The non-equilibrium structural and dynamical properties of a flexible polymer tethered to a reflecting wall and subject to oscillatory linear flow are studied by numerical simulations. Polymer is confined in two dimensions and is modeled as a bead-spring chain immersed in a fluid described by the Brownian multiparticle collision dynamics. At high strain, the polymer is stretched along the flow direction following the applied flow, then recoils at flow inversion before flipping and elongate again. When strain is reduced, it may happen that the chain recoils without flipping when the applied shear changes sign. Conformations are analyzed and compared to stiff polymers revealing more compact patterns at low strains and less stretched configurations at high strain. The dynamics is investigated by looking at the center-of-mass motion which shows a frequency doubling along the direction normal to the external flow. The center-of-mass correlation function is characterized by smaller amplitudes when reducing bending rigidity.

The properties of semiflexible active ring polymers are studied by numerical simulations. The two-dimensionally confined polymer is modeled as a closed bead-spring chain subject to tangential active forces, and the interaction with the fluid is described by the Brownian multiparticle collision dynamics approach. Both phantom polymers and chains with excluded- volume interactions are considered. The size and shape strongly depend on the relative ratio of the persistence length to the ring length as well as on the active force. The long-time dynamics is characterized by a rotational motion whose frequency increases with the active force.

In microfluidic systems, droplets undergo intricate deformations as they traverse flow-focusing junctions, posing a challenging task for accurate measurement, especially during short transit times. This study investigates the physical behavior of droplets within dense emulsions in diverse microchannel geometries, specifically focusing on the impact of varying opening angles within the primary channel and injection rates of fluid components. Employing a sophisticated droplet tracking tool based on deep-learning techniques, we analyze multiple frames from flow-focusing experiments to quantitatively characterize droplet deformation in terms of ratio between maximum width and height and propensity to form liquid with hexagonal spatial arrangement. Our findings reveal the existence of an optimal opening angle where shape deformations are minimal and hexagonal arrangement is maximal. Variations of fluid injection rates are also found to affect size and packing fraction of the emulsion in the exit channel. This paper offers insight into deformations, size, and structure of fluid emulsions relative to microchannel geometry and other flow-related parameters captured through machine learning, with potential implications for the design of microchips utilized in cellular transport and tissue engineering applications.

This work addresses the Knapsack Problem with Forfeit Sets, a recently introduced variant of the 0/1 Knapsack Problem considering subsets of items associated with contrasting choices. Some penalty costs need to be paid whenever the number of items in the solution belonging to a forfeit set exceeds a predefined allowance threshold. We propose an effective metaheuristic to solve the problem, based on the Biased Random-Key Genetic Algorithm paradigm. An appropriately designed decoder function assigns a feasible solution to each chromosome, and improves it using some additional heuristic procedures. We show experimentally that the algorithm outperforms significantly a previously introduced metaheuristic for the problem.

Il rapporto analizza i principi che regolano il Programma HORIZON EUROPE e le modalità di rendicontazione dei costi sostenuti per la realizzazione delle attività progettuali, soffermandosi sulle caratteristiche dello strumento finanziario, sulla tipologia dei costi e sul sistema di controlli attraverso il quale la Commissione Europea vigila sul rispetto delle norme gli obblighi previsti dalla convenzione di sovvenzione.

We analyze the dynamics of the Sun-Earth-Moon system in the context of a particular class of theories of gravity where curvature and matter are nonminimally coupled (NMC). These theories can potentially violate the Equivalence Principle as they give origin to a fifth force and a extra non-Newtonian force that may imply that Earth and Moon fall differently towards the Sun. We show, through a detailed analysis, that consistency with the bound on Weak Equivalence Principle arising from 48 years of Lunar Laser Ranging data, for a range of parameters of the NMC gravity theory, can be achieved via the implementation of a suitable screening mechanism.

In questo report si affrontano gli aspetti tecnici e di sviluppo che hanno portato all’attuale sito web di istituto, all’interno di un approfondimento generale relativo alla comunicazione istituzionale e al posizionamento dell’IAC nel contesto della comunicazione della matematica. Sono, inoltre, discusse le modalità di misurazione delle performance (Analytics) del sito, con una panoramica sulle necessarie attività di miglioramento in termini di Search Engine Optimization (SEO).

