arXiv subjects

Alessandro Petrini

Publications and source records attributed to Alessandro Petrini.

7 recordsLinked to original sources

arXiv2026

Curvature-driven wall accumulation in chiral active particles

We study a dilute system of non-motile chiral active particles confined in geometries ranging from straight channels to circular enclosures. Activity is introduced through chiral particle-wall interactions, modeled as tangential wall forces that generate the edge currents characteristic of chiral active matter. Remarkably, although the particles lack self-propulsion, these boundary currents induce density inhomogeneities. We show that boundary curvature drives a wall accumulation phenomenon: particles remain uniformly distributed in straight channels but accumulate near the boundaries of circular confinements. Numerical simulations and a hydrodynamic theory for the density and momentum fields consistently capture this curvature-induced wall-accumulation. These results identify boundary curvature as a fundamental control parameter for chiral edge transport and confinement-induced organization, with potential experimental relevance to spinning colloids and granular spinners.

cond-mat.soft↗

arXiv2026

Sparkling bubbles in chiral active fluids

We study an inertial chiral active fluid, formed by repulsive particles that transfer angular momentum through odd interactions, i.e. transverse forces. Chirality induces an inhomogeneous phase, consisting of rotating bubbles, whose formation is favored at an optimal packing fraction. In this regime, we discover that bubbles may be dynamically unstable, breaking up and reforming in the steady state, thereby showing a spontaneous sparkling-like behavior reminiscent of supersaturated liquids. Bubbles and sparkling bubbles are predicted by a coarse-grained hydrodynamic theory, revealing the intrinsic non-linearity of these collective phenomena, and call for experimental verifications in granular spinners or spinning colloids.

cond-mat.soft↗

arXiv2026

Kinetic Theory of Chiral Active Disks: Odd Transport and Torque Density

Parity-odd transport is a central signature of chiral fluids, yet analytical predictions are sparse. Here, we introduce a minimal two-dimensional hard-disk gas in which chirality arises solely from a collision-induced transverse impulse. Motivated by granular spinners, collisions are dissipative and inject orbital angular momentum through a fixed tangential ``kick'' at contact. Starting from a Boltzmann-Enskog description, we derive nonlinear hydrodynamic equations for density, momentum, and temperature, and show that chirality generates an antisymmetric homogeneous stress corresponding to a nonzero torque density. In the dilute limit, a Chapman-Enskog expansion yields analytical predictions for transport coefficients, including odd viscosity, odd thermal conductivity, and odd self-diffusivity, in good agreement with numerical simulations. This minimal kinetic model can serve as a foundation for systematic coarse-graining of chiral fluids and as a tractable benchmark for gaining insight into odd transport across a broader class of chiral systems.

cond-mat.stat-mech↗

arXiv2026

Emergent hydrodynamics of chiral active fluids: vortices, bubbles and odd diffusion

Starting from a microscopic multiparticle Langevin equation, we systematically derive a hydrodynamic description in terms of density and momentum fields for chiral active particles interacting via standard repulsive and nonlocal odd forces. These odd interactions are reciprocal but non-conservative: they are non-potential forces, as they act perpendicular to the vector joining any pair of particles. As a result, the torques that two particles exert on one another are non-reciprocal. The ensuing macroscopic continuum description consists of a continuity equation for the density and a generalized compressible Navier-Stokes equation for the fluid velocity. The latter includes a chirality-induced torque density term and an odd viscosity contribution. Our theory predicts the emergence of odd diffusivity, edge currents, and an inhomogeneous phase - characterized by bubble-like structures - recently observed in simulations. Specifically, the theory exhibits a linear instability arising from the interplay between odd viscosity and torque density, and admits steady-state inhomogeneous solutions featuring bubbles and vortices, in agreement with numerical simulations. Our findings can be tested experimentally in systems of granular spinners or rotating microorganisms suspended in a fluid.

cond-mat.soft↗

arXiv2025

Modeling chiral active particles: from circular motion to odd interactions

In this paper, we discuss microscopic models for chiral active particles, i.e., rotating active units that exhibit circular or spinning motion. While non-chiral active particles are typically governed by self-propulsion and conservative interactions, the rotating motion of chiral particles generates additional non-conservative forces that cannot be derived from a potential. These manifest as effective transverse forces, acting perpendicular to the line connecting the centres of two interacting particles, and are referred to as odd interactions, because they break the mirror symmetry of the system. Here, we demonstrate that odd interactions arise from a limiting case of a well-established model describing spinning granular objects. In addition, we show that these models for chiral active objects give rise to a novel collective phenomenon that emerges uniquely from transverse forces and, hence, chirality. Specifically, the system undergoes a transition from a homogeneous phase to an inhomogeneous one characterised by regions depleted of particles, referred to as bubbles. This collective behaviour, termed BIO (bubbles induced by odd interactions), is a general emergent phenomenon arising from chirality and odd interactions. In this work, we review theoretical approaches to this problem, including a scaling argument and predictions for spatial velocity correlations that account for the BIO phase. Finally, we outline perspectives and open challenges concerning this collective phenomenon.

cond-mat.soft↗

arXiv2025

Analyzing partially-polarized light with a photonic deep random neural network

Optical neural networks are emerging as a powerful and versatile tool for processing optical signals directly in the optical domain with superior speed, integrability, and functionality. Their application to optical polarization enables neuromorphic polarization sensors, but their operation is limited to fully-polarized light. Here, we demonstrate single-shot analysis of partially-polarized beams with a photonic random neural network (PRNN). The PRNN is composed of a series of optical layers implemented by a stack of scattering media and a few trainable digital nodes. The setup infers the degree-of-polarization and the Stokes parameters of the polarized component with precision comparable to off-the-shelf polarimeters. The use of several optical layers allows to enhance the accuracy, reduce the sensor size, and minimize digital costs, demonstrating the advantage of a deep optical encoder for processing polarization information. Our results point out photonic neural networks as fast, compact, broadband, low-cost polarimeters that are widely applicable from sensing to imaging.

physics.optics↗

arXiv2021

Compact representations of convolutional neural networks via weight pruning and quantization

The state-of-the-art performance for several real-world problems is currently reached by convolutional neural networks (CNN). Such learning models exploit recent results in the field of deep learning, typically leading to highly performing, yet very large neural networks with (at least) millions of parameters. As a result, the deployment of such models is not possible when only small amounts of RAM are available, or in general within resource-limited platforms, and strategies to compress CNNs became thus of paramount importance. In this paper we propose a novel lossless storage format for CNNs based on source coding and leveraging both weight pruning and quantization. We theoretically derive the space upper bounds for the proposed structures, showing their relationship with both sparsity and quantization levels of the weight matrices. Both compression rates and excution times have been tested against reference methods for matrix compression, and an empirical evaluation of state-of-the-art quantization schemes based on weight sharing is also discussed, to assess their impact on the performance when applied to both convolutional and fully connected layers. On four benchmarks for classification and regression problems and comparing to the baseline pre-trained uncompressed network, we achieved a reduction of space occupancy up to 0.6% on fully connected layers and 5.44% on the whole network, while performing at least as competitive as the baseline.

cs.LG↗