Walk on Heat Stars provides a boundary-integral Monte Carlo solver for parabolic PDEs with Neumann conditions via exact heat-ball sampling that yields unbiased estimators.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 5roles
background 1polarities
background 1representative citing papers
Monte Carlo estimation of volumetric Steklov operators enables robust spectral geometry processing at the scale of hundreds of thousands of in-the-wild meshes and supports contrastive 3D representation learning.
Array-RQMC-WoS reduces Monte Carlo MSE/variance 71- to 3087-fold at n=2^17 on five Dirichlet problems, with empirical rates n^{-1.4} to n^{-1.8}, far above plain RQMC-WoS.
A relaxed Picard iteration plus heteroscedastic boundary denoising lets Monte Carlo PDE solvers solve heat equations with nonlinear radiation boundary conditions more accurately than linearization.
RQMC applied to walk-on-spheres for harmonic functions yields median variance decay slightly better than O(n^{-1.1}) and reduction factors 1.8-10.7 across four methods and five examples.
citing papers explorer
-
Walking on Heat Stars for Parabolic Heat Equations with Neumann Boundary Conditions
Walk on Heat Stars provides a boundary-integral Monte Carlo solver for parabolic PDEs with Neumann conditions via exact heat-ball sampling that yields unbiased estimators.
-
Monte Carlo Steklov Operators for Large-Scale Geometry Processing in the Wild
Monte Carlo estimation of volumetric Steklov operators enables robust spectral geometry processing at the scale of hundreds of thousands of in-the-wild meshes and supports contrastive 3D representation learning.
-
Walk on spheres and Array-RQMC
Array-RQMC-WoS reduces Monte Carlo MSE/variance 71- to 3087-fold at n=2^17 on five Dirichlet problems, with empirical rates n^{-1.4} to n^{-1.8}, far above plain RQMC-WoS.
-
Monte Carlo PDE Solvers for Nonlinear Radiative Boundary Conditions
A relaxed Picard iteration plus heteroscedastic boundary denoising lets Monte Carlo PDE solvers solve heat equations with nonlinear radiation boundary conditions more accurately than linearization.
-
Randomized quasi-Monte Carlo for walk on spheres
RQMC applied to walk-on-spheres for harmonic functions yields median variance decay slightly better than O(n^{-1.1}) and reduction factors 1.8-10.7 across four methods and five examples.