The demographic (shot) noise in population dynamics scales with the square root of the population size. This process is very important, as it yields an absorbing state at zero field, but simulating it, especially on spatial domains, is a nontrivial task. Here, we analyze two similar methods that were suggested for simulating the corresponding Langevin equation, one by Pechenik and Levine and the other by Dornic, Chaté, and Muñoz (DCM). These methods are based on operator-splitting techniques and the essential difference between them lies in which terms are bundled together in the splitting process. Both these methods are first order in the time step so one may expect that their performance will be similar. We find, surprisingly, that when simulating the stochastic Ginzburg-Landau equation with two deterministic metastable states, the DCM method exhibits two anomalous behaviors. First, the stochastic stall point moves away from its deterministic counterpart, the Maxwell point, when decreasing the noise. Second, the errors induced by the finite time step are larger by a significant factor (i.e., $>10\times$) in the DCM method. We show that both these behaviors are the result of a finite-time-step induced shift in the deterministic Maxwell point in the DCM method, due to the particular operator splitting employed. In light of these results, care must be exercised when computing quantities like phase-transition boundaries (as opposed to universal quantities such as critical exponents) in such stochastic spatial systems.

We study the evolution of an open quantum system described by a dynamical semigroup having the Lindblad superoperator as a generator. This generator may have an eigenfunction with a zero eigenvalue referred to as a constant of motion (COM). An open quantum system has a unique stationary state if and only if it has no COMs. A system with multiple stationary states has a basis of COMs, any COM of the system is a linear combination of the basis COMs. The basis divides the space of system states into subspaces. In each subspace, its own stationary state is formed, and any stationary state of the system is a linear combination of these states. Usually, neither the basis of COMs nor even their number is known. We demonstrate that finding the stationary state of the system does not require looking for COMs. Instead, one can construct a set of “invariant” subspaces. If the system evolution begins from one of these subspaces, the system will remain in it, arriving at a stationary state independent of evolution in other subspaces. We suggest a direct way of finding the invariant subspaces by studying the evolution of the system. We show that the sets of invariant subspaces and subspaces generated by the basis of COMs are equivalent. A stationary state of the system is a weighted sum of stationary states in each invariant subspace; weighting factors are determined by the initial state of the system.

Efficiency at maximum power has been investigated extensively, yet the practical control scheme to achieve it remains elusive. We fill this gap with a stepwise Carnot-like cycle, which consists of the discrete isothermal process (DIP) and adiabatic process. With DIP, we validate the widely adopted assumption of the $\mathcal{C}/t$ relation of the irreversible entropy generation $S^{\left(\mathrm{ir}\right)}$ and show the explicit dependence of the coefficient $\mathcal{C}$ on the fluctuation of the speed of tuning energy levels as well as the microscopic coupling constants to the heat baths. Such a dependence allows us to control the irreversible entropy generation by choosing specific control schemes. We further demonstrate the achievable efficiency at maximum power and the corresponding control scheme with the simple two-level system. Our current work opens new avenues for an experimental test, which was not feasible due to the lack the of the practical control scheme in the previous low-dissipation model or its equivalents.

How ergodic is diffusion under harmonic confinements? How strongly do ensemble- and time-averaged displacements differ for a thermally-agitated particle performing confined motion for different initial conditions? We here study these questions for the generic Ornstein-Uhlenbeck (OU) process and derive the analytical expressions for the second and fourth moment. These quantifiers are particularly relevant for the increasing number of single-particle tracking experiments using optical traps. For a fixed starting position, we discuss the definitions underlying the ensemble averages. We also quantify effects of equilibrium and nonequilibrium initial particle distributions onto the relaxation properties and emerging nonequivalence of the ensemble- and time-averaged displacements (even in the limit of long trajectories). We derive analytical expressions for the ergodicity breaking parameter quantifying the amplitude scatter of individual time-averaged trajectories, both for equilibrium and out-of-equilibrium initial particle positions, in the entire range of lag times. Our analytical predictions are in excellent agreement with results of computer simulations of the Langevin equation in a parabolic potential. We also examine the validity of the Einstein relation for the ensemble- and time-averaged moments of the OU-particle. Some physical systems, in which the relaxation and nonergodic features we unveiled may be observable, are discussed.

We calculate the critical exponent $\eta$ of the $D$-dimensional Ising model from a simple truncation of the functional renormalization group flow equations for a scalar field theory with long-range interaction. Our approach relies on the smallness of the inverse range of the interaction and on the assumption that the Ginzburg momentum defining the width of the scaling regime in momentum space is larger than the scale where the renormalized interaction crosses over from long range to short range; the numerical value of $\eta$ can then be estimated by stopping the renormalization group flow at this scale. In three dimensions our result $\eta =0.03651$ is in good agreement with recent conformal bootstrap and Monte Carlo calculations. We extend our calculations to fractional dimensions $D$ and obtain the resulting critical exponent $\eta \left(D\right)$ between two and four dimensions. For dimensions $2\le D\le 3$ our result for $\eta$ is consistent with previous calculations.

