001     7819
005     20230217124329.0
024 7 _ |a 10.1103/PhysRevE.80.061134
|2 DOI
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037 _ _ |a PreJuSER-7819
041 _ _ |a eng
082 _ _ |a 530
084 _ _ |2 WoS
|a Physics, Fluids & Plasmas
084 _ _ |2 WoS
|a Physics, Mathematical
100 1 _ |a Suciu, N.
|b 0
|0 P:(DE-HGF)0
245 _ _ |a Persistent memory of diffusing particles
260 _ _ |a College Park, Md.
|b APS
|c 2009
264 _ 1 |3 online
|2 Crossref
|b American Physical Society (APS)
|c 2009-12-28
264 _ 1 |3 print
|2 Crossref
|b American Physical Society (APS)
|c 2009-12-01
300 _ _ |a 061134-1
336 7 _ |a Journal Article
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440 _ 0 |a Physical Review E
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500 _ _ |a Record converted from VDB: 12.11.2012
520 _ _ |a The variance of the advection-diffusion processes with variable coefficients is exactly decomposed as a sum of dispersion terms and memory terms consisting of correlations between velocity and initial positions. For random initial conditions, the memory terms quantify the departure of the preasymptotic variance from the time-linear diffusive behavior. For deterministic initial conditions, the memory terms account for the memory of the initial positions of the diffusing particles. Numerical simulations based on a global random walk algorithm show that the influence of the initial distribution of the cloud of particles is felt over hundreds of dimensionless times. In case of diffusion in random velocity fields with finite correlation range the particles forget the initial positions in the long-time limit and the variance is self-averaging, with clear tendency toward normal diffusion.
536 _ _ |a Terrestrische Umwelt
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542 _ _ |i 2009-12-28
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588 _ _ |a Dataset connected to Web of Science
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653 2 0 |2 Author
|a diffusion
653 2 0 |2 Author
|a numerical analysis
653 2 0 |2 Author
|a random processes
653 2 0 |2 Author
|a stochastic processes
700 1 _ |a Vamos, C.
|b 1
|0 P:(DE-HGF)0
700 1 _ |a Radu, F.A.
|b 2
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700 1 _ |a Vereecken, H.
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700 1 _ |a Knabner, P.
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773 1 8 |a 10.1103/physreve.80.061134
|b American Physical Society (APS)
|d 2009-12-28
|n 6
|p 061134
|3 journal-article
|2 Crossref
|t Physical Review E
|v 80
|y 2009
|x 1539-3755
773 _ _ |a 10.1103/PhysRevE.80.061134
|g Vol. 80, p. 061134-1
|p 061134
|n 6
|q 80<061134-1
|0 PERI:(DE-600)2844562-4
|t Physical review / E
|v 80
|y 2009
|x 1539-3755
856 7 _ |u http://dx.doi.org/10.1103/PhysRevE.80.061134
856 4 _ |u https://juser.fz-juelich.de/record/7819/files/PhysRevE.80.061134.pdf
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914 1 _ |y 2009
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999 C 5 |1 C. W. Gardiner
|y 2009
|2 Crossref
|t Stochastic Methods: A Handbook for the Natural and Social Sciences
|o C. W. Gardiner Stochastic Methods: A Handbook for the Natural and Social Sciences 2009
999 C 5 |a 10.1103/PhysRev.124.983
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999 C 5 |a 10.1103/PhysRevLett.89.100601
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999 C 5 |a 10.1103/PhysRevB.79.094306
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999 C 5 |a 10.1103/PhysRevE.77.031123
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999 C 5 |a 10.1103/PhysRevE.77.022101
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999 C 5 |a 10.1016/S0370-1573(98)00083-0
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999 C 5 |1 P. E. Kloeden
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999 C 5 |a 10.1016/0370-1573(90)90099-N
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999 C 5 |a 10.1239/aap/1011994031
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999 C 5 |1 N. Suciu
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999 C 5 |1 N. Suciu
|2 Crossref
|t Monte Carlo and Quasi–Monte Carlo Methods 2008
|o N. Suciu Monte Carlo and Quasi–Monte Carlo Methods 2008
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|2 Crossref
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