S
Salvatore Capozziello
Researcher at University of Naples Federico II
Publications - 994
Citations - 47545
Salvatore Capozziello is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: General relativity & Dark energy. The author has an hindex of 97, co-authored 916 publications receiving 39364 citations. Previous affiliations of Salvatore Capozziello include Tomsk State University of Control Systems and Radio-electronics & Istituto Nazionale di Fisica Nucleare.
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Constraining Generalized Non-local Cosmology from Noether Symmetries
TL;DR: In this article, a generalized non-local theory of gravity is studied, which can become either the curvature nonlocal or tele-parallel nonlocal theory, using the Noether Symmetry Approach.
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Gravitational Waves in $F(R)$ Gravity: Scalar Waves and the Chameleon Mechanism
Taishi Katsuragawa,Tomohiro Nakamura,Taishi Ikeda,Salvatore Capozziello,Salvatore Capozziello +4 more
TL;DR: In this paper, the authors discuss the scalar mode of gravitational waves emerging in the context of $F(R)$ gravity by taking into account the chameleon mechanism.
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Scalar–tensor cosmology with R−1 curvature correction by Noether symmetry
TL;DR: In this article, the authors discuss scalar-tensor cosmology with an extra R − 1 correction by the Noether symmetry approach, which selects the forms of the coupling ω ( φ ), of the potential V (φ ) and allows to obtain physically interesting exact cosmological solutions.
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Deriving the mass of particles from Extended Theories of Gravity in LHC era
TL;DR: In this paper, the authors derive a geometrical approach to produce the mass of particles that could be suitably tested at the LHC, starting from a 5D unification scheme, and show that all the known interactions could be plausibly deduced as an induced symmetry breaking of the non-unitary GL(4)-group of diffeomorphisms.
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A deep learning approach to cosmological dark energy models
TL;DR: In this article, the authors proposed a novel deep learning tool in order to study the evolution of dark energy models by combining two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Network (BNN).