An ultraintense, isolated circularly polarized (CP) attosecond x-ray pulse is often required for many application of pump-probe techniques. A quantum electrodynamics (QED) effect dominated coherent synchrotron emission (CSE) regime is proposed for the generation of an ultraintense isolated, nearly CP attosecond x-ray pulse. Due to the QED effects, the density of the target plasma increases due to Breit-Wheeler pair generation and the radiation reaction effects of gamma photons through the nonlinear Compton effect. As a result, the transparent target becomes an opaque target. Accordingly, the relativistic electron sheet (RES) can be accelerated toward a reflected direction only once. After that, the RES was pushed tempestuously forward continuously under the Lorentz force of the driving laser pulse. Therefore, an isolated, nearly CP attosecond x-ray pulse can be obtained in the reflected direction without filtering. The intensity of the nearly CP attosecond x-ray pulse can reach $2.48\times {10}^{24}\mathrm{W}/{\mathrm{cm}}^2$ with ellipticity of $0.83$, and the corresponding power can reach up to $\sim 100\mathrm{PW}$, which opens the door for nonlinear attosecond studies.

Properties of classical molecular systems can be calculated with integral equation theories based on the Ornstein-Zernike equation and a complementary closure relation. One such closure relation is the hypernetted chain (HNC) approximation, which neglects the so-called bridge function. We present a way to use machine learning to train a deep operator network to predict the bridge function, based on the radial distribution function as input. Bridge functions for the Lennard-Jones fluid are calculated from Monte Carlo simulations in a wide range of densities and temperatures. These results are used to train the deep operator network. This network is employed to improve the HNC closure by the prediction for the bridge function, and the resulting set of equations is solved iteratively. For assessment, we compare the radial distribution function and the pressure, calculated by the viral expression, with Monte Carlo results and standard HNC. We demonstrate that incorporating the neural-networkbased bridge function in the closure relation leads to substantially improved predictions. The universality of our method is demonstrated by comparing results for the Mie fluid and the hard-sphere fluid, calculated with our model trained on the Lennard-Jones fluid, with exact Mie and hardsphere results, showing overall good agreement.

The mechanical behavior of disordered materials, such as dense suspensions, glasses, and granular materials, depends on their thermal and mechanical history. Under periodic driving, these materials can evolve into states that encode a memory of their annealing. Such memory effects have been observed experimentally in systems ranging from sheared non-Brownian suspensions to crumpled elastic sheets and in atomistic simulations. Here, we show that a quenched mesoscopic elastoplastic model of a sheared amorphous solid reproduces the phenomena of self-organization and memory formation under mechanical annealing. We analyze sample-to-sample fluctuations under readout protocols and demonstrate their connection to the irreversibility transition. Our model allows us to understand in detail the mechanical processes underlying memory formation. We find that annealing by cyclic shear leads to the self-organization of plasticity, which can be characterized by a density of local mechanical stress thresholds. These thresholds exhibit anisotropy, depending on their alignment with the driving direction. The interplay between these thresholds dictates how the driving history—particularly its direction and amplitude—is imprinted into the material's local structure. We develop readout protocols capable of accessing both the amplitude and the direction of the mechanical training. Our findings can be understood within the framework of return point memory which emerges as a result of mechanical annealing. Building on this, we develop a Preisach-like model of directional memory that describes well our numerical results. We conclude with a discussion of similarities of the evolution of plasticity under mechanical annealing and adaptive evolution in changing environments.

We report on experimental studies of the distribution of the off-diagonal elements of the scattering ($S$) matrix of open microwave networks with symplectic symmetry and chaotic wave dynamics. These consist of two geometrically identical subgraphs with unitary symmetry described by complex conjugate Hamiltonians that are coupled by a pair of bonds. The results are compared to random-matrix theory (RMT) predictions obtained on the basis of the Heidelberg approach for the $S$ matrix of open quantum-chaotic systems, employing random matrices from the Gaussian symplectic ensemble. We demonstrate that deviations observed in the distributions of the off-diagonal $S$-matrix elements may be attributed to the fact that the subgraphs are not fully connected, and propose a RMT model, which takes this into account and indeed confirms the experimental results.

