Results 21 to 30 of about 240 (137)
Equivariant vector bundles on quantum homogeneous spaces [PDF]
Mathematical Research Letters, 2008 The notion of quantum group equivariant homogeneous vector bundles on quantum homogeneous spaces is introduced. The category of such quantum vector bundles is shown to be exact, and its Grothendieck group is determined. It is also shown that the algebras of functions on quantum homogeneous spaces are noetherian. \thanks{{\em Keywords}.
Zhang, Guanglian, Zhang, Ruibin
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Equivariant vector bundles on group completions [PDF]
Journal für die reine und angewandte Mathematik (Crelles Journal), 2005 Final version. Improved Exposition.
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Stably trivial equivariant algebraic vector bundles [PDF]
Journal of the American Mathematical Society, 1995 In this article, the authors describe a method for constructing non-trivial equivariant algebraic vector bundles over representation spaces for reductive groups (the equivariant Serre problem). The idea is to define invariants, which distinguish bundles, which are explicitly given as subbundles of trivial vector bundles. H. Kraft and G.
Masuda, Mikiya, Petrie, Ted
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EQUIVARIANT EXPONENTIALLY NASH VECTOR BUNDLES
Taiwanese Journal of Mathematics, 1997 Exponentially Nash manifolds (or analytic manifolds definable in \(R_{\exp}= \{\mathbb{R},
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Classification of equivariant real vector bundles over a circle
Kyoto Journal of Mathematics, 2002 16 pages, AMS-LaTeX v1 ...
Cho, JH +3 more
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Homogeneous vector bundles and G-equivariant convolutional neural networks
Sampling Theory, Signal Processing, and Data Analysis, 2022 AbstractG-equivariant convolutional neural networks (GCNNs) is a geometric deep learning model for data defined on a homogeneous G-space $$\mathcal {M}$$ M . GCNNs are designed to respect the global symmetry in $$\mathcal {M}$$ M , thereby facilitating learning.
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Advanced Intelligent Discovery, EarlyView.We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob +2 more
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Equivariant Vector Bundles Over Quantum Projective Spaces [PDF]
Theoretical and Mathematical Physics, 2019 This is a revision of the 2nd part of arXiv:1709.08394, which has been split in two papers.
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Fibrados vectoriales equivariantes en espacios homogéneos compactos
Revista Integración, 2011 Se desarrollan los resultados algebraicos concernientes a los fi- brados vectoriales equivariantes sobre algunos espacios compactos, usando construcciones y argumentos globales. El enfoque que se le da es un tanto algebraico.
Fernando Ricardo González Díaz
doaj
A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons
Advanced Intelligent Discovery, EarlyView.We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam +5 more
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