Machine Learning · 2026-11-09

FasterRW: An Efficient Prior-Assisted Random Walk Solver for Thermal Analysis

arXiv:2611.08490v1 Announce Type: new Abstract: Thermal simulation is becoming increasingly critical in modern IC design and manufacturing. Feynman--Kac random walk methods can estimate localized temperatures without solving the global field, but in practical settings without Dirichlet boundaries they require very long paths to control the truncation residual.

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arXiv:2611.08490v1 Announce Type: new Abstract: Thermal simulation is becoming increasingly critical in modern IC design and manufacturing. Feynman--Kac random walk methods can estimate localized temperatures without solving the global field, but in practical settings without Dirichlet boundaries they require very long paths to control the truncation residual. This paper presents FasterRW, a continuation-value framework for accelerating Feynman--Kac thermal random walks. We prove a stopping-time residual identity showing that the expected tail after truncation equals the current attenuation weight multiplied by the temperature at the stopping location.

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Original source: arXiv ML ↗

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