While the propagation of shocks through monoatomic liquids and solids is now well understood, the response of macromolecular systems to shock compression remains far less studied. Here we use molecular dynamics simulations to study the shock compression of diblock copolymers assembled in a lamellae morphology, which may display outstanding ballistic performance. For the first time, we show that the morphologies observed after the passage of the shock resemble those observed at equilibrium, at a temperature dictated by the compression velocity. In copolymers, shock compression leads to a decrease in the lamellae period, favoring the mixing of the polymer blocks, such that strongly segregated initial morphologies evolve into less segregated phases after the passage of the shock, or can even melt into an isotropic phase for strong shocks.

We have studied the kinetic roughening behavior of the fronts of coffee-ring aggregates via extensive numerical simulations of the off-lattice model considered for this context [Dias et al., Soft Matter 14, 1903 (2018)]. This model describes ballistic aggregation of patchy colloids and depends on a parameter $r_{\mathrm{AB}}$ which controls the affinity of the two patches, A and B. Suitable boundary conditions allow us to elucidate a discontinuous pinning-depinning transition at $r_{\mathrm{AB}}=0$, with the front displaying intrinsic anomalous scaling, but with unusual exponent values $\alpha \simeq 1.2, {\alpha }_{\mathrm{loc}}\simeq 0.5, \beta \simeq 1$, and $z\simeq 1.2$. For $0<r_{\mathrm{AB}}\le 1$, comparison with simulations of standard off-lattice ballistic deposition indicates the occurrence of a morphological instability induced by the patch structure. As a result, we find that the asymptotic morphological behavior is dominated by macroscopic shapes. The intermediate time regime exhibits one-dimensional KPZ exponents for $r_{\mathrm{AB}}>0.01$ and the system suffers a strong crossover dominated by the $r_{\mathrm{AB}}=0$ behavior for $r_{\mathrm{AB}}\le 0.01$. A detailed analysis of correlation functions shows that the aggregate fronts are always in the moving phase for $0<r_{\mathrm{AB}}\le 1$ and that their kinetic roughening behavior is intrinsically anomalous for $r_{\mathrm{AB}}\le 0.01$.

Traditionally, acoustic streaming is assumed to be a steady-state, relatively slow fluid response to passing acoustic waves. This assumption, the so-called slow streaming assumption, was made over a century ago by Lord Rayleigh. It produces a tractable asymptotic perturbation analysis from the nonlinear governing equations, separating the acoustic field from the acoustic streaming that it generates. Unfortunately, this assumption is often invalid in the modern microacoustofluidics context, where the fluid flow and acoustic particle velocities are comparable. Despite this issue, the assumption is still widely used today, as there is no suitable alternative. We describe a mathematical method to supplant the classic approach and properly treat the spatiotemporal scale disparities present between the acoustics and remaining fluid dynamics. The method is applied in this work to well-known problems of semi-infinite extent defined by the Navier-Stokes equations, and preserves unsteady fluid behavior driven by the acoustic wave. The separation of the governing equations between the fast (acoustic) and slow (hydrodynamic) spatiotemporal scales are shown to naturally arise from the intrinsic properties of the fluid under forcing, not by arbitrary assumption beforehand. Solution of the unsteady streaming field equations provides physical insight into observed temporal evolution of bulk streaming flows that, to date, have not been modeled. A Burgers equation is derived from our method to represent unsteady flow. By then assuming steady flow, a Riccati equation is found to represent it. Solving these equations produces direct, concise insight into the nonlinearity of the acoustic streaming phenomenon alongside an absolute, universal upper bound of 50% for the energy efficiency in transducing acoustic energy input to the acoustic streaming energy output. Rigorous validation with respect to experimental and theoretical results from the classic literature is presented to connect this work to past efforts by many authors.

A methodology is developed to describe time-dependent phenomena associated with nonlocal transport in complex, two-dimensional geometries. It is an extension of the ‘‘iterative method” introduced previously to solve steady-state transport problems [Maggs and Morales, Phys. Rev. E 99, 013307 (2019)], and it is based on the ‘‘jumping particle” concepts associated with the continuous-time random walk (CTRW) model. The method presented explicitly evaluates the time integral contained in the CTRW master equation. A modified version of the Mittag-Leffler function is used for the waiting-time probability distributions to incorporate memory effects. Calculations of the propagation of ‘‘anomalous transport waves” in various systems, with and without memory, illustrate the technique.