We introduce the $\alpha$-Gauss-Logistic map, a new nonlinear dynamics constructed by composing the logistic and $\alpha$-Gauss maps. Explicitly, our model is given by $x_{t+1}=f_L\left(x_t\right)x_t^{-\alpha }-\lfloor f_L\left(x_t\right)x_t^{-\alpha }\rfloor$, where $f_L\left(x_t\right)=r{x}_t(1-x_t)$ is the logistic map and $\lfloor ...\rfloor$ is the integer part function. Our investigation reveals a rich phenomenology depending solely on two parameters, $r$ and $\alpha$. For $\alpha <1$, the system exhibits multiple period-doubling cascades to chaos as the parameter $r$ is increased, interspersed with stability windows within the chaotic attractor. In contrast, for $1\le \alpha <2$, the onset of chaos is abrupt, occurring without any prior bifurcations, and the resulting chaotic attractors emerge without stability windows. For $\alpha \ge 2$, the regular behavior is absent. The special case of $\alpha =1$ allows an analytical treatment, yielding a closed-form formula for the Lyapunov exponent and conditions for an exact uniform invariant density, using the Perron-Frobenius equation. Chaotic regimes for $\alpha =1$ can exhibit gaps or be gapless. Surprisingly, the golden ratio $\mathrm{\Phi }$ marks the threshold for the disappearance of the largest gap in the regime diagram. Additionally, at the edge of chaos in the abrupt transition regime, the invariant density approaches a $q$-Gaussian with $q=2$, which corresponds to a Cauchy distribution.

We revisit vector breathers with self-steepening in both focusing and defocusing regimes. We present previously unknown existence diagrams of Akhmediev breathers in a frequency-wave-number plane, which can be used to identify a family of nondegenerate second-order Akhmediev breathers in the focusing regime. The existence diagrams are extracted from the exact solutions where we modify the Lax pair to obtain a simple one-to-one correspondence between the square Lax spectral parameter and eigenvalues. Based on the existence diagrams, unique excitations of nondegenerate second-order Akhmediev breathers are predicted. Their role in the nonlinear stage of modulation instability is revealed. In particular, numerical simulations starting from a nonideal initial condition with either single- or two-component modulations confirm our theoretical prediction.

Building on the work of Almasan et al. [IEEE Trans. Netw. Sci. Eng. 12, 1649 (2025)], we propose a continuous-time Markov model for human contact dynamics denoted the continuous random walkers induced temporal graph model (CRWIG). In CRWIG, $M$ walkers move randomly and independently of each other on a Markov graph with $N$ nodes in continuous time. If walkers are in the same state (node of the Markov graph) at time $t$, a link is created between them in their temporal contact graph $G\left(t\right)$, where each walker corresponds to one of the $M$ nodes. We define the exact Markov governing equation that describes the movement of the ensemble of $M$ walkers. We investigate the consequences of the time discretization of CRWIG. We prove that CRWIG is characterized by exponential decay of the initial condition and exponentially tailed intermeeting times of the walkers. We investigate two special cases of CRWIG and derive analytical results supported by simulations. We extend the model to allow for nonexponential sojourn times for the single walkers. The non-Markovian model extension of CRWIG is able to reproduce empirical properties of human mobility observed on data: arbitrary flight length distribution, arbitrary pause-time distribution, and intermeeting time distributions that are power-law with an exponential tail.

A phase-field approach is proposed to model the chemotaxis of Dictyostelium discoideum. In this framework, motion is controlled by active forces as determined by the Meinhardt model of chemical dynamics which is used to simulate directional sensing during chemotaxis. Then the movement of the cell is achieved by the phase-field dynamics, while the reaction-diffusion equations of the Meinhardt model are solved on an evolving cell boundary. This task requires the extension of the usual phase-field formulation to allow for components that are restricted to the membrane. The coupled system is numerically solved by an efficient spectral method under periodic boundary conditions. Numerical experiments show that our model system can successfully mimic the typically observed pseudopodia patterns during chemotaxis.

Nanoconfined binary gas mixtures exhibit complex pressure-volume-temperature properties critical to applications such as shale gas extraction and membrane separation, yet traditional models such as Dalton's Law of Partial Pressures and Lorentz-Berthelot mixing rules fail to capture their behavior due to confinement-induced effects. This study investigates two-component gas mixtures in graphene nanochannels using molecular dynamics (MD) simulations, revealing that the pressure of gas mixtures decreases with the increasing potential energy due to enhanced adsorption. Extending our prior high-precision equation of state (EOS) for single-component gases [C. Sun et al., Phys. Rev. E 111, 025413 (2025)], we propose a different mixing rule based on Lennard-Jones potential parameters, surpassing the limitations of Lorentz-Berthelot mixing rules by incorporating gas type and molar ratio effects. Partition modification further accounts for nonuniform adsorption, decomposing pressure into bulk and adsorption contributions. The resulting EOS achieves predictive accuracies of 85%–98% across diverse gas component pairs (e.g., ${\mathrm{H}}_2$–Ne, ${\mathrm{N}}_2-\mathrm{C}{\mathrm{O}}_2$), component molar ratios, and total molecule numbers, compared to MD simulations. This work elucidates the interplay of potential energy, adsorption, and confinement, offering a robust framework for theoretical and engineering advancements in nanoconfined gas systems.

