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- W2886813100 abstract "We introduce a variant of the Hybrid Monte Carlo (HMC) algorithm to address large-deviation statistics in stochastic hydrodynamics. Based on the path-integral approach to stochastic (partial) differential equations, our HMC algorithm samples space-time histories of the dynamical degrees of freedom under the influence of random noise. First, we validate and benchmark the HMC algorithm by reproducing multiscale properties of the one-dimensional Burgers equation driven by Gaussian and white-in-time noise. Second, we show how to implement an importance sampling protocol to significantly enhance, by orders of magnitudes, the probability to sample extreme and rare events, making it possible to estimate moments of field variables of extremely high order (up to 30 and more). By employing reweighting techniques, we map the biased configurations back to the original probability measure in order to probe their statistical importance. Finally, we show that by biasing the system towards very intense negative gradients, the HMC algorithm is able to explore the statistical fluctuations around instanton configurations. Our results will also be interesting and relevant in lattice gauge theory since they provide insight into reweighting techniques." @default.
- W2886813100 created "2018-08-22" @default.
- W2886813100 creator A5005619051 @default.
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- W2886813100 date "2019-05-07" @default.
- W2886813100 modified "2023-10-01" @default.
- W2886813100 title "Hybrid Monte Carlo algorithm for sampling rare events in space-time histories of stochastic fields" @default.
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- W2886813100 doi "https://doi.org/10.1103/physreve.99.053303" @default.
- W2886813100 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31212557" @default.
- W2886813100 hasPublicationYear "2019" @default.
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