Under conditions of strong scattering, a dilemma often arises regarding the best numerical method to use. Main competitors are the Born series, the beam propagation method, and direct solution of the Lippmann-Schwinger equation. However, analytical relationships between the three methods have not yet, to our knowledge, been explicitly stated. Here, we bridge this gap in the literature. In addition to overall insight about aspects of optical scattering that are best numerically captured by each method, our approach allows us to derive approximate error bounds to be expected under various scattering conditions.

Complex systems are characterized by a tight, nontrivial interplay of their constituents, which gives rise to a multiscale spectrum of emergent properties. In this scenario, it is practically and conceptually difficult to identify those degrees of freedom that mostly determine the behavior of the system and separate them from less prominent players. Here, we tackle this problem making use of three measures of statistical information: Resolution, relevance, and mapping entropy. We address the links existing among them, taking the moves from the established relation between resolution and relevance and further developing novel connections between resolution and mapping entropy; by these means we can identify, in a quantitative manner, the number and selection of degrees of freedom of the system that preserve the largest information content about the generative process that underlies an empirical dataset. The method, which is implemented in a freely available software, is fully general, as it is shown through the application to three very diverse systems, namely, a toy model of independent binary spins, a coarse-grained representation of the financial stock market, and a fully atomistic simulation of a protein.

We study the absorbing-state phase transition in the one-dimensional contact process under the combined influence of spatial and temporal random disorders. We focus on situations in which the spatial and temporal disorders decouple. Couched in the language of epidemic spreading, this means that some spatial regions are, at all times, more favorable than others for infections, and some time periods are more favorable than others independent of spatial location. We employ a generalized Harris criterion to discuss the stability of the directed percolation universality class against such disorder. We then perform large-scale Monte Carlo simulations to analyze the critical behavior in detail. We also discuss how the Griffiths singularities that accompany the nonequilibrium phase transition are affected by the simultaneous presence of both disorders.

In this work we revisit the problem of equilibration in isolated many-body interacting quantum systems. We pay particular attention to quantum chaotic Hamiltonians, and rather than focusing on the properties of the asymptotic states and how they adhere to the predictions of the Eigenstate Thermalization Hypothesis, we focus on the equilibration process itself, i.e., the road to equilibrium. Along the road to equilibrium the diagonal ensembles obey an emergent form of the second law of thermodynamics and we provide an information theoretic proof of this fact. With this proof at hand we show that the road to equilibrium is nothing but a hierarchy in time of diagonal ensembles. Furthermore, introducing the notions of statistical complexity and the entropy-complexity plane, we investigate the uniqueness of the road to equilibrium in a generic many-body system by comparing its trajectories in the entropy-complexity plane to those generated by a random Hamiltonian. Finally, by treating the random Hamiltonian as a perturbation we analyzed the stability of entropy-complexity trajectories associated with the road to equilibrium for a chaotic Hamiltonian and different types of initial states.

We provide a Boltzmann-type kinetic description for dilute polymer solutions based on two-fluid theory. This Boltzmann-type description uses a quasiequilibrium based relaxation mechanism to model collisions between a polymer dumbbell and a solvent molecule. The model reproduces the desired macroscopic equations for the polymer-solvent mixture. The proposed kinetic scheme leads to a numerical algorithm which is along the lines of the lattice Boltzmann method. Finally, the algorithm is applied to describe the evolution of a perturbed Kolmogorov flow profile, whereby we recover the major elastic effect exhibited by a polymer solution, specifically, the suppression of the original inertial instability.

We study the effects of animal social networks with a weighted pattern of interactions on the flocking transition exhibited by models of self-organized collective motion. We consider variations of traditional models of collective motion in which interactions between individuals are mediated by static complex weighted networks, representing patterns of social interactions. For a model representing dynamics on a one-dimensional substrate, application of a heterogeneous mean-field theory provides a phase diagram as function of the heterogeneity of the network connections and the correlations between weights and degree. In this diagram we observe two phases, one corresponding to the presence of a transition and other to a transition suppressed in an always ordered system, already observed in the nonweighted case. Interestingly, a third phase, with no transition in an always disordered state, is also obtained. These predictions, numerically recovered in computer simulations, are also fulfilled for the more realistic Vicsek model, with movement in a two-dimensional space. Additionally, we observe at finite network sizes the presence of a maximum threshold for particular weight configurations, indicating that it is possible to tune weights to achieve a maximum resilience to noise effects. Simulations in real weighted animal social networks show that, in general, the presence of weights diminishes the value of the flocking threshold, thus increasing the fragility of the flocking state. The shift in the threshold is observed to depend on the heterogeneity of the weight pattern.

