Zobrazeno 1 - 10
of 224
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
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
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:
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:
Probert, Matt
Publikováno v:
Contemporary Physics; Dec2021, Vol. 62 Issue 4, p236-237, 2p
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
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—
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
Autor:
Bernhard Mehlig
This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::608e9f743b27beda4dc8304cdaaf035b
https://doi.org/10.1017/9781108860604
https://doi.org/10.1017/9781108860604