We develop a multitask and multifidelity Gaussian process (MMGP) model to accurately predict and optimize the multiobjective performance of a flapping foil while minimizing the cost of high-fidelity data. Through a comparison of three kernels, we have selected and applied the spectral mixture kernel and validated the robustness and effectiveness of a multiacquisition function. To effectively incorporate data with varying levels of fidelity, we have adopted a linear prior formula-based multifidelity framework. Additionally, Bayesian optimization with a multiacquisition function is adopted by the MMGP model to enable multitask active learning. The results unequivocally demonstrate that the MMGP model serves as a highly capable and efficient framework for effectively addressing the multiobjective challenges associated with flapping foils.

The lifting Hele-Shaw cell flow commonly involves the stretching of a viscous oil droplet surrounded by air, in the confined space between two parallel plates. As the upper plate is lifted, viscous fingering instabilities emerge at the air-oil interface. Such an interfacial instability phenomenon is widely observed in numerous technological and industrial applications, being quite difficult to control. Motivated by the recent interest in controlling and stabilizing the Saffman-Taylor instability in lifting Hele-Shaw flows, we propose an alternative way to restrain the development of interfacial disturbances in this gap-variable system. Our method modifies the traditional plate-lifting flow arrangement by introducing a finite fluid annulus layer encircling the central oil droplet, and separating it from the air. A second-order, perturbative mode-coupling approach is employed to analyze morphological and stability behaviors in this three-fluid, two-interface, doubly connected system. Our findings indicate that the intermediate fluid ring can significantly stabilize the interface of the central oil droplet. We show that the effectiveness of this stabilization protocol relies on the appropriate choice of the ring's viscosity and thickness. Furthermore, we calculate the adhesion force required to detach the plates, and find that it does not change significantly with the addition of the fluid envelope as long as it is sufficiently thin. Finally, we detect no distinction in the adhesion force computed for stable or unstable annular interfaces, indicating that the presence of fingering at the ring's boundaries has a negligible effect on the adhesion force.

The liquid-vapor phase separation is investigated via lattice Boltzmann simulations in three dimensions. After expressing length and time scales in reduced physical units, we combined data from several large simulations (on ${512}^3$ nodes) with different values of viscosity, surface tension, and temperature, to obtain a single curve of rescaled length $\widehat{l}$ as a function of rescaled time $\widehat{t}$. We find evidence of the existence of kinetic and inertial regimes with growth exponents ${\alpha }_d=1/2$ and ${\alpha }_i=2/3$ over several time decades, with a crossover from ${\alpha }_d$ to ${\alpha }_i$ at $\widehat{t}\simeq 1$. This allows us to rule out the existence of a viscous regime with ${\alpha }_v=1$ in three-dimensional liquid-vapor isothermal phase separation, differently from what happens in binary fluid mixtures. An in-depth analysis of the kinetics of the phase separation process, as well as a characterization of the morphology and the flow properties, are further presented in order to provide clues into the dynamics of the phase-separation process.

Distributions that deviate from equilibrium predictions are commonly observed across a broad spectrum of systems, ranging from laboratory experiments to astronomical phenomena. These distributions are generally regarded as a manifestation of a quasiequilibrium state and can very often be represented as a superposition of statistics, i.e., superstatistics. The underlying idea in this methodology is that the nonequilibrium system consists of a collection of smaller subsystems that remain infinitely close to equilibrium. This procedure has been effectively implemented in a kinetic setting, but thus far, only in the collisionless regime, limiting its scope of application. In this paper, we address the effect of collisions on the relaxation process and time evolution of superstatistical systems. After confronting the superstatistical distributions with experimental and simulation data, relevant to our analysis, we first study the effect of superstatistics on entropy. We explicitly show that, in the absence of long-range interactions, the extensivity of entropy is preserved, albeit influenced by the specific class of temperature fluctuations. Then, we examine how collisions drive the system towards a global equilibrium state, characterized by a maximum entropy, by employing the relaxation time approximation. This allows us to define a dynamical version of superstatistics, characterized by a singular time-varying parameter $q\left(t\right)$, which undergoes a continuous evolution towards equilibrium. We show how this approach enables the determination of the evolution of the underlying temperature distribution under the influence of collisions, which act as stochastic forces, gradually narrowing the temperature distribution over time.

