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

博客园 - 叶小钗
D
Docker
Google DeepMind News
Google DeepMind News
Y
Y Combinator Blog
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
Blog — PlanetScale
Blog — PlanetScale
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
U
Unit 42
博客园 - 【当耐特】
N
Netflix TechBlog - Medium
V
Visual Studio Blog
Microsoft Azure Blog
Microsoft Azure Blog
博客园_首页
Recent Announcements
Recent Announcements
GbyAI
GbyAI
T
Tailwind CSS Blog
S
SegmentFault 最新的问题
WordPress大学
WordPress大学
T
The Blog of Author Tim Ferriss
Engineering at Meta
Engineering at Meta
L
LangChain Blog
A
About on SuperTechFans
M
MIT News - Artificial intelligence
B
Blog

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
Internal aggregation models with multiple sources and obs...
Uta Freiberg, Nico Heizmann, Robin Kaiser, Ecaterina Sava-Huss · 2022-12-22 · via math.PR updates on arXiv.org

We consider the doubly infinite Sierpinski gasket graph $SG_0$, rescale it by factor $2^{-n}$, and on the rescaled graphs $SG_n=2^{-n}SG_0$, for every $n\in \mathbb{N}$, we investigate the limit shape of three aggregation models with initial configuration $σ_n$ of particles supported on multiple vertices. The models under consideration are: divisible sandpile in which the excess mass is distributed among the vertices until each vertex is stable and has mass less or equal to one, internal DLA in which particles do random walks until finding an empty site, and rotor aggregation in which particles perform deterministic counterparts of random walks until finding an empty site. We denote by $SG=cl(\cup_{n=0}^{\infty} SG_n)$ the infinite Sierpinski gasket, which is a closed subset of $\mathbb{R}^2$, for which $SG_n$ represents the level-n approximating graph, and we consider a continuous function $σ:SG\to\mathbb{N}$. For $σ$ we solve the obstacle problem and we describe the noncoincidence set $D\subset SG$ as the solution of a free boundary problem on the fractal $SG$. If the discrete particle configurations $σ_n$ on the approximating graphs $SG_n$ converge pointwise to the continuous function $σ$ on the limit set $SG$, we prove that, as $n\to\infty$, the scaling limits of the three aforementioned models on $SG_n$ starting with initial particle configuration $σ_n$ converge to the deterministic solution $D$ of the free boundary problem on the limit set $SG\subset\mathbb{R}^2$. For $D$ we also investigate boundary regularity properties.