An observability condition number is defined for physical systems modeled by network dynamics. Assuming that the dynamical equations of the network are known and a noisy trajectory is observed at a subset of the nodes, we calculate the expected distance to the nearest correct trajectory as a function of the observation noise level and discuss how it varies over the unobserved nodes of the network. When the condition number is sufficiently large, reconstructing the trajectory from observations from the subset will be infeasible. This knowledge can be used to choose an optimal subset from which to observe a network.

We conduct molecular dynamics (MD) simulations and develop scaling laws to quantify the lubrication behavior of weakly interpenetrated polymer brush bilayers in the presence of an external shear force. The weakly interpenetrated regime is characterized by $1<d_g/d_0<2$, where $d_g$ is the gap between the opposing surfaces (where the brushes are grafted) and $d_0$ is the unperturbed brush height. MD simulations predict that in the shear thinning regime, characterized by a larger shear force or a large Weissenberg number ($W$), $R_g^2\sim W^{0.19}$ and $\eta \sim W^{-0.38}$, where $R_g$ is the chain extension in the direction of the shear and $\eta$ is the viscosity. These scaling behaviors, which are distinctly different from that witnessed in strongly compressed regime (for such a regime, characterized by $d_g/d_0<1$, $R_g^2\sim W^{0.53}$, and $\eta \sim W^{-0.46}$), match excellently with those predicted by our scaling theory.

Shear banding is widely observed in natural fault zones as well as in laboratory experiments on granular materials. Understanding the dynamics of strain localization under different loading conditions is essential for quantifying strength evolution of fault gouge and energy partitioning during earthquakes and characterizing rheological transitions and fault zone structure changes. To that end, we develop a physics-based continuum model for strain localization in sheared granular materials. The grain-scale dynamics is described by the shear transformation zone (STZ) theory, a nonequilibrium statistical thermodynamic framework for viscoplastic deformation in amorphous materials. Using a finite strain computational framework, we investigate the initiation and growth of complex shear bands under a variety of loading conditions and identify implications for strength evolution and the ductile to brittle transition. Our numerical results show similar localization patterns to field and laboratory observations and suggest that shear zones show more ductile response at higher confining pressures, lower dilatancy, and loose initial conditions. Lower pressures, higher dilatancy, and dense initial conditions favor a brittle response and larger strength drops. These findings shed light on a range of mechanisms for strength evolution in dry sheared granular materials and provide a critical input to physics-based multiscale models of fault zone instabilities.

In this study, we propose a universal scenario explaining the $1/f$ fluctuation, including pink noises, in Hamiltonian dynamical systems with many degrees of freedom under long-range interaction. In the thermodynamic limit, the dynamics of such systems can be described by the Vlasov equation, which has an infinite number of Casimir invariants. In a finite system, they become pseudoinvariants, which yield quasistationary states. The dynamics then exhibit slow motion over them, up to the timescale where the pseudo-Casimir-invariants are effective. Such long-time correlation leads to $1/f$ fluctuations of collective variables, as is confirmed by direct numerical simulations. The universality of this collective $1/f$ fluctuation is demonstrated by taking a variety of Hamiltonians and changing the range of interaction and number of particles.

The mechanisms underlying cascading failures are often modeled via the paradigm of self-organized criticality. Here we introduce a simple network model where nodes self-organize to be either weakly or strongly protected against failure in a manner that captures the trade-off between degradation and reinforcement of nodes inherent in many network systems. If strong nodes cannot fail, any failure is contained to a single, isolated cluster of weak nodes and the model produces power-law distributions of failure sizes. We classify the large, rare events that involve the failure of only a single cluster as “black swans.” In contrast, if strong nodes fail once a sufficient fraction of their neighbors fail, then failure can cascade across multiple clusters of weak nodes. If over 99.9% of the nodes fail due to this cluster hopping mechanism, we classify this as a “dragon king,” which are massive failures caused by mechanisms distinct from smaller failures. The dragon kings observed are self-organized, existing over a wide range of reinforcement rates and system sizes. We find that once an initial cluster of failing weak nodes is above a critical size, the dragon king mechanism kicks in, leading to piggybacking system-wide failures. We demonstrate that the size of the initial failed weak cluster predicts the likelihood of a dragon king event with high accuracy and we develop a simple control strategy that can dramatically reduce dragon kings and other large failures.