We present the theory describing Bose-Einstein condensation (BEC) and superfluidity in a liquid ${}^4\mathrm{He}$ based on the concept that for some temperature interval, there exist metastable diatomic clusters or diatomic quasiparticles which are the bound states of two atoms of ${}^4\mathrm{He}$. It is shown that in liquid ${}^4\mathrm{He}$ for the temperature region $1\mathrm{K}\le T\le T_{\lambda }$ diatomic quasiparticles macroscopically populate the ground state which leads to BEC in liquid ${}^4\mathrm{He}$. The approach yields the lambda temperature as $T_{\lambda }=2.16\mathrm{K}$, which is in excellent agreement with the experimental lambda transition temperature $T_{\lambda }=2.17\mathrm{K}$. The concept of diatomic quasiparticles also leads to superfluid and BEC fractions which are in good agreement with experimental data and Monte Carlo simulations for liquid ${}^4\mathrm{He}$. It is also shown that the condensate fraction for low temperature ($T\le 0.5\mathrm{K}$) at saturated vapor pressure is ${\rho }_0/\rho =7.22\%$, which is very close to the value $7.25\pm 0.75\%$ obtained in recent measurements.

We explore the thermodynamics of stochastic heat engines in the presence of stochastic resetting. The setup comprises an engine whose working substance is a Brownian particle undergoing overdamped Langevin dynamics in a harmonic potential with a time-dependent stiffness, with the dynamics interrupted at random times with a resetting to a fixed location. The effect of resetting to the potential minimum is shown to enhance the efficiency of the engine, while the output work is shown to have a nonmonotonic dependence on the rate of resetting. The resetting events are found to drive the system out of the linear response regime, even for small differences in the bath temperatures. Shifting the reset point from the potential minimum is observed to reduce the engine efficiency. The experimental setup for the realization of such an engine is briefly discussed.

Anomalous diffusive behaviors are observed in highly inhomogeneous but relatively stable environments such as intracellular media and are increasingly attracting attention. In this paper we develop a coupled continuous-time random walk model in which the waiting time is power-law coupled with the local environmental diffusion coefficient. We provide two forms of the waiting time density, namely, a heavy-tailed density and an exponential density. For different waiting time densities, anomalous diffusions with the diffusion exponent between 0 and 2 and Brownian yet non-Gaussian diffusion can be realized within the present model. The diffusive behaviors are analyzed and discussed by deriving the mean-squared displacement and probability density function. In addition we derive the effective jump length density corresponding to the decoupled form to help distinguish the diffusion types. Our model unifies two kinds of anomalous diffusive behavior with different characteristics in the same inhomogeneous environment into a theoretical framework. The model interprets the random motion of particles in a complex inhomogeneous environment and reproduces the experimental results of different biological and physical systems.

Within quantum thermodynamics, many tasks are modeled by processes that require work sources represented by out-of-equilibrium quantum systems, often dubbed quantum batteries, in which work can be deposited or from which work can be extracted. Here we consider quantum batteries modeled as finite-dimensional quantum systems initially in thermal equilibrium that are charged via cyclic Hamiltonian processes. We present optimal or near-optimal protocols for $N$ identical two-level systems and individual $d$-level systems with equally spaced energy gaps in terms of the charging precision and work fluctuations during the charging process. We analyze the trade-off between these figures of merit as well as the performance of local and global operations.

We study the ensemble of pseudo-spin 1/2 ultracold bosons, performing Lévy flights, confined in a parabolic potential. The (pseudo-) spin-orbit coupling (SOC) is additionally imposed on these particles. We consider the structure and dynamics of macroscopic pseudospin qubits based on Bose-Einstein condensates, obtained from the above “fractional” bosons. Under “fractional” we understand the substitution of the ordinary second derivative (kinetic energy term) in the Gross-Pitaevskii equation by a so-called fractional Laplacian, characterized by the Lévy index $\mu$. We show that the joint action of interparticle interaction, SOC, and Zeeman splitting in a synthetic magnetic field makes the dynamics of corresponding qubit highly nontrivial with evident chaotic features at both strong interactions and Lévy indices $\mu \to 1$ when the Lévy trajectories of bosons with long jumps dominated over those derived from ordinary Gaussian distribution, corresponding to $\mu =2$. Using analytical and numerical arguments, we discuss the possibilities to control the above qubit using the synergy of SOC, interaction strength, and “fractionality,” characterized by the Lévy index $\mu$.

