Sampling synthetic turbulent fields as a computationally tractable surrogate for direct numerical simulations (DNS) is an important practical problem in various applications, and allows us to test our physical understanding of the main features of real turbulent flows. Reproducing higher-order Eulerian correlation functions, as well as Lagrangian particle statistics, requires an accurate representation of coherent structures of the flow in the synthetic turbulent fields. To this end, we propose in this paper a systematic coherent-structure-based method for sampling synthetic random fields, based on a superposition of instanton configurations—an instanton gas—from the field-theoretic formulation of turbulence. We discuss sampling strategies for ensembles of instantons, both with and without interactions and including Gaussian fluctuations around them. The resulting Eulerian and Lagrangian statistics are evaluated numerically and compared against DNS results, as well as Gaussian and log-normal cascade models that lack coherent structures. The instanton gas approach is illustrated via the example of one-dimensional Burgers turbulence throughout this paper, and we show that already a canonical ensemble of noninteracting instantons without fluctuations reproduces DNS statistics very well. Finally, we outline extensions of the method to higher dimensions, in particular to magnetohydrodynamic turbulence for future applications to cosmic ray propagation.

This study introduces an approach to extend the Keller and Skalak (KS) theory by integrating the modified Riemann-Liouville fractional derivative. Our focus is on investigating the transition of red blood cells from flipping to stationary motion within shear flows. Expanding upon the predictions outlined by KS regarding flipping periods and alignment with flow, our research aims to delve deeper into this transition process, which remains largely unexplored experimentally. The rationale for employing fractional derivatives stems from their capacity to capture memory effects and nonlocal interactions, thus providing a more nuanced modeling framework for complex particle dynamics.

We present a class of self-similar solutions describing ultrahigh compression of a uniform-density target by spherically converging, stacked shock waves. Extending the classical Guderley model, we derive a scaling law for the final density of the form ${\rho }_r/{\rho }_0\propto {\widehat{P}}^{\beta (N-1)}$, where $N$ is the number of shocks, $\widehat{P}$ the stage pressure ratio, and $\beta$ a numerical exponent determined by the adiabatic index $\gamma$. One-dimensional hydrodynamic simulations confirm the validity of this scaling across a broad parameter range. Notably, the relation remains accurate even in the strongly nonlinear regime up to $\widehat{P}\sim 70$, well beyond the perturbative limit, highlighting the robustness and practical relevance of the model. Owing to its volumetric geometry, this compression scheme inherently avoids the Rayleigh-Taylor instability, which typically compromises shell-based implosions, and thereby establishes a theoretical benchmark for instability-free compression in inertial confinement fusion.

The thinning method for numerically generating the arrival times of nonhomogeneous Poisson processes (NHPPs) has been adapted to accelerate Monte Carlo simulations of kinetic Ising models (KIMs) with Glauber spin-flip dynamics. The performance of the proposed algorithms is illustrated through simulations of the decay of metastable states in stationary KIMs and of the hysteresis of KIMs in a periodic external field. The thinning is implemented using piecewise-constant majorizing functions that bound from above or are equal to the NHPP rate. In favorable cases, this approach enables the simulations of hysteresis at frequencies in the tens of nanohertz and the decay of metastable states with lifetimes many orders magnitude longer than those accessible in previous simulations. The simulated results show good agreement with low-temperature analytical theories. Although algorithmic acceleration becomes more pronounced at lower temperatures, hysteresis has been simulated at moderately low temperatures of practical relevance, ranging from below room temperature up to values used in hyperthermia applications.

Active matter represents a class of nonequilibrium systems that constantly dissipate energy to produce directed motion. Controlling active matter to achieve a target state holds great potential for advancements in synthetic molecular motors, targeted drug delivery, and adaptive smart materials. However, the inherently nonequilibrium nature of active matter poses a significant challenge in achieving optimal control with minimal energy cost. In this work, we extend the concept of thermodynamic geometry, originally developed to provide geometric representations of energy cost in passive systems, to active systems. We propose a systematic geometric framework for minimizing energy cost in active matter with interparticle interactions. Specifically, we derive a cost metric that defines a Riemannian manifold for control parameters, enabling the application of powerful geometric tools to design optimal control protocols. The geometric perspective reveals that, unlike in passive systems, minimizing energy cost in active systems entails a universal trade-off scaling relation, leading to an optimal transportation speed in the geometric space that intriguingly coincides with the self-propulsion speed of an active Brownian particle. This insight enriches the broader concept of thermodynamic geometry. Furthermore, the derived scaling relation suggests an optimal protocol duration that aligns with the general expectation proposed by Davis et al. [Phys. Rev. X 14, 011012 (2024)] for active matter. We illustrate the utility of this approach by optimizing the performance of an active monothermal engine within the geometric framework.

