google DeepMind News
Google DeepMind News
Microsoft Security Blog
Microsoft Security Blog
Martin Fowler
Martin Fowler
MyScale Blog
MyScale Blog
The GitHub Blog
The GitHub Blog
I
InfoQ
A
About on SuperTechFans
GbyAI
GbyAI
宝玉的分享
宝玉的分享
爱范儿
爱范儿
博客园 - 【当耐特】
博客园 - 司徒正美
博客园 - 聂微东
P
Proofpoint News Feed
WordPress大学
WordPress大学
云风的 BLOG
云风的 BLOG
Last Week in AI
Last Week in AI
阮一峰的网络日志
阮一峰的网络日志
B
Blog RSS Feed
Jina AI
Jina AI
aimingoo的专栏
aimingoo的专栏
J
Java Code Geeks
博客园 - 叶小钗

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
Regularity thresholds for anomalous dissipation and relat...
[Submitted on 12 Mar 2026 (v1), last revised 16 Jul 2026 (this v · 2026-03-12 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:We prove the absence of anomalous dissipation for passive scalars driven by some random autonomous divergence-free vector fields in $\mathbb T^d$. In dimension $d=2$ we just need continuity almost surely and a mild nondegeneracy condition on the randomness. In dimension $d\geq 3$ we assume a special geometric structure and almost sure Hölder regularity with a Hölder exponent bigger than $\frac{1}{8}$. No regularity is assumed on the passive scalar except for boundedness in the initial data. The proof relies on dimension-theoretic arguments, as opposed to commutator estimates. A consequence of these results is that the same assumptions prevent (almost surely) many other expected properties of turbulent flows, such as anomalous regularization, the Yaglom-Obukhov-Corrsin law, and Richardson diffusion.

Submission history

From: Daniel W. Boutros [view email]
[v1] Thu, 12 Mar 2026 02:44:36 UTC (21 KB)
[v2] Wed, 1 Apr 2026 23:00:35 UTC (26 KB)
[v3] Thu, 16 Jul 2026 20:05:52 UTC (27 KB)