Small-scale dynamos (SSDs) amplify magnetic fields in turbulent plasmas. Theory predicts nonlinear magnetic energy growth $E_{\mathrm{mag}}\propto t^{p_{\mathrm{nl}}}$, but this scaling has not been tested across flow regimes. Using a large ensemble of SSD simulations spanning subsonic to supersonic turbulence, we measure linear growth ($p_{\mathrm{nl}}=1$) in subsonic flows and quadratic growth ($p_{\mathrm{nl}}=2$) in supersonic flows. In all cases, the nonlinear dynamo converts a nearly constant fraction approximately equal to $1/100$ of the turbulent kinetic energy flux into magnetic energy, and the nonlinear phase has a characteristic duration $\mathrm{\Delta }t\approx 20t_0$, where $t_0$ is the outer-scale turnover time. By isolating the onset of magnetic backreaction in SSDs, our statistical ensemble approach identifies a robust efficiency and duration for the nonlinear SSD that can be used to interpret more complex astrophysical and laboratory plasmas.

A beam of relativistic charged particles propagating in a plasma can drive plasma electrons to oscillate and together form a wave whose phase velocity matches the velocity of the driving beam. These plasma waves realize state-of-the-art compact accelerators through plasma wakefield acceleration. Here, we formulate a one-dimensional analytic theory of beam-driven nonlinear plasma waves that is valid for positively and negatively charged particle beams propagating with arbitrary velocity. We find that nonlinear plasma waves can arise within the interior region of the driving beam, with wavelength and amplitude different from those of the plasma wakefield. In contrast to plasma wake waves, the interior waves are robust with respect to driver length variation. Our analytic results can be used to design current and future plasma accelerators, to understand their limitations due to wave breaking, and for applications in coherent light generation in plasma, such as relativistic mirrors formed by breaking plasma waves. Furthermore, we discuss the potential of our results to offer insights into the origin of ultrahigh-energy cosmic rays.

This article presents a detailed investigation of solid-fluid phase coexistence in a bilayer dusty plasma crystal subjected to varying confinement ring bias voltages in a dc glow discharge argon plasma. Melamine formaldehyde particles were employed to form a stable, hexagonally ordered bilayer crystal within a confinement ring electrically isolated from the grounded cathode. By systematically adjusting the confinement ring bias, a distinct phase coexistence emerged characterized by a fluidlike melted core surrounded by a solid crystalline periphery. Crucially, analysis of the phonon spectra revealed frequency shifts that deviate significantly from the predictions of classical monolayer Mode-Coupling Instability theory. Stability analysis further demonstrated that dynamic interlayer particle pairing and the associated increase in nonreciprocal interaction strength are strongly correlated with the onset of structural destabilization. These findings highlight previously underappreciated mechanisms driving the melting transition in bilayer dusty plasmas, offering a more comprehensive understanding of phase behavior in complex plasma systems. The results underscore the importance of interlayer coupling and confinement effects in tuning structural transitions.

Chiral fluids are defined by broken mirror or time-reversal symmetry, giving rise to tensorial transport coefficients with antisymmetric components. A key example is the odd mobility tensor, which governs the response of a chiral tracer to an applied force and induces a characteristic transverse drift. While this response is well understood in the infinite dilution limit, the impact of interparticle interactions on the tracer dynamics remains largely unexplored. Here, we conduct an analytical and computational study of a chiral fluid with interparticle interactions and show that, under an external driving force, a chiral tracer can undergo a complete reversal of both its transverse Hall drift and its advection along the force. This reversal emerges from the interplay between odd mobility and interaction-mediated forces, resulting in a phenomenon reminiscent of absolute negative mobility.

In astrophysical shear flows, the Kelvin-Helmholtz (KH) instability is generally suppressed by magnetic tension, provided a sufficiently strong streamwise magnetic field. This is often used to infer upper (or lower) bounds on field strengths in systems where shear-driven fluctuations are (or are not) observed, on the basis that perturbations cannot grow in the absence of linear instability. On the contrary, by calculating the maximum growth that small-amplitude perturbations can achieve in finite time for such a system, we show that perturbations can grow in energy by orders of magnitude even when the flow is sub-Alfvénic, raising the possibility that shear-driven turbulence may be found even in the presence of strong magnetic fields, and challenging inferences from the observed presence or absence of shear-driven fluctuations. We further show that magnetic fields introduce additional nonmodal growth mechanisms relative to the hydrodynamic case, and that 2D simulations miss key aspects of these growth mechanisms.

Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-and-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool, offering new opportunities for uncovering hidden causal mechanisms and better understanding complex systems. In this work, we address the issue of detecting the intrinsic causal links of a large class of complex systems in the framework of the response theory in physics. We develop some theoretical ideas put forward by Aurell and Del Farraro [J. Phys. Conf. Ser. 699, 012002 (2016)], and technically we use state-of-the-art ML techniques to build up models from data. We consider both linear stochastic and nonlinear systems. Finally, we compute the asymptotic efficiency of the linear-response-based causal predictor in a case of a large-scale Markov process network of linear interactions.

We consider a two-component log gas consisting of $N$ particles confined in a one-dimensional harmonic trap, where the particles interact via a repulsive logarithmic potential. Unlike the previously studied cases involving a single particle species, the present system consists of two types of particles. Particles of the same type interact with an intraspecies interaction strength $J_1$, while particles of different types interact with an interspecies interaction strength $J_2$. Furthermore, we consider a fraction $f(<1)$ of the particles belongs to one type, while the remaining fraction $(1-f)$ are of the other type. We show that this two-component system in thermal equilibrium can be approximately mapped to a single-component system with an effective interaction strength $J_f=J_1(1-2f+2f^2)+J_2(2f-2f^2)$. This mapping is supported by results for the average density profile and the spacing distribution obtained via Monte Carlo simulations at low temperature.

We formulate a kinetic theory of two species of hard spheres undergoing reactive collisions that convert chemical energy into kinetic energy. The model describes an active species interacting with a passive background, labeled as “birds” and “air,” respectively, with the reactive collisions representing self-propulsion. Microscopic reversibility of the reactive dynamics is imposed, and a chemostat is introduced to drive the system out of equilibrium. When the chemostat is sufficiently strong, and one restricts to grazing interspecies collisions, we find that the bird momentum damping coefficient can change sign, giving rise to a flocking transition.

White dwarf models require accurate equations of state and ionic transport coefficients in the warm dense-matter regime, where kinetic theory models and tabulated equations of state are often inaccurate. Here spectral-partitioned density functional theory and machine-learned interatomic potentials are combined to perform large-scale, first-principles quantum molecular dynamics simulations of deuterated water $\left({\mathrm{D}}_2\mathrm{O}\right)$ near the principal Hugoniot. This approach retains Kohn-Sham accuracy while achieving orders-of-magnitude speedup, yielding converged equation of state and transport properties over a broad pressure and temperature range. The results reveal the thermodynamic conditions under which ionic transport models for interdiffusivity and shear viscosity converge and identify those in closest agreement with density functional theory benchmarks at temperatures in the warm dense-matter regime. The present framework extends first-principles transport calculations to higher temperatures than previously achieved and provides an efficient, scalable, and general approach for studying transport properties in complex multicomponent mixtures.

Field-directed assembly presents a powerful approach for directing the organization of colloidal particles into long-range crystalline orderings. While their ability to tune colloidal crystallization has been widely studied, insights into how they mediate the formation of such structures remain an open question. Here, we utilize an optically generated axisymmetric flow field as an external stimulus to direct the cocrystallization of a ternary system of colloidal particles. We show that the presence of flow fields fluidizes kinetically trapped clusters, suppressing fractionalization tendencies and promoting the formation of cocrystalline assemblies. By developing a model to simulate flow-mediated processes in coarse-grained simulation, we additionally leverage computational screening to identify experimental systems capable of forming complex morphologies previously unobserved in spherical colloidal assemblies, all of which are then validated in experiments. Our integrated experiment-simulation study showcases how axisymmetric flow can serve as a powerful handle to direct the cocrystallization in a multicomponent colloidal system. Furthermore, it provides a path for merging equilibrium and nonequilibrium processes for use in self-assembly, opening new avenues for predictive design of complex materials at the nanoscale.

What are the resources that can be leveraged for a thermodynamic device to exhibit genuine quantum advantage? Typically, the answer to this question is sought in quantum correlations. In the present work, we show that quantum Otto engines that operate with nonlinear qubits significantly outperform linear engines. To this end, we develop a comprehensive thermodynamic description of nonlinear qubits, starting with identifying the proper thermodynamic equilibrium state. We then show that for ideal cycles as well as at maximum power the efficiency of the nonlinear engine is significantly higher. Interestingly, nonlinear dynamics can be thought of as an effective description of a correlated, complex quantum many-body system. Hence, our findings corroborate common wisdom, while at the same time propose a design of more efficient quantum engines.

