The European Physical Journal Special Topics最新文献

In view of the lack of an explicit expression for the stationary response probability density of generalized nonlinear systems subjected to combined harmonic and Gaussian white noise excitations, a data-driven method is proposed in this paper. The approach involves constructing an expansion expression with undetermined coefficients and determining these coefficients through solving an optimal problem. Initially, leveraging the principle of maximum entropy and the Buckingham Pi theorem, the stationary probability density of the system energy is represented in exponential form. The power of the exponential function is then expanded into a combination of basis functions of Pi groups with undetermined coefficients, constructed from system and excitation parameters, along with the system energy. Subsequently, the coefficients are determined by solving an optimal problem aimed at minimizing the residual between the expression and histogram-based estimations of the probability density of the system energy from random state data. Additionally, a sparse optimization algorithm is employed and then the explicit expression for the probability density of the system energy can be identified including system and excitation parameters. Two typical nonlinear systems, namely the Duffing oscillator and Coulomb friction system, are given to illustrate the effectiveness and accuracy of the proposed data-driven method. The identified expressions cover both resonant and non-resonant cases, showcasing the versatility and applicability of the proposed approach. Furthermore, the extensionality of the expression is thoroughly examined and discussed.

The prime objective of this work is to analyze a Caputo fractional order HIV model where infection occurs through various modes. A detailed investigation on existence, uniqueness, boundedness as well as non-negativity of solutions have been performed at first. Stability analysis of the fixed points has been carried out in the next section. Sensitivity analysis of the threshold parameter has also been performed. Finally, a series of numerical simulations are used to confirm theoretical findings.

A data-driven system identification method based on the Koopman operator with sparse optimization is proposed. Koopman theory provides insights into transforming nonlinear systems into a higher-dimensional measurement function space dominated by a linear Koopman operator, which enhances system identification. The effective data-driven approach of the eigenfunctions of the Koopman operator is becoming a challenging topic. Compared with the state-of-the-art methods, this paper introduces a sparse basis selection algorithm to enhance the implementation of the compressed Koopman operator. The validity and accuracy of the method are demonstrated in a 2D Duffing system and a 3D chaotic Lorenz system. The method is also robust to noisy data.

Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural networks is one of the well-established areas. In this review, we discuss different tagging frameworks available to tag boosted objects, especially boosted Higgs boson and top quark, at the Large Hadron Collider (LHC). Our aim is to study the interplay of traditional jet substructure-based methods with the state-of-the-art machine learning ones. In this methodology, we would gain some interpretability of those machine learning methods, and which in turn helps to propose hybrid taggers relevant for tagging of those boosted objects belonging to both Standard Model (SM) and physics beyond the SM.

The Poisson–Boltzmann equation characterizes the internal electric potential in electroosmotic and magnetohydrodynamic (MHD) processes, under the assumptions of thermodynamic equilibrium and negligible fluid flow effects. However, for significant convective ion transport, the Nernst–Planck equation is requisite. This study develops predictive models for electroosmotic and MHD flows between squeezing plates, where convective ion transport is minimal. The partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) using similarity transformations and solved analytically via the homotopy analysis method (HAM). The HAM results, validated against the numerical solver BVP4c, exhibit strong concordance. Various physical effects are elucidated through graphical and tabular representations, revealing that squeezing the plates reduces electroosmotic flow profiles while increasing the magnetic Reynolds number in both homogeneous and heterogeneous reactions.

This article analyzes a fractional-order SEIR infection epidemic model, including time delays and vaccination strategies. Four differential equations describe the infection dynamics with non-integer derivative orders, which account for memory effects and non-local interactions in disease spread. The paper first establishes the existence and uniqueness of the solution and presents equilibrium points based on the basic reproduction number, (R_{0}). Using the Lyapunov direct method, the global stability of each equilibrium is proven to depend primarily on (R_{0}). Theoretical findings are validated through numerical simulations, exploring the impact of vaccination and fractional derivatives on the epidemic dynamics.

