We analyze a multi-phase-field model for an epithelial monolayer with pairwise adhesions between neighboring cells following an Ornstein-Uhlenbeck process, representing the stochastic turnover of junctional molecular motors. These fluctuations in junctional adhesion result in rearrangements in the tissue, fluidizing it and producing diffusive cell motion. Similar junctional fluctuations have proven a very useful tool in the vertex model literature, and we hope they will be equally helpful to the multi-phase-field model approach. Moreover, we observe that the cells' effective diffusion coefficient depends nonmonotonically on the persistence time of the fluctuations, confirming results previously observed in the vertex model.

Previous studies dealing with Floquet stability analysis of non-zero-mean time-modulated Rayleigh-Bénard convection have shown the existence of only harmonic and subharmonic instability modes. Here, we emphasize the existence of quasiperiodic instabilities that have not yet been reported in the literature. This is achieved through both a Floquet-based analysis of the basic state and direct numerical simulations of the linearized stability equations. Particular attention is devoted to highlighting the existence of kinks in the numerically computed marginal stability curves that render difficult the detection of the stability exchange between quasiperiodic instabilities and their harmonic and subharmonic counterparts. Depending on the Prandtl number of the working fluid, such instability exchanges set in via either bicritical states or the migration of complex-conjugate pairs Floquet multipliers which can be combined to produce subharmonic responses.

Wall roughness plays a central role in determining turbulent friction in pipe flows, yet most predictive frameworks continue to characterize the wall by a single effective roughness scale. Classical datasets—notably Nikuradse's sand-grain experiments and the rough-pipe measurements of Colebrook and White—demonstrate that this description is incomplete: surfaces with comparable nominal roughness heights can exhibit markedly different friction curves, including branch splitting and multiregime behavior. Reexamining the Colebrook-White experiments, we identify clear evidence of multiscale additivity, whereby the contribution of a smaller roughness scale enters once it becomes dynamically accessible and increases approximately linearly with its areal coverage. Motivated by this observation, we develop a multiscale extension of the Gioia-Chakraborty momentum-transfer framework in which roughness elements at distinct geometric scales contribute additive increments to the total friction. In the model, each scale becomes dynamically active when its height exceeds a Reynolds-number-dependent viscous cutoff proportional to the Kolmogorov length, while its contribution is weighted by a scale-dependent coefficient representing the geometric prominence of that roughness scale. This construction generalizes the original scale-selection mechanism by allowing multiple roughness scales to participate concurrently in outer-layer momentum transfer. The resulting framework provides a unified and continuous description connecting the classical roughness-dominated Nikuradse-Strickler regime, Colebrook-White-type trends at intermediate roughness, and smooth-wall behavior. For the applications considered here, however, the model coefficients are not uniquely determined from surface properties and are selected in a data-guided manner; establishing a quantitative mapping from measured surface topography to these coefficients remains an open problem.

When a horizontal plate vibrates strongly enough, it causes small particles such as sand grains to continually bounce on it and, over time, to diffuse across its surface. This phenomenon is the cause of the well-known Chladni figure, which is drawn by a higher density of grains gathering along the nodal lines of a resonating elastic plate. Using a heterogeneous, nonresonating plate, we investigate experimentally this type of diffusion. We find that, for the most part, it is comparable to classical molecular diffusion. We can define a temperature for the bouncing grains, and the system then obeys the fluctuation-dissipation theorem. We also recover Maxwell-Boltzmann statistics at equilibrium, when temperature is uniform. However, when temperature varies across the vibrating plate, the microscopic details of the grains' dynamics affect their macroscopic behavior: Fick's law, for instance, no longer applies. Instead, our experiments support a new transport relation that was recently proposed to represent diffusion in Chladni's experiment. Finally, we propose an expression for the heat flux associated to the nonequilibrium steady state predicted by this new relation and test it against observations.

Thermodynamic uncertainty relations (TURs) bound current precision by entropy production, but transport of noncommuting (non-Abelian) charges lacks a single joint classical record of all current components. We derive an operational, process-level matrix TUR for this setting from the entropy production $\mathrm{\Sigma }=D({\rho }_{SE}^{\prime }\parallel {\rho }_S^{\prime }\otimes {\rho }_E)$. Isolating the bath divergence $D_{\mathrm{bath}}=D({\rho }_E^{\prime }\parallel {\rho }_E)$, we prove a fully nonlinear, saturable lower bound valid for arbitrary current vectors $\mathrm{\Delta }q$: $D_{\mathrm{bath}}\ge B(\mathrm{\Delta }q,V,V^{\prime })$, where the bound depends only on the transported-charge signal $\mathrm{\Delta }q$ and on the pre- and postcollision covariance matrices $V$ and $V^{\prime }$ reconstructed from separate probe measurement settings. In the small-fluctuation regime $D_{\mathrm{bath}}\ge \frac{1}{2}\mathrm{\Delta }{q}^{\mathsf{T}}{V}^{-1}\mathrm{\Delta }q+{O(\parallel \mathrm{\Delta }q\parallel }^4)$, while beyond linear response the full integral bound remains valid. Numerical strong-coupling qubit collisions illustrate the bound and its near-saturation using only local measurements on the bath probe.

