Zobrazeno 1 - 10
of 1 278
pro vyhledávání: '"A, Carlevaro"'
We discuss an evolutionary dark energy model, based on the presence of non-equilibrium effects on the dark energy constituents, which are described via a bulk viscosity contribution. We implement the proposed dynamics by the analysis of the 40-bins T
Externí odkaz:
http://arxiv.org/abs/2411.07060
A reduced 1D model describing the non-linear hybrid LIGKA/HAGIS simulations was developed and successfully tested in [Carlevaro et al. PPCF 64, 035010 (2022)] addressing the ITER 15MA baseline scenario. In this paper, we introduce a detailed phase-sp
Externí odkaz:
http://arxiv.org/abs/2409.12644
We analyze the transport properties of the two-dimensional electrostatic turbulence characterizing the edge of a Tokamak device from the study of test particles motion (passive fluid tracers) following the EXB drift. We perform statistical tests on t
Externí odkaz:
http://arxiv.org/abs/2407.18634
Publikováno v:
Entropy 26, 662 (2024)
We consider a dynamic scenario for characterizing the late Universe evolution, aiming to mitigate the Hubble tension. Specifically, we consider a metric $f(R)$ gravity in the Jordan frame which is implemented to the dynamics of a flat isotropic Unive
Externí odkaz:
http://arxiv.org/abs/2407.12409
We analyze a model for Dark Energy - Dark Matter interaction, based on a decaying process of the former into the latter. The dynamical equations are constructed following a kinetic formulation, which separates the interacting fluctuations from an equ
Externí odkaz:
http://arxiv.org/abs/2404.15977
Autor:
Montani, Giovanni, Carlevaro, Nakia
Publikováno v:
Symmetry 16, 306 (2024)
We present a new perspective on the symmetries that govern the formation of large-scale structures across the Universe, particularly focusing on the transition from the seeds of galaxy clusters to the seeds of galaxies themselves. We address two main
Externí odkaz:
http://arxiv.org/abs/2403.18759
Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classif
Externí odkaz:
http://arxiv.org/abs/2403.10368
Publikováno v:
Phys. Dark Univ. 44, 101486 (2024)
We construct a theoretical framework to interpret the Hubble tension by means of a slow-rolling dynamics of a self-interacting scalar field. In particular, we split the Friedmann equation in order to construct a system for the three unknowns, corresp
Externí odkaz:
http://arxiv.org/abs/2311.04822
Publikováno v:
Symmetry 15, 1745 (2023)
The plasma edge of a tokamak configuration is characterized by turbulent dynamics leading to enhanced transport. We construct a simplified 3D Hasegawa--Wakatani model reducing to a single partial differential equation for the turbulent electric poten
Externí odkaz:
http://arxiv.org/abs/2309.06078
Supervised classification recognizes patterns in the data to separate classes of behaviours. Canonical solutions contain misclassification errors that are intrinsic to the numerical approximating nature of machine learning. The data analyst may minim
Externí odkaz:
http://arxiv.org/abs/2309.04627