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

V
Visual Studio Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 聂微东
博客园 - 【当耐特】
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
C
Check Point Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
美团技术团队
WordPress大学
WordPress大学
Last Week in AI
Last Week in AI
Y
Y Combinator Blog
IT之家
IT之家
T
Tailwind CSS Blog
月光博客
月光博客
Vercel News
Vercel News
V
V2EX
Engineering at Meta
Engineering at Meta
B
Blog
Stack Overflow Blog
Stack Overflow Blog
A
About on SuperTechFans
Hugging Face - Blog
Hugging Face - Blog
人人都是产品经理
人人都是产品经理
腾讯CDC
I
InfoQ

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
Law of Iterated Logarithms and Fractal Properties of the ...
Sayan Das, Promit Ghosal · 2021-01-04 · via math.PR updates on arXiv.org

We consider the Cole-Hopf solution of the (1+1)-dimensional KPZ equation started from the narrow wedge initial condition. In this article, we ask how the peaks and valleys of the KPZ height function (centered by time/24) at any spatial point grow as time increases. Our first main result is about the law of iterated logarithms for the KPZ equation. As time variable $t$ goes to $\infty$, we show that the limsup of the KPZ height function with the scaling by $t^{1/3}(\log\log t)^{2/3}$ is almost surely equal to $(\frac{3}{4\sqrt{2}})^{2/3}$ whereas the liminf of the height function with the scaling by $t^{1/3}(\log\log t)^{1/3}$ is almost surely equal to $-6^{1/3}$. Our second main result concerns with the macroscopic fractal properties of the KPZ equation. Under exponential transformation of the time variable, we show that the peaks of KPZ height function mutate from being monofractal to multifractal, a property reminiscent of a similar phenomenon in Brownian motion [Khoshnevisan-Kim-Xiao 17, Theorem 1.4]. The proofs of our main results hinge on the following three key tools: (1) a multi-point composition law of the KPZ equation which can be regarded as a generalization of the two point composition law from [Corwin-Ghosal-Hammond 19, Proposition 2.9], (2) the Gibbsian line ensemble techniques from [Corwin-Hammond 14, Corwin-Hammond 16, Corwin-Ghosal-Hammond 19] and, (3) the tail probabilities of the KPZ height function in short time and its spatio-temporal modulus of continuity. We advocate this last tool as one of our new and important contributions which might garner independent interest.