Refine
Year of publication
Document Type
- Article (35956)
- Doctoral Thesis (6515)
- Monograph/Edited Volume (5569)
- Postprint (3296)
- Review (2295)
- Part of a Book (1088)
- Other (966)
- Preprint (569)
- Conference Proceeding (561)
- Part of Periodical (531)
Language
- English (30953)
- German (26148)
- Spanish (363)
- French (330)
- Italian (115)
- Russian (112)
- Multiple languages (70)
- Hebrew (36)
- Portuguese (25)
- Polish (24)
Keywords
- Germany (206)
- climate change (182)
- Deutschland (145)
- machine learning (88)
- European Union (79)
- diffusion (78)
- Sprachtherapie (77)
- morphology (74)
- Logopädie (73)
- Migration (73)
Institute
- Institut für Biochemie und Biologie (5491)
- Institut für Physik und Astronomie (5457)
- Institut für Geowissenschaften (3673)
- Institut für Chemie (3485)
- Wirtschaftswissenschaften (2645)
- Historisches Institut (2524)
- Department Psychologie (2353)
- Institut für Mathematik (2151)
- Institut für Romanistik (2114)
- Sozialwissenschaften (1883)
Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations.