Particle based gPC methods for mean-field models of swarming with uncertainty

In this work we focus on the construction of numerical schemes for the approximation of stochastic mean--field equations which preserve the nonnegativity of the solution. The method here developed makes use of a mean-field Monte Carlo method in the physical variables combined with a generalized Polynomial Chaos (gPC) expansion in the random space. In contrast to a direct application of stochastic-Galerkin methods, which are highly accurate but lead to the loss of positivity, the proposed schemes are capable to achieve high accuracy in the random space without loosing nonnegativity of the solution. Several applications of the schemes to mean-field models of collective behavior are reported.Comment: Communications in Computational Physics, to appea

Análises do sêmen de suínos em centrais de inseminação artificial e detecção de circovírus suíno tipo 2 (PCV2).

bitstream/item/58128/1/CUsersPiazzonDocuments438.pd

A nonequilibrium renormalization group approach to turbulent reheating

We use nonequilibrium renormalization group (RG) techniques to analyze the thermalization process in quantum field theory, and by extension reheating after inflation. Even if at a high scale $\Lambda$ the theory is described by a non-dissipative $\lambda\phi^{4}$ theory, the RG running induces nontrivial noise and dissipation. For long wavelength, slowly varying field configurations, the noise and dissipation are white and ohmic, respectively. The theory will then tend to thermalize to an effective temperature given by the fluctuation-dissipation theorem.Comment: 8 pages, 2 figures; to appear in J. Phys. A; more detailed account of the calculation of the noise and dissipation kernel

Kinematics of z≥6z\geq 6 galaxies from [CII] line emission

We study the kinematical properties of galaxies in the Epoch of Reionization via the [CII] 158$\mu$m line emission. The line profile provides information on the kinematics as well as structural properties such as the presence of a disk and satellites. To understand how these properties are encoded in the line profile, first we develop analytical models from which we identify disk inclination and gas turbulent motions as the key parameters affecting the line profile. To gain further insights, we use "Althaea", a highly-resolved ($30\, \rm pc$) simulated prototypical Lyman Break Galaxy, in the redshift range $z = 6-7$, when the galaxy is in a very active assembling phase. Based on morphology, we select three main dynamical stages: I) Merger , II) Spiral Disk, and III) Disturbed Disk. We identify spectral signatures of merger events, spiral arms, and extra-planar flows in I), II), and III), respectively. We derive a generalised dynamical mass vs. [CII]-line FWHM relation. If precise information on the galaxy inclination is (not) available, the returned mass estimate is accurate within a factor $2$ ($4$). A Tully-Fisher relation is found for the observed high-$z$ galaxies, i.e. $L_{\rm[CII]}\propto (FWHM)^{1.80\pm 0.35}$ for which we provide a simple, physically-based interpretation. Finally, we perform mock ALMA simulations to check the detectability of [CII]. When seen face-on, Althaea is always detected at $> 5\sigma$; in the edge-on case it remains undetected because the larger intrinsic FWHM pushes the line peak flux below detection limit. This suggests that some of the reported non-detections might be due to inclination effects.Comment: 14 pages, 12 figures, accepted for publication in MNRA

Control with uncertain data of socially structured compartmental epidemic models

The adoption of containment measures to reduce the amplitude of the epidemic peak is a key aspect in tackling the rapid spread of an epidemic. Classical compartmental models must be modified and studied to correctly describe the effects of forced external actions to reduce the impact of the disease. In addition, data are often incomplete and heterogeneous, so a high degree of uncertainty must naturally be incorporated into the models. In this work we address both these aspects, through an optimal control formulation of the epidemiological model in presence of uncertain data. After the introduction of the optimal control problem, we formulate an instantaneous approximation of the control that allows us to derive new feedback controlled compartmental models capable of describing the epidemic peak reduction. The need for long-term interventions shows that alternative actions based on the social structure of the system can be as effective as the more expensive global strategy. The importance of the timing and intensity of interventions is particularly relevant in the case of uncertain parameters on the actual number of infected people. Simulations related to data from the recent COVID-19 outbreak in Italy are presented and discussed