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

美团技术团队
N
Netflix TechBlog - Medium
WordPress大学
WordPress大学
云风的 BLOG
云风的 BLOG
J
Java Code Geeks
V
Visual Studio Blog
H
Help Net Security
Engineering at Meta
Engineering at Meta
Hugging Face - Blog
Hugging Face - Blog
Microsoft Security Blog
Microsoft Security Blog
腾讯CDC
博客园 - 【当耐特】
B
Blog
Stack Overflow Blog
Stack Overflow Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
大猫的无限游戏
大猫的无限游戏
GbyAI
GbyAI
博客园 - 司徒正美
博客园 - 叶小钗
Y
Y Combinator Blog
MyScale Blog
MyScale 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
G
Google Developers Blog
酷 壳 – CoolShell
酷 壳 – CoolShell

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
Probabilistic representation of weak solutions to a parab...
Masaaki Tsuchiya, Hajime Kawakami · 2017-10-14 · via math.PR updates on arXiv.org

The probabilistic representation of weak solutions to a parabolic boundary value problem is established in the following framework. The boundary value problem consists of a second order parabolic equation defined on a time-varying Lipschitz domain in a Euclidean space and of a mixed boundary condition composed of a Robin and the homogeneous Dirichlet conditions. It is assumed that the time-varying domain is included in a fixed smooth domain and that a certain part of the boundary of the time-varying domain is also included in the boundary of the fixed domain, say the fixed boundary. The Robin condition is imposed on a part of the boundary included in the fixed one and the Dirichlet condition on the rest of the boundary. Such a parabolic boundary value problem always has a unique weak solution for given data; however it does not possess a classical or strong solution in general, even in the case of equations with constant coefficients. The stochastic solution to the boundary value problem is also considered and, by showing the equality between both the solutions, it is obtained the probabilistic representation for the weak solution. Furthermore, it is ensured that, for the weak solution, the stochastic solution gives a version which is continuous up to the lateral boundary of the domain except the border of the adjoining place imposed each of the boundary conditions. As an application, it is shown the continuity property of a functional (cost function) related to an optimal stopping problem motivated by an inverse problem determining the shape of a domain.