Results 21 to 30 of about 305,187 (339)
Coarse Graining Spin Foam Quantum Gravity—A Review
Frontiers in Physics, 2020 In quantum gravity, we envision renormalization as the key tool for bridging the gap between microscopic models and observable scales. For spin foam quantum gravity, which is defined on a discretization akin to lattice gauge theories, the goal is to ...
Sebastian Steinhaus
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Renormalization aspects of N=1 Super Yang-Mills theory in the Wess-Zumino gauge [PDF]
, 2014 The renormalization of N=1 Super Yang-Mills theory is analysed in the Wess-Zumino gauge, employing the Landau condition. An all orders proof of the renormalizability of the theory is given by means of the Algebraic Renormalization procedure.
Capri, M. A. L. +6 more
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Multigrid renormalization [PDF]
Journal of Computational Physics, 2018 We combine the multigrid (MG) method with state-of-the-art concepts from the variational formulation of the numerical renormalization group. The resulting MG renormalization (MGR) method is a natural generalization of the MG method for solving partial differential equations.
Lubasch, M, Moinier, P, Jaksch, D
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A measure of identifying influential community based on the state of critical functionality
Mathematical Biosciences and Engineering, 2020 As an open issue, the measure of community influential is no uniform standard, how to measure the influence of community has attracted extensive attention. This paper proposed a quantitative measure to identify the influence of community.
Mingli Lei , Daijun Wei
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Renormalization group and nonequilibrium action in stochastic field theory [PDF]
, 2002 We investigate the renormalization group approach to nonequilibrium field theory. We show that it is possible to derive nontrivial renormalization group flow from iterative coarse graining of a closed-time-path action.
A. Berera +56 more
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Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers [PDF]
International Conference on Artificial Neural Networks, 2016 Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e.g. an image, down to relevance scores for the single input dimensions of the sample such as subpixels of an image.
Alexander Binder +4 more
semanticscholar +1 more source
Renormalized functional renormalization group [PDF]
Physics Letters B, 2018 5+1 ...
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Entanglement Renormalization [PDF]
Physical Review Letters, 2007 4 pages, 4 figures, updated ...
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, 2021 We examine the perturbative calculation of the inclusive semi-leptonic decay rate $\Gamma$ for the $b$-quark, using mass-independent renormalization.
Chishtie, F. A. +2 more
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Dynamic and Renormalization-Group Extensions of the Landau Theory of Critical Phenomena
Entropy, 2020 We place the Landau theory of critical phenomena into the larger context of multiscale thermodynamics. The thermodynamic potentials, with which the Landau theory begins, arise as Lyapunov like functions in the investigation of the relations among ...
Miroslav Grmela +2 more
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