Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Bernhard Mehlig"'
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045032 (2023)
Bayesian inference can quantify uncertainty in the predictions of neural networks using posterior distributions for model parameters and network output. By looking at these posterior distributions, one can separate the origin of uncertainty into alea
Externí odkaz:
https://doaj.org/article/b297eca8815f44b3b0fce0c7832368a9
Autor:
Marina Rafajlović, Anders Eriksson, Anna Rimark, Sara Hintz-Saltin, Grégory Charrier, Marina Panova, Carl André, Kerstin Johannesson, Bernhard Mehlig
Publikováno v:
PLoS ONE, Vol 8, Iss 10, p e75587 (2013)
Genetic variation within and among populations is influenced by the genetic content of the founders and the migrants following establishment. This is particularly true if populations are small, migration rate low and habitats arranged in a stepping-s
Externí odkaz:
https://doaj.org/article/9edf644383c2488aa65d2fe25e5955fc
Autor:
Marina Panova, Johan Boström, Tobias Hofving, Therese Areskoug, Anders Eriksson, Bernhard Mehlig, Tuuli Mäkinen, Carl André, Kerstin Johannesson
Publikováno v:
PLoS ONE, Vol 5, Iss 3, p e9640 (2010)
While males usually benefit from as many matings as possible, females often evolve various methods of resistance to matings. The prevalent explanation for this is that the cost of additional matings exceeds the benefits of receiving sperm from a larg
Externí odkaz:
https://doaj.org/article/dd20075e4e964ef88dc304d5bbc569d1
Publikováno v:
Physical Review Fluids. 8
Caustic singularities of the spatial distribution of particles in turbulent aerosols enhance collision rates and accelerate coagulation. Here we investigate how and where caustics form at weak particle inertia, by analysing a three-dimensional Gaussi
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 147:2160-2172
The distribution of liquid water in ice-free clouds determines their radiative properties, a significant source of uncertainty in weather and climate models. Evaporation and turbulent mixing cause a cloud to display large variations in droplet-number
Autor:
Filippo De Lillo, Matteo Borgnino, Guido Boffetta, Kristian Gustafsson, Bernhard Mehlig, Massimo Cencini
Many phytoplankters are able to swim, and are thus not passively transported by the flow. Although usually weak, ocean turbulence can affect the motion of one-celled organisms in nontrivial ways. It is known that an ellipsoidal body can be rotated by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37ec3fab5d75caf8b55911dca9526c82
https://doi.org/10.5194/egusphere-egu22-13293
https://doi.org/10.5194/egusphere-egu22-13293
Autor:
Bernhard Mehlig
Marine micro-organisms must cope with complex flow patterns and even turbulence as they navigate the ocean. To survive they must avoid predation and find efficient energy sources. A major difficulty in analysing possible survival strategies is that t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4c290f850b942bebb5bbb442967c9e86
https://doi.org/10.5194/egusphere-egu22-7811
https://doi.org/10.5194/egusphere-egu22-7811
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031167874
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff699c05136b0e01101dc50268ddb340
https://doi.org/10.1007/978-3-031-16788-1_36
https://doi.org/10.1007/978-3-031-16788-1_36
Publikováno v:
Nature Machine Intelligence. 2:94-103
The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—