The rheological response of oriented axisymmetric grains has additional degrees of complexity associated with their microstructure orientation. These additional kinematic degrees of freedom that give rise to complex transient macroscale rheological responses are not well understood. In this Letter, we study the rheology of axisymmetric grains subjected to transient flow. We identify strong coupling between the microstructure rearrangement and strain hardening which, under certain conditions, can yield jamming. We identify the critical conditions corresponding to jamming and the dependency on the shape of the grains. It is shown that this is a particular form of jamming that is directional in nature, since unjamming occurs if the shear direction is reversed.

We study phase transition properties of the two-dimensional $q$-state clock model by an extensive Monte Carlo simulation. By analyzing the Binder ratio and its temperature derivative, we confirm that the two-dimensional $q$-state clock model exhibits two distinct Kosterlitz-Thouless phase transitions for $q=5,6$ but it has one second-order phase transition for $q=4$. The critical temperatures are estimated quite accurately from the crossing behavior of the Binder ratio (for $q<5$) and from negative divergent dips of the derivative of the Binder ratio (for $q\ge 5$) around these critical points. We also calculate the correlation length, the helicity modulus, and the derivative of the helicity modulus, and analyze their behaviors in different phases in detail.

Active Brownian motion commonly assumes spherical overdamped particles. However, self-propelled particles are often neither symmetric nor overdamped yet underlie random fluctuations from their surroundings. Active Brownian motion has already been generalized to include asymmetric particles. Separately, recent findings have shown the importance of inertial effects for particles of macroscopic size or in low-friction environments. We aim to consolidate the previous findings into the general description of a self-propelled asymmetric particle with inertia. We derive the Langevin equation of such a particle as well as the corresponding Fokker-Planck equation. Furthermore, a formula is presented that allows reconstructing the hydrodynamic resistance matrix of the particle by measuring its trajectory. Numerical solutions of the Langevin equation show that, independently of the particle's shape, the noise-free trajectory at zero temperature starts with an inertial transition phase and converges to a circular helix. We discuss this universal convergence with respect to the helical motion that many microorganisms exhibit.

Networks with as many mechanical constraints as degrees of freedom and no redundant constraints are minimally rigid or isostatic. Isostatic networks are relevant in the study of network glasses, soft matter, and sphere packings. Because of being at the verge of mechanical collapse, they have anomalous elastic and dynamical properties not found in the more commonly occurring hyperstatic networks. In particular, while hyperstatic networks are only slightly affected by geometric disorder, the elastic properties of isostatic networks are dramatically altered by it. In this paper, we show how disorder and system size strongly affect the ability of isostatic networks to sustain a compressive load. We develop an analytic method to calculate the bulk compressive modulus $B$ for various boundary conditions as a function of disorder strength and system size. For simplicity, we consider square and cubic lattices with $L^d$ sites, each having $d$ mechanical degrees of freedom, and $d{L}^d$ rotatable springs in the presence of hot-solid disorder of magnitude $\epsilon$. Additionally, $\sim L^{\theta }$ sites may be fixed, thus introducing a nonextensive number of redundancies, either in the bulk or on the boundaries of the system. In all cases, $B$ is analytically and numerically shown to decay as $L^{-\mu }$ with ${\mu }_{\text{large}}=d-\theta$ for large disorder and ${\mu }_{\text{small}}=\text{max}\{(d-\theta -1),0\}$ for small disorder. Furthermore $B(L,\epsilon )L^{{\mu }_{\text{small}}}=g\left(\lambda \right)$ with $\lambda =L^{({\mu }_{\text{large}}-{\mu }_{\text{small}})}{\epsilon }^2$ a scaling variable such that $\lambda <<1$ is small disorder and $\lambda >1$ is large disorder. The faster decay to zero of $B$ in the large disorder regime results from a broad distribution of spring tensions, including tensions of both signs in equal proportions, which is remarkable since the system is under a purely compressive load. Notably, the bulk modulus is discontinuous at $\epsilon =0$, a consequence of the fact that the regular network sits at an unstable degenerate configuration.

