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

B
Blog
A
About on SuperTechFans
Microsoft Security Blog
Microsoft Security Blog
Y
Y Combinator Blog
罗磊的独立博客
J
Java Code Geeks
人人都是产品经理
人人都是产品经理
MongoDB | Blog
MongoDB | Blog
The GitHub Blog
The GitHub Blog
G
Google Developers Blog
U
Unit 42
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - Franky
Jina AI
Jina AI
F
Fortinet All Blogs
H
Help Net Security
B
Blog RSS Feed
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Last Week in AI
Last Week in AI
博客园 - 司徒正美
云风的 BLOG
云风的 BLOG
M
MIT News - Artificial intelligence
C
Check Point Blog
GbyAI
GbyAI

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
The gradient condition and the contribution of the dynami...
Makiko Sasada · 2017-04-12 · via math.PR updates on arXiv.org

In the diffusive hydrodynamic limit for a symmetric interacting particle system (such as the exclusion process, the zero range process, the stochastic Ginzburg-Landau model, the energy exchange model), a possibly non-linear diffusion equation is derived as the hydrodynamic equation. The bulk diffusion coefficient of the limiting equation is given by Green-Kubo formula and it can be characterized by a variational formula. In the case the system satisfies the gradient condition, the variational problem is explicitly solved and the diffusion coefficient is given from the Green-Kubo formula through a static average only. In other words, the contribution of the dynamical part of Green-Kubo formula is 0. In this paper, we consider the converse, namely if the contribution of the dynamical part of Green-Kubo formula is 0, does it imply the system satisfies the gradient condition or not. We show that if the equilibrium measure μ is product and {L^2} space of its single site marginal is separable, then the converse also holds. As an application of the result, we consider a class of stochastic models for energy transport studied by Gaspard and Gilbert in [1, 2], where the exact problem is discussed for this specific model.