Central pattern generators are small networks that contribute to generating animal locomotion. The models used to study gait generation and gait transition mechanisms often require biologically accurate neuron and synapse models, with high dimensionality and complex dynamics. Tuning the parameters of these models to elicit network dynamics compatible with gait features is not a trivial task, due to the impossibility of inferring a priori the effects of each parameter on the nonlinear system's emergent dynamics. In this paper we explore the use of global optimization strategies for parameter optimization in multigait central pattern generator (CPG) models with complex cell dynamics and minimal topology. We first consider an existing quadruped CPG model as a test bed for the objective function formulation, then proceed to optimize the parameters of a newly proposed multigait, interlimb hexapod CPG model. We successfully obtain hexapod gaits and prompt gait transitions by varying only control currents, while all CPG parameters, once optimized, are kept fixed. This mechanism of gait transitions is compatible with short-term synaptic plasticity.

We investigate how a symmetric penetrable object immersed in an active fluid becomes motile due to a negative drag acting in the direction of its velocity. While similar phenomena have been reported only for active fluids that possess polar or nematic order, we demonstrate that such motility can occur even in active fluids without any preexisting order. The emergence of object motility is characterized by both continuous and discontinuous transitions associated with the symmetry-breaking bifurcation of the object's steady-state velocity. Furthermore, we also discuss the relevance of the transitions to the nonmonotonic particle-size dependence of the object's diffusion coefficient.

The particles on demand method [Phys. Rev. Lett. 121, 130602 (2018)] was recently formulated with a conservative finite-volume discretization and validated against challenging benchmarks. In this work, we focus on the properties of the reference frame transformation and its implications on the accuracy of the model. Based on these considerations, we propose strategies that simplify the scheme and generalize it to include a tunable Prandtl number via quasi-equilibrium relaxation. Finally, we adapt concepts from the multiscale semi-Lagrangian lattice Boltzmann formulation to the proposed framework, further improving the potential and the operating range of the kinetic model. Numerical simulations of high Mach compressible flows demonstrate excellent accuracy and stability of the model over a wide range of conditions.

A gambling demon is an external agent that can terminate a time-dependent driving protocol when a certain observable of the system exceeds a prescribed threshold. The gambling demon is examined in detail both theoretically and experimentally in a Brownian particle system under a compressing potential trap. Insight for choosing an appropriate work threshold for stopping is discussed. The energetics and the distributions of the stopping positions and stopping times are measured in simulations to gain further understanding of the process. Furthermore, the nonstationary and far-from-equilibrium stochastic process in the action of the gambling demon allows us to examine in detail some fundamental issues in stochastic thermodynamics, such as irreversibility and stopping-time fluctuation relation. Paradoxical violation of the stopping-time fluctuation relation can be reconciled in terms of the entropy production associated with fast hidden internal degrees of freedom. All the simulation or theoretical results are confirmed experimentally.

We combine histogram reweighting techniques with the two-lattice matching Monte Carlo renormalization group method to conduct computationally efficient calculations of critical exponents on systems with moderately small lattice sizes. The approach, which relies on the construction of renormalization group mappings between two systems of identical lattice size to partially eliminate finite-size effects, and the use of histogram reweighting to obtain computationally efficient results in extended regions of parameter space, is utilized to explicitly determine the renormalized coupling parameters of the two-dimensional ${\varphi }^4$ scalar field theory and to extract multiple critical exponents. We conclude by quantifying the computational benefits of the approach and discuss how reweighting opens up the opportunity to extend Monte Carlo renormalization group methods to systems with complex-valued actions.