We develop a prototypical stochastic model for a local search around a given home. The stochastic dynamic model is motivated by experimental findings of the motion of a fruit fly around a given spot of food but will generally describe the local search behavior. The local search consists of a sequence of two epochs. In the first the searcher explores new space around the home, whereas it returns to the home during the second epoch. In the proposed two-dimensional model both tasks are described by the same stochastic dynamics. The searcher moves with constant speed and its angular dynamics is driven by a symmetric $\alpha$-stable noise source. The latter stands for the uncertainty to decide the new direction of motion. The main ingredient of the model is the nonlinear interaction dynamics of the searcher with its home. In order to determine the new heading direction, the searcher has to know the actual angles of its position to the home and of the heading vector. A bound state to the home is realized by a permanent switch of a repulsive and attractive forcing of the heading direction from the position direction corresponding to search and return epochs. Our investigation elucidates the analytic tractability of the deterministic and stochastic dynamics. Noise transforms the conservative deterministic dynamics into a dissipative one of the moments. The noise enables a faster finding of a target distinct from the home with optimal intensity. This optimal situation is related to the noise-dependent relaxation time. It is uniquely defined for all $\alpha$ and distinguishes between the stochastic dynamics before and after its value. For times large compared to this, we derive the corresponding Smoluchowski equation and find diffusive spreading of the searcher in the space. We report on the qualitative agreement with the experimentally observed spatial distribution, noisy oscillatory return times, and spatial autocorrelation function of the fruit fly. However, as a result of its simplicity, the model aims to reproduce the local search behavior of other units during their exploration of surrounding space and their quasiperiodic return to a home.

We study the spectral properties of classical and quantum Markovian processes that are reset at random times to a specific configuration or state with a reset rate that is independent of the current state of the system. We demonstrate that this simple reset dynamics causes a uniform shift in the eigenvalues of the Markov generator, excluding the zero mode corresponding to the stationary state, which has the effect of accelerating or even inducing relaxation to a stationary state. Based on this result, we provide expressions for the stationary state and probability current of the reset process in terms of weighted sums over dynamical modes of the reset-free process. We also discuss the effect of resets on processes that display metastability. We illustrate our results with two classical stochastic processes, the totally asymmetric random walk and the one-dimensional Brownian motion, as well as two quantum models: a particle coherently hopping on a chain and the dissipative transverse field Ising model, known to exhibit metastability.

We present a molecular dynamics simulation of a two-dimensional Carnot engine. The optimization of this engine is achieved through the velocity of the piston, allowing not only the optimization of power output but also some other figures of merit involving entropy production. The maximum power and maximum ecological efficiencies are computed. It is shown that the near ideal gas working substance displays an endoreversible Carnot-like engine behavior. This can be considered as a prove of the validity of the Carnot-like endoreversible model. An effective reversible cycle different than the Carnot one is obtained, in agreement with the endoreversible hypothesis flexibility. We compare the efficiencies stemming from an ideal gas approximation with those of the simulation, and then we propose a suitable approximation to an endoreversible heat engine and to a reversible Joule-Brayton cycle which fits very well to the simulation results. Finally, we show that the maximum ecological efficiency $\eta =1-{\tau }^{3/4}$, which is also very close to the upper bound of the low-dissipation heat engine under maximum ecological (and Omega) conditions, is close for describing the dynamics of the simulated cycle under maximum power and maximum ecological conditions in the so-named heat engine operability region.

Models for the stabilization of an inverted pendulum figure prominently in studies of human balance control. Surprisingly, fluctuations in measures related to the vertical displacement angle for quietly standing adults with eyes closed exhibit chaos. Here we show that small-amplitude chaotic fluctuations (“microchaos”) can be generated by the interplay between three essential components of human neural balance control, namely time-delayed feedback, a sensory dead zone, and frequency-dependent encoding of force. When the sampling frequency of the force encoding is decreased, the sensitivity of the balance control to changes in the initial conditions increases. The sampled, time-delayed nature of the balance control may provide insights into why falls are more common in the very young and the elderly.

Random matrix theory successfully connects the structure of interactions of large ecological communities to their ability to respond to perturbations. One of the most debated aspects of this approach is that so far studies have neglected the role of population abundances on stability. While species abundances are well studied and empirically accessible, studies on stability have so far failed to incorporate this information. Here we tackle this question by explicitly including population abundances in a random matrix framework. We derive an analytical formula that describes the spectrum of a large community matrix for arbitrary feasible species abundance distributions. The emerging picture is remarkably simple: while population abundances affect the rate to return to equilibrium after a perturbation, the stability of large ecosystems is uniquely determined by the interaction matrix. We confirm this result by showing that the likelihood of having a feasible and unstable solution in the Lotka-Volterra system of equations decreases exponentially with the number of species for stable interaction matrices.