We investigate a decentralized traffic routing strategy that leverages local traffic load (LTL) information to improve the overall performance of urban transportation networks. Unlike conventional approaches based on static shortest-distance paths or globally optimized travel times, the proposed strategy dynamically balances travel distance with real-time local congestion levels, enabling efficient detour decisions with minimal computational overhead. Using a two-dimensional cellular automaton model, we simulate traffic dynamics under different routing schemes and evaluate key performance indicators, including average velocity, flow, and arrival rate. The results show that the LTL-based strategy significantly enhances system throughput and stability across a broad range of traffic densities. We attribute this improvement to its ability to induce timely, locally informed detours that suppress congestion buildup and spatial heterogeneity. Further analyses of detour statistics, travel distances, and spatiotemporal vehicle density fluctuations elucidate the underlying mechanisms. The robustness of the strategy is also confirmed on real-world urban network topologies. These findings suggest that routing strategies based on localized feedback can provide scalable and adaptive solutions for mitigating congestion in complex traffic systems.

We investigate the melting behavior of two-dimensional colloidal crystals stabilized by a core-softened potential featuring two competing interaction length scales. Using molecular-dynamics simulations, we uncover and analyze distinct melting pathways for three polymorphic solid phases: a low-density triangular (LDT) lattice, a stripe phase, and a kagome crystal. The triangular and kagome crystals undergo abrupt first-order transitions, driven by the interplay between energetic frustration and structural reorganization. In particular, the LDT phase melts through a sharp transition induced by a crossover between the two characteristic length scales. In contrast, the stripe phase exhibits a continuous transition with liquid-crystalline features: Orientational and translational order decay gradually, while intrastripe mobility persists, consistent with a Kosterlitz-Thouless-Halperin-Nelson-Young-like scenario. These findings demonstrate that melting in two-dimensional soft-matter systems is inherently nonuniversal and governed by the competition between lattice symmetry, frustration, and multiple interaction scales. Our results provide microscopic insight into melting mechanisms beyond classical universality classes and offer guiding principles for the design of self-assembled materials with tunable phase behavior.

We study the emergent orientational dynamics of a dumbbell dimer—two asymmetric monomers connected by a linking spring—in a three-dimensional chiral environment with odd viscosity. In classical systems with conserved parity symmetry, reciprocal oscillations of a dimer do not lead to rotational motion. Here, through an analytical calculation, we find that the presence of chirality in the system induces rotational dynamics as a function of the expansion/contraction of the dimer. By incorporating thermal fluctuations, we further find that the rotational diffusivity is affected by the coupling between conformational fluctuations and rotational motion. Our results provide insights into problems where the parity symmetry is broken and can be used as a building block to study similar models at the collective level. These problems include multicomponent molecular machines in odd-viscous fluids and systems with charged polymers where oddity is present through external magnetic fields.

Disorder is often seen as detrimental to collective dynamics, yet recent work has shown that heterogeneity can enhance network synchronization. However, its constructive role in stabilizing nontrivial cooperative patterns remains largely unexplored. In this Letter, we show that frequency heterogeneity among oscillators can induce stable cyclops and cluster states in Kuramoto networks with higher-mode coupling, even though these states are unstable in the identical oscillator case. Cyclops states, introduced in [Munyaev et al., Phys. Rev. Lett. 130, 107201 (2023)], feature two synchronized clusters and a solitary oscillator, requiring a delicate phase balance. Surprisingly, heterogeneity alone is sufficient to stabilize these patterns across a sizable range of detuning values without needing to be compensated by other forms of disorder or external tuning. To explain this effect, we introduce a mesoscopic collective coordinate approach that links microscopic frequency structure with mean-field cluster-level stability. Importantly, we demonstrate that the same disorder-induced stabilization mechanism also arises robustly in biologically and physically grounded networks of Winfree and Stuart-Landau oscillators, pointing to its generality and opening directions for designing multistate dynamics in heterogeneous networks.

We investigate the coevolution of network structure and opinion dynamics by integrating a threshold-based complex contagion model with a target rewiring mechanism. In contrast to previous models that allow all nonadopting nodes to rewire indiscriminately, our framework introduces the concept of superspreader nonadopting nodes that are particularly instrumental in spreading the adoption. Only these nodes rewire their connections away from adopting neighbors to randomly chosen nonadopting nodes. Through mean-field theoretical analysis and numerical simulations, we show that this targeted rewiring strategy efficiently contains adoption cascades while significantly reducing the overall number of rewiring operations required. The presence of superspreaders naturally localizes structural adaptation to critical points in the network, leading to a more economical and stable network evolution. Our results reveal that this mechanism not only substantially reduces the cost of rewiring but also causes minimal structural change, making the system more efficient and realistic in terms of intervention cost and rewiring burden.

We numerically show that a deep neural network (DNN) can learn macroscopic thermodynamic laws purely from microscopic data. Using molecular dynamics simulations, we generate the data of snapshot images of gas particles undergoing adiabatic processes. We train a DNN to determine the temporal order of input image pairs. We observe that the trained network induces an order relation between states consistent with adiabatic accessibility, satisfying the axioms of thermodynamics. Furthermore, the internal representation learned by the DNN acts as an entropy. These results suggest that machine learning can discover emergent physical laws that are valid at scales far larger than those of the underlying constituents—opening a pathway to data-driven discovery of macroscopic physics.