The volume collects the long abstracts of the 79 contributions presented during the fourth edition of the “Young Applied Mathematicians Conference” (YAMC, www.yamc.it). Organized in Rome under the sponsorship of the Institute for Applied Mathematics (IAC) of the CNR and the Department of Mathematics at Sapienza, University of Rome, the conference took place from September 16 to 20, 2024, and brought together primarily young researchers (students, PhD candidates, post-docs, etc.) from 37 universities and research centers across 8 countries. This volume is intended to promote the communication of the research presented in the field of applied mathematics, with a primary focus on numerical analysis, artificial intelligence, statistics, and mathematical modeling. Il volume raccoglie i long abstracts dei 79 contributi presentati durante la quarta edizione del convegno "Young Applied Mathematicians Conference" (YAMC, www.yamc.it). Organizzato a Roma sotto il patrocinato dell'Istituto per le Applicazioni del Calcolo (IAC) del CNR e del dipartimento di Matematica di Sapienza, Università di Roma, il convegno si è svolto nelle giornate 16--20 settembre 2024 ed ha riunito principalmente giovani ricercatori (studenti, dottorandi, post-doc, ...) provenienti da 37 fra università e centri di ricerca di 8 nazioni. Il presente volume è indirizzato a favorire la comunicazione delle ricerche presentate nel panorama della matematica applicata, con principale attenzione in analisi numerica, intelligenza artificiale, statistica e modellistica matematica.

Personalized medicine strategies are gaining momentum nowadays, enabling the introduction of targeted treatments based on individual differences that can lead to greater therapeutic efficacy by reducing adverse effects. Despite its crucial role, studying the contribution of the immune system (IS) in this context is difficult because of the intricate interplay between host, pathogen, therapy, and other external stimuli. To address this problem, a multidisciplinary approach involving in silico models can be of great help. In this perspective, we will discuss the use of a well-established agent-based model of the immune response, C-ImmSim, to study the relationship between long-lasting diseases and the combined effects of IS, drug therapies and exogenous factors such as physical activity and dietary habits.

A mixed approach with meta-modelling techniques and machine-learning algorithms is here applied to the minimization of the lap time of a Formula 1 race car. The fine tuning of the front wing is performed in order to optimize the car for each specific racetrack. This task is performed by a simplified model, which is trained by some high-fidelity fluid dynamic simulations and then extended to the complete design space. The resulting tool is reliable, fast and easy to use. The accuracy of the resulting speed profiles of the chosen car in comparison with available measurements is indicating the overall reliability of the procedure.

Numerical optimization of complex systems benefits from the technological development of computing platforms in the last twenty years. Unfortunately, this is still not enough, and a large computational time is still necessary when mathematical models that include richer (and therefore more realistic) physical models are adopted. In this paper we show how the combination of optimization and Artificial Intelligence (AI), in particular the Machine Learning algorithms, can help in strongly reducing the overall computational times, making possible the use of complex simulation systems within the optimization cycle. Original approaches are also proposed.

: We analyze the entropy production in run-and-tumble models. After presenting the general formalism in the framework of the Fokker-Planck equations in one space dimension, we derive some known exact results in simple physical situations (free run-and-tumble particles and harmonic confinement). We then extend the calculation to the case of anisotropic motion (different speeds and tumbling rates for right- and left-oriented particles), obtaining exact expressions of the entropy production rate. We conclude by discussing the general case of heterogeneous run-and-tumble motion described by space-dependent parameters and extending the analysis to the case of d-dimensional motions.

We present a mapping between a Schrödinger equation with a shifted nonlinear potential and the Navier–Stokes equation. Following a generalization of the Madelung transformations, we show that the inclusion of the Bohm quantum potential plus the laplacian of the phase field in the nonlinear term leads to continuity and momentum equations for a dissipative incompressible Navier–Stokes fluid. An alternative solution, built using a complex quantum diffusion, is also discussed. The present models may capture dissipative effects in quantum fluids, such as Bose–Einstein condensates, as well as facilitate the formulation of quantum algorithms for classical dissipative fluids.

In this paper, we provide explicit upper bounds on some distances between the (law of the) output of a random Gaussian neural network and (the law of) a random Gaussian vector. Our main results concern deep random Gaussian neural networks with a rather general activation function. The upper bounds show how the widths of the layers, the activation function, and other architecture parameters affect the Gaussian approximation of the output. Our techniques, relying on Stein's method and integration by parts formulas for the Gaussian law, yield estimates on distances that are indeed integral probability metrics and include the convex distance. This latter metric is defined by testing against indicator functions of measurable convex sets and so allows for accurate estimates of the probability that the output is localized in some region of the space, which is an aspect of a significant interest both from a practitioner's and a theorist's perspective. We illustrated our results by some numerical examples.

For a homogeneous incompressible 2D fluid confined within a bounded Lipschitz simply connected domain, homo- geneous Neumann pressure boundary conditions are equivalent to a constant boundary vorticity. We investigate the rigidity of such conditions.

