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pro vyhledávání: '"Torres, Fabricio Arend"'
Autor:
Torres, Fabricio Arend, Negri, Marcello Massimo, Nagy-Huber, Monika, Samarin, Maxim, Roth, Volker
Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural networks. Although PINNs are generally viewed as mesh-free, current ap
Externí odkaz:
http://arxiv.org/abs/2206.01545
Considering smooth mappings from input vectors to continuous targets, our goal is to characterise subspaces of the input domain, which are invariant under such mappings. Thus, we want to characterise manifolds implicitly defined by level sets. Specif
Externí odkaz:
http://arxiv.org/abs/2204.07009
Autor:
Keller, Sebastian Mathias, Samarin, Maxim, Torres, Fabricio Arend, Wieser, Mario, Roth, Volker
Archetypes are typical population representatives in an extremal sense, where typicality is understood as the most extreme manifestation of a trait or feature. In linear feature space, archetypes approximate the data convex hull allowing all data poi
Externí odkaz:
http://arxiv.org/abs/2002.00815
Akademický článek
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