Identification of anomalous diffusion sources by unsupervised learning
Fractional Brownian motion (fBm) is a ubiquitous diffusion process in which the memory effects of the stochastic transport result in the mean-squared particle displacement following a power law 〈Δr^{2}〉∼t^{α}, where the diffusion exponent α characterizes whether the transport is subdiffusive (α<1...
Main Authors: | Raviteja Vangara, Kim Ø. Rasmussen, Dimiter N. Petsev, Golan Bel, Boian S. Alexandrov |
---|---|
Format: | Article |
Language: | English |
Published: |
American Physical Society
2020-05-01
|
Series: | Physical Review Research |
Online Access: | http://doi.org/10.1103/PhysRevResearch.2.023248 |
Similar Items
-
Emulsions : structure, stability and interactions /
by: Petsev, Dimiter N.
Published: (2004) -
Thermodynamic Properties of Electrolyte Solutions, Derived from Fluctuation Correlations: A Methodological Review
by: Dimiter N. Petsev
Published: (2022-06-01) -
Unsupervised learning with diffusion models
by: Wang, Jiankun
Published: (2023) -
Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.
by: Filip L Iliev, et al.
Published: (2018-01-01) -
Unsupervised detection of anomalous sounds for machine condition monitoring
by: Xie, Yonggang
Published: (2022)