Physical Review E最新文献

The weakly asymmetric exclusion process (WASEP) in one dimension is a paradigmatic system of interacting particles described by the macroscopic fluctuation theory (MFT) in the presence of driving. We consider an initial condition with densities ϱ_{1},ϱ_{2} on either side of the origin, so that for ϱ_{1}=ϱ_{2} the gas is stationary. Starting from the microscopic description, we obtain exact formulas for the cumulant generating functions and large deviation rate functions of the time-integrated current and the position of a tracer. As the asymmetry/driving is increased, these describe the crossover between the symmetric exclusion process and the weak-noise regime of the Kardar-Parisi-Zhang (KPZ) equation: we recover the two limits and describe the crossover from the WASEP cubic tail to the 5/2 and 3/2 KPZ tail exponents. Finally, we show that the MFT of the WASEP is classically integrable, by exhibiting the explicit Lax pairs, which are obtained through a novel mapping between the MFT of the WASEP and a complex extension of the classical anisotropic Landau-Lifshitz spin chain. This shows integrability of all MFTs of asymmetric models with quadratic mobility as well as their dual versions.

Transmission through optical fibers offers ultrafast and long-haul communications. However, the search for its ultimate capacity limits in the presence of distributed amplifier noise is complicated by the competition between wave dispersion and nonlinearity. In this paper, we exploit the integrability of the nonlinear Schrödinger equation, which accurately models optical fiber communications, to derive an expression for the mutual information of an optical fiber communications channel, expressed fully in the scattering data domain of the nonlinear Fourier transform and valid in the limit of low amplifier noise, which when optimized with respect to the input provides the corresponding capacity. We utilize the relationship between the derived noise-covariance operator and the Jacobian of the mapping between the signal and the scattering data to obtain the properties of the former. Emerging from the structure of the covariance operator is the significance of the Gordon-Haus effect in moderating and finally reversing the increase of the mutual information with power. This effect is showcased in numerical simulations for Gaussian input in the high-bandwidth regime.

Stochastic resetting is a driving mechanism that is known to minimize the first passage time to reach a target at the cost of energy expenditure. The choice of the physical implementation of each resetting event determines the trade-off between the acceleration of the search process and its energetic cost. Here we use an optimal transport protocol that balances the duration and the energetic cost of each resetting event. This protocol drives a harmonically trapped Brownian particle between two equilibrium states within a finite time and with minimal energetic cost. An explicit comparison with other types of finite-time protocols further shows its specific thermodynamic properties. Its cost is both a lower bound on the cost of unoptimized shortcut protocols and an upper bound on the cost of optimal protocols which do not ensure final equilibrium. When applying the optimal transport protocol to implement stochastic resetting, a single lower time-energy bound is reached: This protocol allows to reach the best trade-off between energetic cost and search time.

We study the out-of-equilibrium scaling behavior of two-dimensional and three-dimensional Ising systems, when they are slowly driven across their magnetic first-order transitions at low temperature T0 of the magnetic field, which decrease as h_{*}∼1/(lnt_{s})^{κ}, with κ=2 and κ=1 in two and three dimensions, respectively, for t_{s}→∞. We identify σ≡t(lnt)^{κ}/t_{s} as the relevant scaling variable associated with the KZ dynamics in the TL.

Spatiotemporal chaos in systems such as turbulent flows often resides on finite-dimensional attractors, enabling the construction of reduced-order models. Unfortunately, as the dimension of these attractors increases it becomes more difficult to train reduced-order models because more data are needed to sample states of the system. For example, the attractor dimension often scales linearly with the domain size in a single direction for large-scale spatially extended systems, thus we need methods for decomposing these systems to overcome the burden of increasing dimensionality. Here, we develop a framework that constructs local reduced-order models by decomposing spatially extended systems into patches. Each patch uses autoencoders for dimension reduction and neural ordinary differential equations for learning the temporal dynamics locally. We apply this framework to the Kuramoto-Sivashinsky equation and two-dimensional Kolmogorov flow. Our approach reduces the dimension by up to two orders of magnitude while accurately capturing both short-term dynamics and long-term statistics. This framework is applicable to any dissipative partial differential equations, thereby offering broad implications for a wide range of physical and engineering systems.

While quantum measurements have been shown to constitute a resource for operating quantum thermal machines, the nature of the energy exchanges involved in the interaction between system and measuring apparatus is still under debate. In this work, we show that a microscopic model of the apparatus is necessary to unambiguously determine whether quantum measurements provide energy in the form of heat or work. We illustrate this result by considering a measurement-based refrigerator, made of a double quantum dot embedded in a two-terminal device, with the charge of one of the dots being continuously monitored. Tuning the parameters of the measurement device interpolates between heat- and work-fueled regimes with very different thermodynamic efficiency. Notably, we demonstrate a trade-off between a maximal thermodynamic efficiency when the measurement-based refrigerator is fueled by heat and a maximal measurement efficiency quantified by the signal-to-noise ratio in the work-fueled regime. Our analysis sheds light on the nature of the energy exchanges occurring during a quantum measurement, paving the way for energy optimization in quantum protocols and quantum machines.

