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

爱范儿
爱范儿
WordPress大学
WordPress大学
博客园 - 【当耐特】
The Cloudflare Blog
B
Blog
Last Week in AI
Last Week in AI
小众软件
小众软件
量子位
S
SegmentFault 最新的问题
V
Visual Studio Blog
博客园 - 叶小钗
美团技术团队
阮一峰的网络日志
阮一峰的网络日志
Hugging Face - Blog
Hugging Face - 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
宝玉的分享
宝玉的分享
A
About on SuperTechFans
雷峰网
雷峰网
J
Java Code Geeks
Microsoft Azure Blog
Microsoft Azure Blog
腾讯CDC
MongoDB | Blog
MongoDB | Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
Martin Fowler
Martin Fowler

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
A generalized Dean-Kawasaki equation for an interacting B...
Paul C Bressloff · 2023-12-11 · via math.PR updates on arXiv.org

The Dean-Kawasaki (DK) equation is a stochastic partial differential equation (SPDE) for the global density $ρ$ of a gas of $N$ over-damped Brownian particles. In the thermodynamic limit $N\rightarrow \infty$ with weak pairwise interactions, the expectation ${\mathbb E}[ρ]$ converges in distribution to the solution of a McKean-Vlasov (MV) equation. In this paper we derive a generalized DK equation for an interacting Brownian gas in a partially absorbing one-dimensional medium. In the case of the half-line with a totally reflecting boundary at $x=0$, the generalized DK equation is an SPDE for the joint global density $ρ(x,\ell,t)=N^{-1}\sum_{j=1}^Nδ(x-X_j(t))δ(\ell-L_j(t))$, where $X_j(t)$ and $L_j(t)$ denote the position and local time of the $j$th particle, respectively. Assuming the DK equation has a well-defined mean field limit, we derive the MV equation on the half-line with a reflecting boundary, and analyze stationary solutions for a Curie-Weiss (quadratic) interaction potential. We then use an encounter-based approach to develop the analogous theory for a partially absorbing boundary at $x=0$. Each particle is independently absorbed when its local time $L_j(t)$ exceeds a random threshold $\widehat{\ell}_j$ with probability distribution $Ψ(\ell)=¶[\widehat{\ell}_j>\ell]$. The joint global density is now summed over the set of particles that have not yet been absorbed, and expectations are taken with respect to the Gaussian noise and the random thresholds $\widehat{\ell}_j$. Extensions to finite intervals and partially absorbing traps are also considered.