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Precursors to rare events in stochastic resonance

Giorgini, Ludovico T. (author)
Stockholms universitet,Nordiska institutet för teoretisk fysik (Nordita),Stockholm Univ, S-10691 Stockholm, Sweden.;Nordita SU
Lim, S. H. (author)
Stockholms universitet,Nordiska institutet för teoretisk fysik (Nordita),Stockholm Univ, S-10691 Stockholm, Sweden.
Moon, W. (author)
Stockholms universitet,Nordiska institutet för teoretisk fysik (Nordita),Matematiska institutionen,Stockholm Univ, S-10691 Stockholm, Sweden.;Stockholm Univ, Dept Math, S-10691 Stockholm, Sweden.
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Wettlaufer, John (author)
Stockholms universitet,KTH,Nordic Institute for Theoretical Physics NORDITA,Stockholm Univ, S-10691 Stockholm, Sweden.;Yale Univ, New Haven, CT 06520 USA.,Nordiska institutet för teoretisk fysik (Nordita),Yale University, USA
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 (creator_code:org_t)
2020-03-17
2020
English.
In: Europhysics letters. - : IOP Publishing. - 0295-5075 .- 1286-4854. ; 129:4
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • In stochastic resonance, a periodically forced Brownian particle in a double-well potential jumps between minima at rare increments, the prediction of which poses a major theoretical challenge. Here, we use a path-integral method to find a precursor to these transitions by determining the most probable (or "optimal") space-time path of a particle. We characterize the optimal path using a direct comparison principle between the Langevin and Hamiltonian dynamical descriptions, allowing us to express the jump condition in terms of the accumulation of noise around the stable periodic path. In consequence, as a system approaches a rare event these fluctuations approach one of the deterministic minimizers, thereby providing a precursor for predicting a stochastic transition. We demonstrate the method numerically, which allows us to determine whether a state is following a stable periodic path or will experience an incipient jump with a high probability. The vast range of systems that exhibit stochastic resonance behavior insures broad relevance of our framework, which allows one to extract precursor fluctuations from data. open access editor's choice Copyright

Subject headings

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)
NATURVETENSKAP  -- Fysik (hsv//swe)
NATURAL SCIENCES  -- Physical Sciences (hsv//eng)

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