The use of statistical physics models to investigate real-world networks and reveal their underlying dynamics has shown promising results and acquired increasing attention. Here, we show how exponential random-graph (ERG) models can be suitably adopted to characterize how Alzheimer's disease (AD) affects brain connectivity. Magnetic-resonance imaging (MRI) of the brain was used to define a brain connectivity network whose nodes are the different brain regions, and the links indicate the pairwise structural relationships. Based on T1-weighted MRI brain scans of 126 normal controls (NC) and 92 AD patients, ERGs were able to outline both "global" and "local" disease patterns. Our findings demonstrate that ERGs accurately highlight how AD affects brain connectivity reaching an overall classification accuracy of 0.82±0.08. Besides, ERGs outline which regions of the brain are the most affected by the disease, thus proving to be a formidable instrument also to investigate the disease pathological mechanisms; more importantly, as these effects are evaluated at patient level, they can be exploited to design innovative diagnosis support systems or to provide a novel explainable framework for decision support systems. Finally, thanks to its generality, the approach proposed in this study paves the way for further applications and investigations inquiring into the use of ERGs for other diseases and different data sources or the use of alternative models.

We investigate the transport behavior of tungsten impurities with finite inertia in drift-wave turbulence using the Hasegawa-Wakatani model. Unlike previous tracer-based models, our simulations reveal a transition to nondiffusive dynamics for a range of charge states. This mechanism offers a turbulence-driven route to core impurity accumulation. This finding underscores the nontrivial role of particle inertia in impurity dynamics and has strong implications for impurity control in future fusion devices such as ITER.

A single droplet can be effectively isolated from a capillary liquid jet by applying a short-duration velocity oscillatory pulse at its exit from the nozzle outlet. The previous temporal analysis of F. J. García et al. [Phys. Rev. E 100, 053111 (2019)2470-004510.1103/PhysRevE.100.053111], which modeled the jet as an infinite liquid column subjected to a local axisymmetric spatial perturbation, revealed that Gaussian wave packets are the optimal choice for this purpose. Here, a more realistic semi-infinite jet model was selected to confirm the validity of the simpler infinite-jet model and identify new phenomena. To this end, a semi-infinite jet subjected to a symmetrical Gaussian oscillatory pulse of velocity at the outlet was investigated through both a linear spatial analysis and nonlinear numerical simulations. The spatiotemporal evolution of the Gaussian wave packets thus generated was described by fitting their relevant parameters and compared with the prediction of the temporal analysis of an infinite jet. Nonlinear simulations confirmed the results of the linear spatial analysis whenever the amplitude remained sufficiently small. A Gaussian wave packet forms during the stimulation stage, which then moves advectively and disperses as if it were on a perturbed infinite jet, even for small velocities. This explains the parametric agreement between the spatial and temporal analyses, especially for brief stimulation pulses. The most relevant discrepancy was a phase shift in the carrier wave, whose magnitude can be explained in terms of the stimulation and initial-transient characteristic times. It could be reverted through an appropriate initial phase of the stimulation pulse. An example with symmetrical breakup pattern was achieved in this way, which could be useful for further isolation of single droplets.

Gap solitons (GSs) bifurcating from flat bands, which may be represented in terms of Wannier functions, have garnered significant interest due to their strong localization with extremely small norms. Moiré lattices (MLs), with multiple flat bands, offer an appropriate platform for creating such solitons. We explore the formation mechanism and stability of GSs in spin-1 Bose-Einstein condensates under the combined action of the Rashba spin-orbit coupling (SOC) and an ML potential. We identify five Wannier-type GS families bifurcating from the lowest five energy bands in the spectrum induced by the ML with sufficiently large period and depth. These fundamental GSs serve as basic elements for constructing more complex Wannier-type GS states. Reducing the lattice period and depth triggers a transition from the Wannier-type GSs to those of the Bloch type, the latter exhibiting higher norm thresholds and pronounced spatial broadening near edges of the energy bands. In addition to tuning the lattice-potential parameters, adjusting the SOC strength can also modulate the flatness of energy bands and enhance the localization of gap solitons, enabling reversible transitions between the GSs of the Wannier and Bloch types. Distinctive properties of GSs in the quasiperiodic ML are uncovered too. Thus, we propose the theoretical foundation for the creation of and manipulations with strongly localized GSs.