The temporal rich club (TRC) phenomenon is widespread in real systems, forming a tight and continuous collection of the prominent nodes that control the system. However, there is still a lack of sufficient understanding of the mechanisms of TRC formation. Here we use the international N-nutrient trade network as an example of an in-depth identification, analysis, and modeling of its TRC phenomenon. The system exhibits a statistically significant TRC phenomenon, with eight economies forming the cornerstone club. Our analysis reveals that node degree is the most influential factor in TRC formation compared to other variables. The mathematical evolution models we constructed propose that the TRC in the N-nutrient trade network arises from the coexistence of degree-homophily and path-dependence mechanisms. By comprehending these mechanisms, we introduce a different perspective on TRC formation. Although our analysis is limited to the international trade system, the methodology can be extended to analyze the mechanisms underlying TRC emergence in other systems.

The present paper analytically investigates the stability of amplitude death in a multiplex Stuart-Landau oscillator network with a delayed interlayer connection. The network consists of two frequency-mismatched layers, and all oscillators in each layer have identical frequencies. We show that, if the matrices describing the network topologies of each layer commute, then the characteristic equation governing the stability can be reduced to a simple form. This form reveals that the stability of amplitude death in the multiplex network is equally or more conservative than that in a pair of frequency-mismatched oscillators coupled by a delayed connection. In addition, we provide a procedure for designing the delayed interlayer connection such that amplitude death is stable for any commuting matrices and for any intralayer coupling strength. These analytical results are verified through numerical examples. Moreover, we numerically discuss the results for the case in which the commutative property does not hold.

We investigate the interplay of an external forcing and an adaptive network, whose connection weights coevolve with the dynamical states of the phase oscillators. In particular, we consider the Hebbian and anti-Hebbian adaptation mechanisms for the evolution of the connection weights. The Hebbian adaptation manifests several interesting partially synchronized states, such as phase and frequency clusters, bump state, bump frequency phase clusters, and forced entrained clusters, in addition to the completely synchronized and forced entrained states. Anti-Hebbian adaptation facilitates the manifestation of the itinerant chimera characterized by randomly evolving coherent and incoherent domains along with some of the aforementioned dynamical states induced by the Hebbian adaptation. We introduce three distinct measures for the strength of incoherence based on the local standard deviations of the time-averaged frequency and the instantaneous phase of each oscillator, and the time-averaged mean frequency for each bin to corroborate the distinct dynamical states and to demarcate the two parameter phase diagrams. We also arrive at the existence and stability conditions for the forced entrained state using the linear stability analysis, which is found to be consistent with the simulation results.

Collective motion of locally interacting agents is found ubiquitously throughout nature. The inability to probe individuals has driven longstanding interest in the development of methods for inferring the underlying interactions. In the context of heterogeneous collectives, where the population consists of individuals driven by different interactions, existing approaches require some knowledge about the heterogeneities or underlying interactions. Here, we investigate the feasibility of identifying the identities in a heterogeneous collective without such prior knowledge. We numerically explore the behavior of a heterogeneous Vicsek model and find sufficiently long trajectories intrinsically cluster in a principal component analysis-based dimensionally reduced model-agnostic description of the data. We identify how heterogeneities in each parameter in the model (interaction radius, noise, population proportions) dictate this clustering. Finally, we show the generality of this phenomenon by finding similar behavior in a heterogeneous D'Orsogna model. Altogether, our results establish and quantify the intrinsic model-agnostic statistical disentanglement of identities in heterogeneous collectives.

Charged colloidal particles propel themselves through asymmetric fluxes of chemically generated ions on their surface. We show that asymmetry in the surface charge distribution provides an additional mode of self-propulsion at the nanoscale for chemically active particles that produce ionic species. Particles of sizes smaller than or comparable to the Debye length achieve directed self-propulsion through surface charge asymmetry even when ionic flux is uniform over its surface. Janus nanoparticles endowed with both surface charge and ionic flux asymmetries result in enhanced propulsion speeds of the order of $\mu \mathrm{m}/\mathrm{s}$ or higher. Our work suggests an alternative avenue for specifying surface properties that optimize self-propulsion in ionic media.