A general class of hybrid models has been introduced recently, gathering the advantages of multiscale descriptions. Concerning biological applications, the particular coupled structure fits to collective cell migrations and pattern formation phenomena due to intercellular and chemotactic stimuli. In this context, cells are modeled as discrete entities and their dynamics are given by ODEs, while the chemical signal influencing the motion is considered as a continuous signal which solves a diffusive equation. From the analytical point of view, this class of models has been recently proved to have a mean-field limit in the Wasserstein distance towards a system given by the coupling of a Vlasov-type equation with the chemoattractant equation. Moreover, a pressureless nonlocal Euler-type system has been derived for these models, rigorously equivalent to the Vlasov one for monokinetic initial data. For applications, the monokinetic assumption is quite strong and far from a real experimental setting. The aim of this paper is to introduce a numerical approach to the hybrid coupled structure at the different scales, investigating the case of general initial data. Several scenarios will be presented, aiming at exploring the role of the different terms on the overall dynamics. Finally, the pressureless nonlocal Euler-type system is generalized by means of an additional pressure term.

One of the most crucial and lethal characteristics of solid tumors is represented by the increased ability of cancer cells to migrate and invade other organs during the so-called metastatic spread. This is allowed thanks to the production of matrix metalloproteinases (MMPs), enzymes capable of degrading a type of collagen abundant in the basal membrane separating the epithelial tissue from the connective one. In this work, we employ a synergistic experimental and mathematical modelling approach to explore the invasion process of tumor cells. A mathematical model composed of reaction-diffusion equations describing the evolution of the tumor cells density on a gelatin substrate, MMPs enzymes concentration and the degradation of the gelatin is proposed. This is completed with a calibration strategy. We perform a sensitivity analysis and explore a parameter estimation technique both on synthetic and experimental data in order to find the optimal parameters that describe the in vitro experiments. A comparison between numerical and experimental solutions ends the work.

The shape of liquid droplets in air plays an important role in the aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small drones. Their precise manipulation can yield optimal efficiency in such systems. It is desired to have a minimal representation of droplet shapes using as few parameters as possible to automate shape manipulation using self-learning algorithms, such as reinforcement learning. In this paper, we use a neural compression algorithm to represent, with only two parameters, elliptical and bullet-shaped droplets initially represented with 200 points (400 real numbers) at the droplet boundary. The mapping of many to two points is achieved in two stages. Initially, a Fourier series is formulated to approximate the contour of the droplet. Subsequently, the coefficients of this Fourier series are condensed to lower dimensions utilizing a neural network with a bottleneck architecture. Finally, 5000 synthetically generated droplet shapes were used to train the neural network. With a two-real-number representation, the recovered droplet shapes had excellent overlap with the original ones, with a mean square error of ∼10−3 . Hence, this method compresses the droplet contour to merely two numerical parameters via a fully reversible process, a crucial feature for rendering learning algorithms computationally tractable.

We introduce a two-step, fully reversible process designed to project the outer shape of a generic droplet onto a lower-dimensional space. The initial step involves representing the droplet's shape as a Fourier series. Subsequently, the Fourier coefficients are reduced to lower-dimensional vectors by using autoencoder models. The exploitation of the domain knowledge of the droplet shapes allows us to map generic droplet shapes to just two-dimensional (2D) space in contrast to previous direct methods involving autoencoders that could map it on minimum eight-dimensional (8D) space. This six-dimensional (6D) reduction in the dimensionality of the droplet's description opens new possibilities for applications, such as automated droplet generation via reinforcement learning, the analysis of droplet shape evolution dynamics, and the prediction of droplet breakup. Our findings underscore the benefits of incorporating domain knowledge into autoencoder models, highlighting the potential for increased accuracy in various other scientific disciplines.

We present a highly optimized thread-safe lattice Boltzmann model in which the non-equilibrium part of the distribution function is locally reconstructed via recursivity of Hermite polynomials. Such a procedure allows the explicit incorporation of non-equilibrium moments of the distribution up to the order supported by the lattice. Thus, the proposed approach increases accuracy and stability at low viscosities without compromising performance and amenability to parallelization with respect to standard lattice Boltzmann models. The high-order thread-safe lattice Boltzmann is tested on two types of turbulent flows, namely, the turbulent channel flow at R e τ = 180 and the axisymmetric turbulent jet at Re = 7000; it delivers results in excellent agreement with reference data [direct numerical simulations (DNS), theory, and experiments] and (a) achieves peak performance [ ∼ 5 × 10 12 floating point operations (FLOP) per second and an arithmetic intensity of ∼ 7 FLOP / byte on a single graphic processing unit] by significantly reducing the memory footprint, (b) retains the algorithmic simplicity of standard lattice Boltzmann computing, and (c) allows to perform stable simulations at vanishingly low viscosities. Our findings open attractive prospects for high-performance simulations of realistic turbulent flows on GPU-based architectures. Such expectations are confirmed by excellent agreement among lattice Boltzmann, experimental, and DNS reference data.