Physical Review E最新文献

The Liang-Kleeman (LK) information flow provides a powerful framework for quantifying causality among variables, while linear inverse modeling (LIM) offers an effective empirical approach to studying the dynamical evolution of system states from input data. In this study, we unify these two concepts by proposing the LIM-LK framework-a data-driven method for estimating LK information flow from input data using LIM. Beyond capturing causality among state variables, the proposed framework establishes a direct connection between causality and system dynamics, and also enables the quantification of entropy transfer from the ambient environment to the system through both memoryless and persistent stochastic forcing. The effectiveness of this unified approach is demonstrated through an application to the interaction between the Pacific and Indian Oceans, offering both causal and dynamical insight into ocean variability and its seasonal modulation.

As engineering advances toward the nanoscale, understanding design principles for molecular motors becomes increasingly valuable. Many molecular motors consist of coupled components transducing one free-energy source into another. Here, we study the performance of coupled rotary molecular motors with different rotational symmetries under constant and scaling driving forces. Under constant driving and strong coupling, symmetry match between the motors decreases the output power. In contrast, under a scaling driving force, the output power is not sensitive to symmetries. However, driving the upstream motor too strongly reduces the downstream motor's output power, leading to a perhaps counterintuitive phenomenon we term disruption, in which the two motors become disconnected. Across both driving schemes, output power peaks at intermediate coupling, confirming the value of flexible coupling. Beyond providing insights into biological motors, these findings could inform the future design of synthetic nanomotors and structure-based drugs.

In this work, the closed Frank model was simulated using a stochastic method based on the Ehrenfest urn on a surface (mathematically expressed as a matrix), considering that the reactions and diffusion processes of the species occur within a predetermined, adjusted neighborhood. It investigated the role of neighborhood size in achieving (or not) the global homochiral state (GHS). All simulations started without enantiomeric excess, with the particles randomly distributed over the surface, and other distributions were also considered. It is shown that, spatially, the surface gradually becomes occupied by domains (of the two enantiomers) that are randomly distributed, and as the size of the neighborhood increases, the number of simulations that reach the GHS also increases, with a corresponding decrease in the simulation time required to reach it. Besides, domains continue to form in this case, but over time, fluctuations (in space and time) in concentrations lead to GHS. When the GHS is not reached, the steady state is characterized by the presence of domains of both species on the surface, as previously mentioned. Still, the reactions continue to occur, and the generation and consumption rates of each species are equal. These occur mainly on the interface of the domains, especially in small neighborhood sizes.

Autochemotaxis, the directed movement of cells along gradients in chemicals they secrete, is central to the formation of complex spatiotemporal patterns in biological systems. Since the introduction of the Keller-Segel model, numerous variants have been analyzed, revealing phenomena such as coarsening of aggregates, stable aggregate sizes, and spatiotemporally chaotic dynamics. Here we consider general mass-conserving Keller-Segel models, that is, models without cell growth and death, and analyze the generic long-time dynamics of the chemotactic aggregates. Building on and extending our previous work, which demonstrated that chemotactic aggregation can be understood through a generalized Maxwell construction balancing density fluxes and reactive turnover, we use singular perturbation theory to derive the rates of mass competition between well-separated aggregates. We analyze how this mass-competition process drives coarsening in both diffusion- and reaction-limited regimes, with the diffusion-limited rate aligning with our previous quasi-steady-state analyses. Our results generalize earlier mathematical findings, demonstrating that coarsening is driven by self-amplifying mass transport and aggregate coalescence. Additionally, we provide a linear stability analysis of the lateral instability, predicting it through a nullcline-slope criterion that parallels the curvature criterion in spinodal decomposition. Overall, our findings suggest that chemotactic aggregates behave similarly to phase-separating droplets, providing a robust framework for understanding the coarse-grained dynamics of autochemotactic cell populations and a quantitative basis for comparing chemotactic coarsening to canonical nonequilibrium phase separation.

Causal emergence (CE) based on effective information (EI) demonstrates that macrostates can exhibit stronger causal effects than microstates in dynamics. However, the identification of CE and the maximization of EI both rely on coarse-graining strategies, which is a key challenge. A recently proposed CE framework based on approximate dynamical reversibility, utilizing singular-value decomposition (SVD), is independent of coarse-graining. Still, it is limited to transition probability matrices in discrete states. To address this, this article proposes a CE quantification framework for Gaussian iterative systems, based on approximate dynamical reversibility derived from the SVD of inverse covariance matrices in forward and backward dynamics. The positive correlation between SVD-based and EI-based CE, along with the equivalence condition, is given analytically. After that, we provide precise coarse-graining strategies directly from singular-value spectra and orthogonal matrices. This new framework can be applied to any dynamical system with continuous states and Gaussian noise, such as autoregressive growth models, Markov-Gaussian systems, and even SIR modeling using neural networks. Numerical simulations on typical cases validate our theory and offer a new approach to studying the CE phenomenon, emphasizing noise and covariance over dynamical functions in both known models and machine learning.

Advancements in noisy intermediate-scale quantum (NISQ) computing are steadily pushing these systems toward outperforming classical supercomputers on specific well-defined computational tasks. In this work we explore and control quantum chaos in NISQ systems using discrete-time quantum walks (DTQWs) on cyclic graphs. To efficiently implement quantum walks on NISQ hardware, we employ the quantum Fourier transform to diagonalize the conditional shift operator, optimizing circuit depth and fidelity. We experimentally realize the transition from quantum chaos to order via DTQW dynamics on both odd and even cyclic graphs, specifically 3- and 4-cycle graphs, using the counterintuitive Parrondo paradox strategy across three different NISQ devices. While the 4-cycle graphs exhibit high-fidelity quantum evolution, the 3-cycle implementation shows significant fidelity improvement when augmented with dynamical decoupling pulses. Our results demonstrate a practical approach to probing and harnessing controlled chaotic dynamics on real quantum hardware, laying the groundwork for future quantum algorithms and cryptographic protocols based on quantum walks.