Recent experiments have demonstrated the generation of coherent mechanical oscillations in a suspended carbon nanotube, which are driven by an electric current through the device above a certain voltage threshold, in close analogy with a lasing transition. We investigate this phenomenon from the perspective of work extraction, by modeling a nanoelectromechanical device as a quantum flywheel or battery that converts electrical power into stored mechanical energy. We introduce a microscopic model that qualitatively matches the experimental finding, and we compute the Wigner function of the quantum vibrational mode in its nonequilibrium steady state. We characterize the threshold for self-sustained oscillations using two approaches to quantifying work deposition in nonequilibrium quantum thermodynamics: the ergotropy and the nonequilibrium free energy. We find that ergotropy serves as an order parameter for the phonon lasing transition. The framework we employ to describe work extraction is general and widely transferable to other mesoscopic quantum devices.

How do near-bankruptcy events in the past affect the dynamics of stock-market prices in the future? Specifically, what are the long-time properties of a time-local exponential growth of stock-market prices under the influence of stochastically occurring economic crashes? Here, we derive the ensemble- and time-averaged properties of the respective “economic” or geometric Brownian motion (GBM) with a nonzero drift exposed to a Poissonian constant-rate price-restarting process of “resetting.” We examine—based both on thorough analytical calculations and on findings from systematic stochastic computer simulations—the general situation of reset GBM with a nonzero [positive] drift and for all special cases emerging for varying parameters of drift, volatility, and reset rate in the model. We derive and summarize all short- and long-time dependencies for the mean-squared displacement (MSD), the variance, and the mean time-averaged MSD (TAMSD) of the process of Poisson-reset GBM under the conditions of both rare and frequent resetting. We consider three main regions of model parameters and categorize the crossovers between different functional behaviors of the statistical quantifiers of this process. The analytical relations are fully supported by the results of computer simulations. In particular, we obtain that Poisson-reset GBM is a nonergodic stochastic process, with generally $\text{MSD}\left(\mathrm{\Delta }\right)\ne \text{TAMSD}\left(\mathrm{\Delta }\right)$ and $\text{Variance}\left(\mathrm{\Delta }\right)\ne \text{TAMSD}\left(\mathrm{\Delta }\right)$ at short lag times $\mathrm{\Delta }$ and for long trajectory lengths $T$. We investigate the behavior of the ergodicity-breaking parameter in each of the three regions of parameters and examine its dependence on the rate of reset at $\mathrm{\Delta }/T\ll 1$. Applications of these theoretical results to the analysis of prices of reset-containing options are pertinent.

Structural balance theory is an established framework for studying social relationships of friendship and enmity. These relationships are modeled by a signed network whose energy potential measures the level of imbalance, while stochastic dynamics drives the network toward a state of minimum energy that captures social balance. It is known that this energy landscape has local minima that can trap socially aware dynamics, preventing it from reaching balance. Here we first study the robustness and attractor properties of these local minima. We show that a stochastic process can reach them from an abundance of initial states and that some local minima cannot be escaped by mild perturbations of the network. Motivated by these anomalies, we introduce best-edge dynamics (BED), a new plausible stochastic process. We prove that BED always reaches balance and that it does so fast in various interesting settings.

Kinetic simulations of collisionless (or weakly collisional) plasmas using the Vlasov equation are often infeasible due to high-resolution requirements and the exponential scaling of computational cost with respect to dimension. Recently, it has been proposed that matrix product state (MPS) methods, a quantum-inspired but classical algorithm, can be used to solve partial differential equations with exponential speed-up, provided that the solution can be compressed and efficiently represented as a MPS within some tolerable error threshold. In this work, we explore the practicality of MPS methods for solving the Vlasov-Poisson equations for systems with one coordinate in space and one coordinate in velocity, and find that important features of linear and nonlinear dynamics, such as damping or growth rates and saturation amplitudes, can be captured while compressing the solution significantly. Furthermore, by comparing the performance of different mappings of the distribution functions onto the MPS, we develop an intuition of the MPS representation and its behavior in the context of solving the Vlasov-Poisson equations, which will be useful for extending these methods to higher-dimensional problems.

The regime of self-compression has been proposed for plasma-based backward Raman amplification upon a flying focus. By using a pumping focus moving with a speed equal to the group velocity of stimulated Raman backscattering (SRBS), only a short part of SRBS which always synchronizes with the flying focus can be amplified. Therefore, instead of a short pulse, plasma noise or a long pulse can seed the BRA amplifiers. Here we demonstrate the regime by 2D particle-in-cell simulations, showing that the pump pulse is compressed from 26 ps to 116 fs, with an output amplitude comparable with the case of a well-synchronized short seed. As only one laser pulse is used in the simulation, the results present a significant path to simplify the Raman amplifiers.