Regions of fast-flowing ice in ice sheets, known as ice streams, have been theorized to be able to exhibit build-up/surge oscillatory variability due to thermomechanical coupling at the base of the ice. A simple model of three coupled ice streams is constructed to replicate the spatial configuration of a single ice stream being bisected into two termini. The model is constructed to mimic existing branching ice streams in northern Greenland. This model is shown to exhibit both steady-flow and build-up/surge oscillations. Further, the variability can be chaotic due to the nonlinear coupling of three incommensurate frequencies. This provides a mode of chaotic internal variability for ice sheets that contain these types of ice streams.

Nanoscale molecular systems such as DNA require an atomistic quantum treatment to accurately capture their electrical properties, owing to their small dimensions. A central challenge in modeling transport through these systems is the inclusion of phase-breaking scattering. Decoherence-probe methods enable such modeling for large systems, but existing implementations have limitations. Energy-independent scattering rates tend to overly broaden energy levels, yielding an unphysically large density of states (DOS) within energy gaps. Conversely, energy-dependent models may introduce spurious energy levels and transmission peaks and require additional fitting parameters. To address these issues, we use a DOS-weighted decoherence model in which the scattering rate and, equivalently, the associated decoherence probe self-energy is proportional to the local DOS. The model iteratively updates the decoherence self-energy and the DOS until self-consistency is achieved. This approach yields energy and spatially dependent scattering rates that avoid spurious energy levels without the excessive broadening of DOS in energy gaps. We also examine the impact of partitioning schemes that prevent artificial pathways for charge transport and discuss how they can be avoided. Overall, the DOS-weighted model provides an improved and more physically grounded framework for simulating charge transport in DNA and potentially other weakly coupled molecular systems.

Neural quantum states (NQS) provide flexible and compact wave-function parametrizations for numerical studies of quantum many-body physics. In particular, NQS aim to circumvent the exponential scaling of the Hilbert space by compressing quantum many-body wave functions with a tractable amount of parameters. While inspired by deep learning, it remains unclear to what extent NQS share characteristics with neural networks used for standard machine learning tasks. We demonstrate that, in a simplified supervised setting, NQS exhibit the double descent phenomenon, a key feature of modern deep learning, where generalization worsens as network size increases before improving again in an overparameterized regime. Notably, we find the second descent to occur only for network sizes much larger than the Hilbert space dimension, i.e., network sizes that are out of reach for problems of practical interest. Within our setting, this observation places typical NQS in the underparameterized regime. We also observe that the optimal network size in the underparameterized regime depends on the number of unique training samples. While the double descent phenomenon does indeed translate to the NQS setting, potential practical consequences of our findings point more toward the need for symmetry-aware, physics-informed architecture design, rather than directly adopting machine learning heuristics.

We study the dynamics of domain growth when multipole moments of the order parameter are conserved. Following a quench into the ordered phase of the Ising model, the typical size of domains grows with time as $R\left(t\right)\sim t^{1/2}$ in the absence of conserved quantities. When the order parameter is conserved, the domain growth slows to $R\left(t\right)\sim t^{1/3}$. Conservation of higher moments of the order parameter fundamentally modifies this behavior: Coarsening proceeds via anomalously slow growth. We analytically and numerically show that conservation of the $m\mathrm{th}$ multipole moment causes domains to grow as $R\left(t\right)\sim t^{1/(2m+3)}$. This cascade of dynamical critical exponents characterizes a new family of nonequilibrium universality classes for fractonic systems.

