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pro vyhledávání: '"Rad M"'
Equaixed dendrites are frequently encountered in solidification. They typically form in large numbers, which makes their detection, localization, and tracking practically impossible for a human eye. In this paper, we show how recent progress in the f
Externí odkaz:
http://arxiv.org/abs/2207.07428
In this paper, we prove that any surface corresponding to linear second-order ODEs as a submanifold is minimal in all classes of third-order ODEs $y'''=f(x, y, p, q)$ as a Riemannian manifold where $y'=p$ and $y''=q$, if and only if $q_{yy}=0$. Moreo
Externí odkaz:
http://arxiv.org/abs/2204.04926
Grain Boundaries (GB) whose energy is larger than twice the energy of the solid/liquid interface exhibit the premelting phenomenon, for which an atomically thin liquid layer develops at temperatures slightly below the bulk melting temperature. Premel
Externí odkaz:
http://arxiv.org/abs/2111.08098
Publikováno v:
Acta Veterinaria Scandinavica, Vol 44, Iss Suppl 1, p P115 (2003)
Externí odkaz:
https://doaj.org/article/96ae38aaf1cf4c85b4e1f9046447462d
Publikováno v:
Acta Veterinaria Scandinavica, Vol 44, Iss Suppl 1, p P98 (2003)
Externí odkaz:
https://doaj.org/article/dcf1cde5ba3345ee926fd9f6813dcb0b
Deep neural networks are transforming fields ranging from computer vision to computational medicine, and we recently extended their application to the field of phase-change heat transfer by introducing theory-trained neural networks (TTNs) for a soli
Externí odkaz:
http://arxiv.org/abs/2102.04890
Deep neural networks are machine learning tools that are transforming fields ranging from speech recognition to computational medicine. In this study, we extend their application to the field of alloy solidification modeling. To that end, and for the
Externí odkaz:
http://arxiv.org/abs/1912.09800
Publikováno v:
In Materialia June 2023 29