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
Stochastic numerical approximation for nonlinear Fokker-P...
[Submitted on 8 Apr 2025 (v1), last revised 6 Aug 2026 (this ver · 2025-04-08 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper studies the convergence rate of the Euler-Maruyama scheme for systems of interacting particles used to approximate solutions of nonlinear Fokker-Planck equations with singular interaction kernels, such as the Keller-Segel model. We derive explicit error estimates in the large-particle limit for two objects: the empirical measure of the interacting particle system and the density distribution of a single particle. Specifically, under certain assumptions on the interaction kernel and initial conditions, we show that the convergence rate of both objects towards solutions of the corresponding nonlinear FokkerPlanck equation depends polynomially on N (the number of particles) and on h (the discretization step). The analysis shows that the scheme converges despite singularities in the drift term. To the best of our knowledge, there are no existing results in the literature of such kind for the singular kernels considered in this work.

Submission history

From: Nicoleta Cazacu [view email]
[v1] Tue, 8 Apr 2025 15:24:44 UTC (37 KB)
[v2] Thu, 6 Aug 2026 09:31:57 UTC (35 KB)