We study the shape and shape fluctuations of incompatible, positively curved ribbons, with a flat reference metric and a spherelike reference curvature. Such incompatible geometry is likely to occur in many self-assembled materials and other experimental systems. Ribbons of this geometry exhibit a sharp transition between a rigid ring and an anomalously soft spring as a function of their width. As a result, the temperature dependence of these ribbons' shape is unique, exhibiting a nonmonotonic dependence of the persistence and Kuhn lengths on the temperature and width. We map the possible configuration phase space and show the existence of three phases: At high temperatures it is the ideal chain phase, where the ribbon is well described by classical models (e.g., wormlike chain model). The second phase, for cold and narrow ribbons, is the plane ergodic phase; a ribbon in this phase might be thought of as made out of segments that gyrate within an oblate spheroid with extreme aspect ratio. The third phase, for cold, wide ribbons, is a direct result of the residual stress caused by the incompatibility, called the random structured phase. A ribbon in this phase behaves on large scales as an ideal chain. However, the segments of this chain are not straight; rather they may have different shapes, mainly helices (both left and right handed) of various pitches.

We investigate the fracture of heterogeneous materials occurring under unloading from an initial load. Based on a fiber bundle model of time-dependent fracture, we show that depending on the unloading rate the system has two phases: for rapid unloading the system suffers only partial failure and it has an infinite lifetime, while at slow unloading macroscopic failure occurs in a finite time. The transition between the two phases proved to be analogous to continuous phase transitions. Computer simulations revealed that during unloading the fracture proceeds in bursts of local breakings triggered by slowly accumulating damage. In both phases the time evolution starts with a relaxation of the bursting activity characterized by a universal power-law decay of the burst rate. In the phase of finite lifetime the initial slowdown is followed by an acceleration towards macroscopic failure where the increasing rate of bursts obeys the (inverse) Omori law of earthquakes. We pointed out a strong correlation between the time where the event rate reaches a minimum value and of the lifetime of the system which allows for forecasting of the imminent catastrophic failure.

In amorphous solids, a non-negligible part of thermal conductivity results from phonon scattering on the structural disorder. The conversion of acoustic energy into thermal energy is often measured by the dynamical dtructure factor (DSF) thanks to inelastic neutron or x-ray scattering. The DSF is used to quantify the dispersion relation of phonons, together with their damping. However, the connection of the dynamical structure factor with dynamical attenuation of wave packets in glasses is still a matter of debate. We focus here on the analysis of wave-packet propagation in numerical models of amorphous silicon. We show that the damped harmonic oscillator model fits of the dynamical structure factors give a good estimate of the wave packets mean free path, only below the Ioffe-Regel frequency. Above the Ioffe-Regel frequency and below the mobility edge, a pure diffusive regime without a definite mean free path is observed. The high-frequency mobility edge is characteristic of a transition to localized vibrations. Below the Ioffe-Regel frequency, a mixed regime is evidenced at intermediate frequencies, with a coexistence of propagative and diffusive wave fronts. The transition between these different regimes is analyzed in detail and reveals a complex dynamics for energy transport, thus raising the question of the correct modeling of thermal transport in amorphous materials.

The artificial microswimmer is a cutting-edge technology with applications in drug delivery and micro-total-analysis systems. The flow field around a microswimmer can be regarded as Stokes flow, in which reciprocal body deformation cannot induce migration. In this study, we propose a microcapsule swimmer that undergoes amoeboidlike shape deformations under fluid oscillation conditions. This is a study on the propulsion principle using a capsule with a solid membrane, and one of only a few studies using fluid oscillation. The microswimmer consists of an elastic capsule containing fluid and a rigid sphere. Opposing forces are generated when fluid oscillations are applied, because the densities of the internal fluid and sphere are different. The opposing forces induce nonreciprocal body deformation, which leads to migration of the microswimmer under Stokes flow conditions. Using numerical simulations, we found that the microswimmer propels itself in one of two modes, i.e., stroke swimming or drag swimming. We discuss the feasibility of the proposed microswimmer and show that the most efficient swimmer can migrate tens of micrometers per second. These findings pave the way for future artificial microswimmer designs.

A systematic numerical study of wake potential and ion density distribution of a single grain in flowing ions under the influence of a magnetic field applied along the flow is presented. A strong magnetic field introduces ion focus depletion behind grains, facilitating the entrance of electrons far in the downstream towards the grain. It is shown that the magnetic field suppresses the amplitude of the wake potential and modifies the ion density distribution substantially. The wake peak potential and position characteristics and density distribution of plasma constituents in the presence of a magnetic field and charge-exchange collisions for the subsonic, sonic, and supersonic regime are also delineated. In the subsonic regime, simulations demonstrate the accumulation of ions near the dust grain in the transverse direction, while complete suppression of oscillations in the transverse direction takes place for the sonic and supersonic regime.