A scenario frequently encountered in real-world complex systems is the temporary failure of a few components. For spatially extended chaotic systems whose functionality hinges on the collective dynamics of the interacting components, a viable approach to dealing with the failures is replacing the malfunctioning components with their backups, so that the collective dynamics of the systems can be precisely maintained for a duration long enough to resolve the problem. Here, taking the chaotic dynamics of the paradigmatic Kuramoto model as an example and considering the scenario of oscillator failures, we propose substituting the failed oscillators with digital twins trained by the data measured from the system evolution. Specifically, leveraging the technique of an adaptable reservoir computer (RC) in machine learning, we demonstrate that a single, small-size RC can substitute any oscillator in the Kuramoto model such that the time evolution of the synchronization order parameter of the repaired system is identical to that of the original system for a certain time period. The performance of adaptable RC is evaluated in various contexts, and it is found that the sustaining period is influenced by multiple factors, including the size of the training dataset, the overall coupling strength of the system, and the number of substituted oscillators. Additionally, though the synchronization order parameter diverges from the ground truth in the long-term running, the functional networks of the oscillators are still faithfully sustained by the machine substitutions.

An experiment was performed on the OMEGA EP Laser System to investigate the effects of magnetic fields on laser imprint. A 30 µm thick CH target was driven by a single beam delivering 3.0 kJ of UV energy in a 5 ns square pulse without smoothing by spectral dispersion. The Rayleigh-Taylor amplification of this beam's imprint on the target surface was monitored using face-on gated x-ray radiography from a Gd backlighter. Magnetic fields of up to 45T were applied normal to the target surface. Analysis of the resulting radiographs shows a 60±13% increase in the spectrally resolved surface perturbation amplitudes, consistent at all times and for all unsaturated frequencies. This consistency indicates that the increase in perturbation amplitudes was due to a change in the initial amplitudes rather than Rayleigh-Taylor growth. This is supported by the trajectory of individual modes and the inferred time-averaged perturbation growth rates. Laser imprint is therefore inferred to have increased due to strong magnetic fields that remain in the conduction zone of the target, suppressing off-axis electron motion and limiting the effects of thermal smoothing.

The Cahn-Hilliard equation, as a classical diffusion-interface method of phase field, has been extensively employed for simulating two-phase fluid dynamics. However, it suffers from a key challenge in the simulation process, specifically the volume conservation of each phase cannot be guaranteed. To address this issue, in this paper, a modified Cahn-Hilliard equation for two-phase flow modeling is first introduced, and the basic idea of this model lies in that it combines the profile correction method with the level-set approach, and thus, it effectively improves the deficiency of the classical Cahn-Hilliard equation in terms of volume nonconservation of each phase. Based on this modified Cahn-Hilliard equation, we further propose an accurate interface-capturing lattice Boltzmann model. After that, we perform a range of numerical simulations, including two stationary droplets immersed in the gas phase, single vortex, Rayleigh-Plateau fluid instability, and droplet deformation under a shear flow. These simulations illustrate that the proposed lattice Boltzmann model has superior performance in maintaining local volume conservation and accurately capturing interfaces. More importantly, compared to the lattice Boltzmann model derived from the classical Cahn-Hilliard equation, it not only achieves more precise volume conservation for each phase but also provides a more consistent representation of the droplet's interface morphology more consistently, especially in dealing with small droplet problems.

We propose and experimentally demonstrate the quasi-superposition quantum-inspired system (QSQS)-a conceptual quantum system for randomness generation built on measuring two conjugate observables of a permutation sorting process: the deterministic permutation count n_{p} and the fundamentally nondeterministic sorting time t. By analogy with quantum systems, these observables are linked by an uncertainty-like constraint: algorithmic determinism ensures structural uniformity, while system-level fluctuations introduce irreducible unpredictability. We realize this framework concretely as a quantum permutation pad (QPP) random number generator (RNG) or QPP-RNG, a system-embedded, software-based true random number generator (TRNG). In QPP-RNG, real-time measurements of sorting time t-shaped by CPU pipeline jitter, cache latency, and OS scheduling-dynamically reseed the pseudorandom RNG, driving the permutation sequence. This design fuses deterministic and nondeterministic components, so that entropy emerges organically from the quasisuperposition structure of the system. Crucially, the QSQS transforms initially right-skewed raw distributions of n_{p} and t into nearly uniform outputs after modulo reduction. This effect arises from the system's internal degeneracies: many distinct internal states collapse into the same output symbol, effectively flattening biases and filling out the output space. This transformation from biased measurements to uniform randomness is the core principle of the QSQS. Empirical results show that as the repetition factor m increases, output entropy converges toward theoretical maxima: Shannon and NIST SP 800-90B min-entropy values approach 8 bits, chi-squared statistics stabilize near ideal uniformity, and bell curve plots visually confirm the flattening from skewed to uniform distributions. The convergence to uniformity occurs at a rate inversely proportional to the size of the permutation space, making the system both scalable and theoretically grounded. Beyond practical implications, our findings illustrate how the QSQS unifies deterministic algorithmic processes with nondeterministic physical fluctuations in a single framework, offering a physics-based perspective for engineering randomness. In the quantum-safe era, the QPP-RNG can close the entropy gap by embedding true randomness directly into cryptographic modules, reducing reliance on external entropy sources and enabling entropy-rich, self-contained postquantum cryptographic ecosystems.