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FasterRW: An Efficient Prior-Assisted Random Walk Solver for Thermal Analysis — arXiv:2611.08490

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08490
License (as archived): CC0-1.0
Archived: 2026-11-09T22:41:08.037Z
SHA-256 of the archived text: 915767d47a9c9b998b4db9b2fa889a49594fcddb3928755a79c76a40ffc660bd
Stable URL of this page: https://metraipolis.com/documento/dc_cd523cd261af

How to cite

FasterRW: An Efficient Prior-Assisted Random Walk Solver for Thermal Analysis — arXiv:2611.08490 — arXiv (en), https://arxiv.org/abs/2611.08490 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd523cd261af (Metraipolis archive dc_cd523cd261af, sha256 915767d47a9c9b998b4db9b2fa889a49594fcddb3928755a79c76a40ffc660bd)

BibTeX

@misc{dccd523cd261af,
  title = {FasterRW: An Efficient Prior-Assisted Random Walk Solver for Thermal Analysis — arXiv:2611.08490},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08490},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd523cd261af; sha256 915767d47a9c9b998b4db9b2fa889a49594fcddb3928755a79c76a40ffc660bd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08490. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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. This identity enables a prior-corrected single-point estimator: a cheap, noisy prior replaces the unknown continuation value, making the truncation bias scale with prior error rather than absolute temperature and yielding an error-controlled threshold-selection rule. For multi-point queries, FasterRW further converts cross-query pass-through tails into pseudo-samples and refines the resulting observations in prior-error coordinates through kriging. Compared with prior state-of-the-art methods, FasterRW achieves up to $28\times$ acceleration while maintaining accuracy. Our code is open-sourced at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Numerical Analysis (math.NA); Computation (stat.CO).
Pagina del lavoro: https://arxiv.org/abs/2611.08490 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-09T22:33:47.127Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).