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

罗磊的独立博客
Recent Announcements
Recent Announcements
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
有赞技术团队
有赞技术团队
J
Java Code Geeks
T
The Blog of Author Tim Ferriss
MyScale Blog
MyScale Blog
人人都是产品经理
人人都是产品经理
aimingoo的专栏
aimingoo的专栏
U
Unit 42
The GitHub Blog
The GitHub Blog
云风的 BLOG
云风的 BLOG
T
Tailwind CSS Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 三生石上(FineUI控件)
Apple Machine Learning Research
Apple Machine Learning Research
小众软件
小众软件
Hugging Face - Blog
Hugging Face - Blog
博客园 - 司徒正美
腾讯CDC
I
InfoQ
GbyAI
GbyAI
博客园_首页

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
Ergodicity of stochastic reaction-diffusion equations on ...
[Submitted on 25 Jun 2026] · 2026-06-26 · via math.PR updates on arXiv.org

View PDF HTML (experimental)

Abstract:We consider the stochastic reaction-diffusion equation on the whole space: \begin{align*}
\left\{
\begin{aligned}
du(t,x) &=\frac{1}{2}\partial_{xx} u(t,x) dt+b(u(t,x))dt+ \sigma(u(t,x)) W(dt,dx),\quad t\geq 0,\ x\in \mathbb{R},\\
u(0,x)&=u_0(x), \quad x\in \mathbb{R},
\end{aligned}
\right. \end{align*} where $W(dt,dx)$ is a space-time white noise, $b$, $\sigma$ are measurable coefficients. We first show that the solution is not strong Feller, and then establish the existence and uniqueness of invariant measures, exponential mixing as well as irreducibility for the solutions. To overcome the difficulties caused by the unbounded domain, we design special controls and controlled equations to prove the irreducibility. To obtain the exponential mixing property under the dissipative condition $$(b(x)-b(y))(x-y)\leq -\alpha (x-y)^2,$$ the obstacle is the lack of the Itô formula/energy equality. To circumvent the problem, we manage to find a new way to fully exploit comparison principles, which we believe could be useful for other type of stochastic partial differential equations driven by multiplicative space-time noise. We note that the dissipative condition allows the coefficients to be of polynomial, even exponential growth. There exist plenty of models that satisfy the dissipative condition, including the Allen-Cahn type equations.
To the best of our knowledge, this is the first paper to establish the ergodicity, exponential mixing and irreducibility of stochastic reaction-diffusion equations (SRDEs) driven by multiplicative space-time noise on unbounded domains. The results on exponential mixing are also new for (SRDEs) driven by multiplicative space-time noise on bounded domains.

Submission history

From: Shijie Shang [view email]
[v1] Thu, 25 Jun 2026 05:30:51 UTC (21 KB)