The flow of micropolar nanofluids affected by the phenomena of MHD and thermal radiation through a regular and irregular vertical plate is examined in the present study. The primary goal of the research is to investigate the influence of boundary irregularities on the flow and heat transfer phenomena while considering the phenomena of Brownian motion, MHD, thermal radiation, and thermophoresis. The similarity transformation is applied to the flow’s governing momentum and energy nonlinear coupled partial differential equations, converting them into linear coupled ordinary differential equations. The coupled ODEs are numerically solved using a quasilinearization technique and finite difference schemes. The physical effects of Brownian motion, MHD, heat radiation, and thermophoresis are explored through data and illustrations. The importance of MHD in controlling flow and boundary layer thickness is demonstrated in particular by showing the impact of crucial physical parameters such as the buoyancy force and the Brownian motion parameter. Also, more significant effects on velocity, temperature profiles, and heat transfer rate are observed in irregular boundaries than in regular boundaries.

The primary objective of the present study is to explore the novelty in the analysis of entropy generation introduced in the oscillatory flow of Jeffrey fluid through an asymmetric tapered wavy channel subjected to Lorentz force and thermal radiation. It has diverse applications in a range of disciplines: automotive elastomers in the material selection process, soft tissue mechanics modeling in biomechanics, extrusion and injection molding optimization in polymer processing, rheological test design and data interpretation in rheology. The unique nature of the tapered wavy shape in the channel and its influence on the velocity profile of MHD oscillatory Jeffrey fluid flow represents a novel element that has not been extensively explored previously. The governing equations are transformed into a system of nonlinear differential equations using non-similarity transformations. The transient system of dimensionless partial differential equations (PDEs) is solved using an implicit finite difference numerical scheme called the Crank-Nicolson method. Incorporating relevant parameters, the exact behavior of the flow with respect to velocity, temperature and volumetric rate of entropy generation is graphically depicted. The increase in entropy generation with a higher Brinkman number implies that the enhanced influence of the porous structure leads to greater irreversibility in the Jeffrey fluid flow. A comparative study is carried out to characterize Newtonian and Jeffrey fluid behavior by analyzing the velocity and temperature profiles. Finally, the findings of the current study have been compared to those of earlier studies. The comparison is seen to bear a good agreement with the existing literature.

In recent years, deep learning models have emerged as a popular numerical method for solving nonlinear partial differential equations (PDEs). In this paper, the improved physical informed memory networks (PIMNs) are introduced, which are constructed upon domain decomposition. In the improved PIMNs, the solution domain is decomposed into non-overlapping rectangular sub-domains. The loss for each sub-domain is computed independently, and an adaptive function is employed to dynamically adjust the coefficients of the loss terms. This approach significantly improves the PIMNs’ ability to train regions with high loss values. To validate the superiority of the improved PIMNs, the nonlinear Schrödinger equation, the KdV-Burgers equation, and the KdV-Burgers-Kuramoto equation are solved via both the original and the improved PIMNs. The experimental results clearly show that the improved PIMNs provide a significant enhancement in terms of solution accuracy compared to the original PIMNs.

This study explores the propagation of S-waves in a layered medium characterized by anisotropy, non-homogeneity, incompressibility, pre-existing stress, and couple stress effects. The frequency equation that determines the phase velocity of shear waves, incorporating linear inhomogeneities, has been derived. Using numerical computations in MATLAB, the effects of varying initial stress, density, anisotropy, rigidity, and couple stress parameters on wave propagation are analysed. The findings are presented graphically, providing insights into how these factors influence phase velocity and improving our understanding of wave behaviour in complex media. These findings are not only instrumental in advancing fundamental understanding but also hold practical significance across diverse applications. For instance, in geotechnical engineering, this knowledge can inform the design of robust infrastructure capable of withstanding seismic events. In material science and manufacturing, they facilitate the development of resilient materials and structures.