We investigate the transition to synchronization in adaptive multilayer networks with higher-order interactions, both analytically and numerically, in the presence of phase frustration ($\beta$). The higher-order topology consists of pairwise and triadic couplings. The analytical framework for the investigation is based on the Ott-Antonsen ansatz, which leads to a convenient low-dimensional model. Extensive bifurcation analysis of the low-dimensional model and the numerical simulation of the full networks are performed to explore the paths to synchronization. The combined analysis shows a complex dependence of the transition to synchronization on adaptation exponents, coupling strengths, phase-lag parameter, and multilayer configuration. Various types of transitions to synchronization, namely continuous, tiered, and explosive, are exhibited by the system in different regions of the parameter space. In all the cases, a satisfactory match between the low-dimensional model and the numerical simulation results is observed. The origin of different transitions to synchronization is clearly understood using the low-dimensional model. Exploration of a wide region of the parameter space suggests that the phase frustration parameter inhibits tiered as well as explosive synchronization transitions for fixed triadic coupling strength ($K_2$). On the other hand, discontinuous transition is promoted by the phase frustration parameter for fixed pairwise coupling strength ($K_1$). Moreover, the exponent of the adaptation function with the pairwise coupling decreases the width of the hysteresis, despite the dominance of the higher-order coupling for fixed $\beta$ and $K_2$. While the exponent of the function adapted with higher-order coupling shows the opposite effect, it promotes bistability in spite of dominance of pairwise coupling strength for fixed $\beta$ and $K_1$.

This paper presents an analytical model for the emergence of reflexively autocatalytic and food-generated (RAF) sets in Kauffman networks. The sharp transition in the probability of observing RAF sets as a function of catalytic probability is a well-documented but unexplained phenomenon. In this paper, we show that RAF formation proceeds through two regimes: a finite-size regime dominated by the probabilistic occurrence of minimal catalytic cores, and an asymptotic regime modeled by the disparity between the number of ways to select $k$ unique molecule-reaction pairs and the number of ways to allocate minimally RAF sets among them. This perspective on RAF theory offers new insights into the governing mechanics of autocatalytic emergence in Kauffman networks, particularly emphasizing the distinction between contributions arising from the underlying network structure and those from the catalytic model. The presented theory is applicable across a range of catalytic schemes, allowing for tractable analysis and a comparison of emergence behavior across a broad class of systems. The analytic description of this theory additionally provides a quantitative measurement of transition sharpness and predicts the scaling behavior of catalytic cores proportional to $n{2}^{n+1}$. Although this model advances the understanding of RAF set formation, it relies on the simplifying assumption of nonoverlapping catalytic cores. Future work should incorporate overlap probabilities and information on RAF core size distributions, and refine the restricted partition function to more accurately capture the behavior of RAF emergence.

In this paper, we explore the features of a genetic network where the transcription factors, namely, activators and repressors bind to the promoter in a competitive way. We have developed an analytical method to find the most probable set of parameter values that are unavailable in experiments. We study the behavior of intrinsic noise of the circuit and compare the profile with the network where the activator and the repressor bind the promoter noncompetitively. We observe that the noise found in the super-Poissonian region of the competitive genetic circuit is higher than the noise obtained in the same from a noncompetitive one. We further notice that, due to the effect of transcriptional reinitiation in the presence of the activator and repressor molecules, there exist some anomalous characteristic features in the mean expressions and noise profiles. On top of that, we find low noise in the transcriptional level and high noise in the translational level in presence of reinitiation than in absence of the same. In addition, we find out the method to reduce the noise further below the Poissonian level in competitive circuit than the noncompetitive one with the help of some noise-reducing factors. We also notice that the effect of extrinsic noise due to the environmental fluctuation on the competitive activator-repressor system is insignificant in the low noise limit.

The generation of skin friction in a spatially evolving turbulent boundary layer at a Mach number ${\text{Ma}}_{\infty }=2.9$ is analyzed using direct numerical simulation (DNS). Particular attention is paid to clarifying the impact of the turbulent/nonturbulent interface (TNTI) height on the skin friction. In contrast to the RD identity N. Renard et al. [J. Fluid Mech. 790, 339 (2016)] and its extension to compressible flow W. P. Li et al. [J. Fluid Mech. 875, 101 (2019)] we decompose the instantaneous skin friction into four physics-informed contributions. Taking advantage of this novel decomposition method, the conditional statistics contributing to the skin friction near the TNTI can be analyzed. The instantaneous height of the TNTI follows a Gaussian distribution. At a moderate Reynolds number, the height of the TNTI shows no statistical correlation with the skin friction. More specifically, in the vicinity of the TNTI, the contributions of the viscous dissipation and the spanwise inhomogeneity are close to zero. Both the material derivative term and the streamwise inhomogeneity term are mainly determined by the production of the pressure gradient and the streamwise velocity, but these two terms approximately counterbalance each other. This numerical study contributes to understanding the generation of the skin friction in a supersonic turbulent boundary layer, which is crucial in controlling aerodynamics drag in aerospace engineering.