This work deals with numerical simulation of water freezing and thawing in a complex three-dimensional geometry of a porous medium. The porous structure is represented by a virtual container filled with glass beads. Phase transition modeling is approached at both macroscale and microscale, combining heat transfer in a heterogeneous medium and a phase-field approximation of the Gibbs-Thomson relation by means of the Allen-Cahn equation. The formulation of the model contains novel components tailored for the given purpose. In addition to this general model, two limit scenarios are considered: At the macroscale, surface tension effects are negligible and phase transition focusing based on temperature can replace the Allen-Cahn equation. In contrast to that, simulations of equilibrium states at the microscale allow us to eliminate the heat equation by assuming constant supercooling. For numerical solution, an efficient hybrid parallel algorithm based on the finite volume method and the Runge-Kutta-Merson solver with adaptive time stepping are employed. The results of different model variants at different scales are discussed. In a parametric study, the full phase-field model is demonstrated to deliver consistent results across a wide range of surface tension values, exhibiting curvature-induced premelting if surface tension is artificially exaggerated. As surface tension tends to the realistic values, the results of the phase-field approach those of the simplified temperature-driven phase transition model. In addition, microscale simulations of water freezing at different supercooling values aim to predict the unfrozen water content and compare the results with data from literature. Numerical stability, accuracy, and computational costs are also discussed.

Fractional Brownian motion is a Gaussian stochastic process with long-range correlations in time; it has been shown to be a useful model of anomalous diffusion. Here, we investigate the effects of mutual interactions in an ensemble of particles undergoing fractional Brownian motion. Specifically, we introduce a mean-density interaction in which each particle in the ensemble is coupled to the gradient of the total, time-integrated density produced by the entire ensemble. We report the results of extensive computer simulations for the mean-squared displacements and the probability densities of particles undergoing one-dimensional fractional Brownian motion with such a mean-density interaction. We find two qualitatively different regimes, depending on the anomalous diffusion exponent $\alpha$ characterizing the fractional Gaussian noise. The motion is governed by the interactions for $\alpha <4/3$, whereas it is dominated by the fractional Gaussian noise for $\alpha >4/3$. We develop a scaling theory explaining our findings. We also discuss generalizations to higher space dimensions and nonlinear interactions, the relation of our process to the “true” or myopic self-avoiding walk, as well as applications to the growth of strongly stochastic axons (e.g., serotonergic fibers) in vertebrate brains.

We apply a simple sudden-quench approximation for the unitary work strokes of a quantum Otto engine in order to provide a general analysis of its performance, applicable to arbitrary quantum models with two-body interactions. This work extends recent results for an interaction-driven Otto cycle to generic many-body interacting quantum models, providing universal bounds on their operation efficiency. From this, we demonstrate that the net work of such an engine cycle is determined entirely by interparticle correlations. Applications are demonstrated for a handful of paradigmatic many-body quantum models, including an engine—with a spin-1/2 Fermi gas with contact two-body interactions as its working medium—in which we leverage control over spin polarization to greatly enhance its performance compared to the unpolarized case. We then extend the analysis of interaction-driven quantum Otto engine cycles to systems where control is exerted over the strength of arbitrary quantum operators that might be present in the system Hamiltonian (such as one-body, or three-body, etc.), finding that the general principles derived for the sudden quench with two-body interactions apply universally. As an example, this is demonstrated for a conventional volumetric Otto cycle, where beneficial net work is generated by leveraging the control over the frequency of an external trap, which is a one-body operator. However, we emphasize that the results derived here apply universally to all Otto engine cycles operating under a sudden-quench protocol.

The steadily moving charged object (obstacle) induced modulated electrostatic ion cyclotron (EIC) wave dynamics is modeled through a forced nonlinear Schrödinger equation in collisionless and homogeneous plasmas. The disturbance created by the moving obstacle acts as the source of free energy and is responsible for the forcing term. The parametric simulations at the transcritical speed predict the ordered to highly disordered spatiotemporal patterns of the potential depending on the amplitude and width of the moving source and also the strength of the magnetic field. The time series analysis using the delay embedding phase-space (DEPS) reconstruction method is noteworthy, which predicts the chaotic-like behavior of the highly disordered patterns controlled by the magnetic field. Moreover, the random noise driven simulation predicts the external force induced EIC extreme events. The disordered patterns correspond to the time domain spiky and large-amplitude wave burst electric field structure observed in the astrophysical plasmas.

