Array-RQMC-WOS cuts Monte Carlo variance by 57-2290 times with empirical rates n^{-1.4} to n^{-1.8} and introduces a column-wise mean dimension to explain the gain.
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Randomized quasi-Monte Carlo applied to walk-on-spheres yields variance reduction factors between 1.8 and 10.7 and median convergence slightly better than O(n^{-1.1}) across five examples.
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.
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Walk on spheres and Array-RQMC
Array-RQMC-WOS cuts Monte Carlo variance by 57-2290 times with empirical rates n^{-1.4} to n^{-1.8} and introduces a column-wise mean dimension to explain the gain.
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Randomized quasi-Monte Carlo for walk on spheres
Randomized quasi-Monte Carlo applied to walk-on-spheres yields variance reduction factors between 1.8 and 10.7 and median convergence slightly better than O(n^{-1.1}) across five examples.
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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.