In both classical and quantum physics, irreversible processes are described by maps that contract the space of states. The change in volume has often been taken as a natural quantifier of the amount of irreversibility. In Bayesian inference, loss of information results in the retrodiction for the initial state becoming increasingly influenced by the choice of reference prior. In this paper, we import this latter perspective into physics, by quantifying the irreversibility of any process with its Bayesian subjectivity—that is, the sensitivity of its retrodiction to one's prior. From this perspective, we review analytical and numerical results that highlight both intuitive and subtle insights that this measure sheds on irreversible processes.

We study the dynamics of the Kuramoto model on the sphere under higher-order interactions and an external periodic force. For identical oscillators, we introduce a novel way to incorporate three- and four-body interactions into the dynamics of the order parameter, allowing for a full dimensional reduction of this system. We discuss how such reduction can be implemented in two different ways and how they are related. When restricted to the equator, the dynamics is similar to that of the usual Kuramoto model, up to an interesting renormalization of the coupling constants. Outside this plane, the motion reduces to a two-parameter set of periodic orbits. We also locate the bifurcation curves of the system as functions of different parameters.

We study a simple one-dimensional model of swarmalators, a generalization of phase oscillators that swarm around in space as well as synchronize internal oscillations in time. Previous studies of the model focused on Kuramoto-type couplings, where the phase interactions are governed by phase differences. Here we consider Winfree-type coupling, where the interactions are multiplicative, determined by the product of a phase response function $Q\left(\theta \right)$ and phase pulse function $P\left(\theta \right)$. This more general interaction (from which the Kuramoto phase differences emerge after averaging) produces rich physics: six long-term modes of organization are found, which we characterize numerically and analytically.

To understand how interaction topology and competition affect the diversity of information in the real world, we study the social contagion model introduced by Halvorsen-Pedersen-Sneppen (HPS) [Phys. Rev. E 103, 022303 (2021)] on static complex networks. The diversity of the information is measured through the root mean square of the information value $W(N,t)$, which corresponds to the interface width in roughening phenomena. From the numerical simulations, we find that the HPS model on Erdős-Rényi random networks is always in the smooth phase characterized by the roughness exponent $\alpha =0$. However, it undergoes a transition from a rough phase ($\alpha >0$) to a smooth phase on scale-free networks as the degree exponent $\gamma$ increases. When $\gamma =4$, we find that the steady-state value of $W(N,t)$ scales as $W_{\mathrm{sat}}\left(N\right)\sim logN$, indicating that the threshold for the transition is ${\gamma }^{*}\simeq 4$.

The cover-time problem is fundamental to the exhaustive exploration of a given complex domain. Deriving the cover-time distribution from the structural properties of a system remains a significant challenge, yet it is of great value across various social, natural, and engineering applications. In this paper, we propose a scheme to analytically estimate the original cover-time distribution. The approach is based on the recent discovery of the universal cover-time distribution after rescaling the original cover times by the first-passage times. Here, we find that the first-passage time of each node can be effectively approximated by the inverse of the occupation ratio, i.e., the probability that a walker visits this node, which is proportional to the number of its links, multiplied by a fitting constant. With this, conversely, the original cover-time distribution can be obtained from the universal scaled cover-time distribution. We have examined this theoretical prediction of the original cover-time distribution of random walks in several model and realistic network systems, and show excellent agreement with the numerical simulations. Furthermore, for random walk on Erdős-Rényi graph, we analyze the effectiveness of this mean-field approximation and derive a validity bound indicating that when the network is too sparse the approximation may fail. These findings may provide an analytical tool for analyzing exhaustive random exploration in complex domains.

An analog of nuclear magnetic resonance is realized in a microwave network with symplectic symmetry. The network consists of two identical subgraphs coupled by a pair of bonds with a length difference corresponding to a phase difference of $\pi$ for the waves traveling through the bonds. As a consequence, all eigenvalues appear as Kramers doublets. Detuning the length difference from the $\pi$ condition Kramers degeneracy is lifted, which may be interpreted as a Zeeman splitting of a spin $\frac{1}{2}$ in a magnetic field. The lengths of another pair of bonds are modulated periodically with frequencies of some 10 MHz by means of diodes, thus emulating a magnetic radio-frequency field. This setup enables the realization of well-known NMR phenomena, such as the transformation from the laboratory to the rotating frame and the observation of Lorentzian-shaped resonance lines.

The linear stability of a lipid membrane under a dc electric field, applied perpendicularly to the interface, is investigated in the electrokinetic framework, taking into account the dynamics of the Debye layers formed near the membrane. The perturbed charge in the Debye layers redistributes and generates destabilizing Maxwell stress on the membrane, which outweighs the stabilizing contribution from the electrical body force, leading to a net destabilizing effect. The instability is suppressed as the difference in the electrolyte concentration of the solutions separated by the membrane increases, due to a weakened base state electric field near the membrane. This result contrasts with the destabilizing effect predicted using the leaky dielectric model in cases of asymmetric conductivity. We attribute this difference to the varying assumptions about the perturbation amplitude relative to the Debye length, which result in different regimes of validity for the linear stability analysis within these two frameworks.