Tuning the interface properties of multiphase models is of paramount importance to the final goal of achieving a one-to-one matching with nucleation and cavitation experiments. The surface tension, at the leading order, and the Tolman length, at higher order, play a crucial role in the estimation of the free-energy barrier determining the experimentally observed nucleation rates. The lattice Boltzmann method allows for a computationally efficient modeling approach of multiphase flows; however, tuning results are concerned with the surface tension and neglect the Tolman length. We present a novel perspective that leverages all the degrees of freedom hidden in the forcing stencil of the Shan-Chen multiphase model. By means of the lattice pressure tensor we determine and tune the coefficients of higher-order derivative terms related to surface tension and Tolman length at constant interface width and density ratio. We test the method by means of both hydrostatic and dynamic simulations and demonstrate the dependence of homogeneous nucleation rates on the value of the Tolman length. This work provides a new tool that can be integrated with previously existing strategies thus marking a step forwards to a high-fidelity modeling of phase-changing fluid dynamics.

This work introduces a generic quantitative framework for studying dynamical processes that involve interactions of polymer sequences. Possible applications range from quantitative studies of the reaction kinetics of polymerization processes to explorations of the behavior of chemical implementations of computational—including basic lifelike—processes. This way, we establish a bridge between thermodynamic and computational aspects of systems that are defined in terms of sequence interactions. As by-products of these investigations, we clarify some common confusion around the notion of “autocatalysis” and show quantitatively how a chemically implemented Turing machine can operate close to the Landauer bound. Using a Markov process model of polymer sequence composition and dynamical evolution of the Markov process's parameters via an ordinary differential equation that arises when taking the double “chemical” many-particle limit as well as “rarefied interactions” limit, this approach enables—for example—accurate quantitative explorations of entropy generation in systems where computation is driven by relaxation to thermodynamic equilibrium. The computational framework internally utilizes the Scheme programming language's intrinsic continuation mechanisms to provide nondeterministic evaluation primitives that allow the user to specify example systems in straight purely functional code, making exploration of all possible relevant sequence composition constellations–which would be otherwise tedious to write code for—automatic and hidden from the user. As the original motivation for this work came from investigations into emergent program evolution that arises in computational substrates of the form discussed in recent work on “computational life” [Alakuijala et al. arXiv:2406.19108], a major focus of attention is on giving a deeper explanation of key requirements for the possible emergence of self-replicators, especially in settings whose behavior is governed by real-world physics rather than ad hoc rules that may be difficult to implement in a physical system. A collection of fully worked out examples elucidate how this modeling approach is quantitatively related to Monte Carlo–based simulations, as well as exact or approximate analytic approaches, and how it can be utilized to study a broad range of different systems. These examples can also serve as starting points for further explorations.

Estimating free energy is a fundamental and difficult problem in statistical physics. In recent years, machine-learning methods, especially deep generative models, have been extensively applied to this problem. The variational autoregressive network (VAN) is one of the most successful methods. VAN has notable advantages, such as tractable probabilities from normalized distribution or the absolute value of the free energy. However, the sampling process of VAN is serial, which limits its application to high-dimensional and large-scale systems. In this work, we utilize renormalization group (RG) with VAN, namely RG-VAN, to improve the sampling efficiency of VAN. The method reduces the computational complexity from polynomial to logarithmic with respect to the degrees of freedom. Numerical experiments on the two-dimensional Ising model and Edwards-Anderson model show that RG-VAN can accelerate the training and inference process by an order of magnitude, with little loss of accuracy.

We study spreading processes in two-dimensional systems of heterogeneous active agents that exhibit different individual active speeds. We obtain, combining kinetic and complex network theory, an analytical expression for the spreading threshold that depends not only on the first but also second moment of the active speed distribution. Moreover, we prove that spreading can even occur for vanishing average active speed. Furthermore, we find that random vaccination strategies are ineffective in heterogeneous active systems, whereas targeted ones are effective. Finally, we also show that vaccination acts as (quenched) noise: it can decrease or increase the outbreak size. Our results offer insights into how information propagates in heterogeneous populations of active agents.