Processes controlled by stochastic synthesis and degradation (SSD) are widespread in biology but their reaction kinetics are not well understood. Using methods borrowed from the theory of resetting processes, we determine the first-passage properties of a collection of independent particles that are synthesized and degraded at constant rates, and follow an arbitrary diffusive motion. At equal synthesis and degradation rates, the mean reaction time with a target site can be minimized as in stochastic resetting, and a $CV$ criterion is derived. When the degradation rate is held fixed and the synthesis costs are taken into account, an optimal synthesis rate is obtained. In bounded domains, despite particle degradation, SSD improves the mean search time compared to a single nondegrading particle if the synthesis rate exceeds a critical value which obeys a universal relation. We illustrate these findings with Brownian diffusion on the infinite line and in an interval.

We study the equilibrium phase behavior of a fluid confined in a slit made from two patterned walls. Shearing the walls frustrates the fluid, due to a competition between capillary condensation and interface delocalization, forcing the formation of bridging phases with different pinning properties. This leads to an unusually rich phase diagram, displaying first-order and continuous phase transitions, depending sensitively on the slit width and shear. Generalized Kelvin equations determine the phase boundaries, while the bridging phases are characterized by large correlation lengths, predictions for which are tested using a microscopic density functional model.

We propose a model that extends the standard depinning paradigm by incorporating an aging mechanism into the local pinning force. This favors oscillations between a stuck state of large pinning and a slipping state of smaller pinning. We show that for mean field interactions between sites this mechanism can lead to the appearance of “king avalanches” and global instabilities, producing a global oscillatory stick-slip stress regime. We construct the phase diagram for this mean field case and identify regions of smooth dynamics, pure stick-slip, and bistability. Crucially, when considering two-dimensional systems with short-range interactions we find that states of global stress oscillation persist, but in contrast to the mean field case, no system-size avalanches appear. Instead, we observe alternating temporal intervals of larger and lower avalanche activity that correlate with the stress oscillations.

Statistical-gauge structures generated by current operators provide a symmetry-based description of forces and hyperforces in closed quantum statistical mechanics, but their status in open quantum dynamics remains largely unexplored. Here we develop a generalized statistical-gauge framework for Markovian open quantum systems governed by Lindblad evolution. The construction formulates statistical shifts in a smeared operator language, thereby avoiding singular local expressions while preserving the force and hyperforce content of the closed theory. Within this framework, the action of the Lindblad generator is analyzed in a jump-resolved representation that separates Hamiltonian transport from dissipative contributions in jump space. The central result is a modular Ward identity that holds for any full-rank quantum state, independently of equilibrium, stationarity, or unitary dynamics. We show that weak covariance is controlled by the full dissipative structure in jump space, rather than by invariance of individual jump operators or by particle-number conservation. This leads to a classification of continuum reservoirs according to density weight and spatial rate profile, and shows that Lindblad dynamics can deform the transported shift algebra through its product defect even when the bath is weakly covariant. Physically, the theory explains why uniform one-body loss or pumping may preserve the statistical-gauge structure, whereas density dephasing, multiparticle loss, and nonuniform bath profiles generically produce covariance-breaking residuals. These residuals provide practical diagnostics for reservoir engineering, approximate master equations, and numerical many-body simulations of open quantum systems.

Quantum chaotic systems exhibit certain universal statistical properties that closely resemble predictions from random matrix theory (RMT). With respect to observables, it has recently been conjectured that, when truncated to a sufficiently narrow energy window, their statistical properties can be described by a unitarily invariant ensemble, and testable criteria have been introduced, which are based on the scaling behavior of free cumulants. In this paper, we investigate the conjecture numerically in translationally invariant Heisenberg spin chains with spin quantum number $S=\frac{1}{2},1,\frac{3}{2}$. Combining a quantum-typicality-based numerical method with the exploitation of the system's symmetries, we study the spin current operator and find clear evidence of consistency with the proposed criteria in chaotic cases. Our findings further support the conjecture of the existence of RMT universality as manifest in the observable properties in quantum chaotic systems.

We analyze a continuous-time random walk model with stochastic reversals of direction. There is no external potential but the reorientation mechanism generates a nonzero current from asymmetry in the forward and backward waiting-time distributions (even when they have the same mean); the system can therefore be considered as a type of active particle ratchet. We derive an explicit expression for the mean ratchet current with exponentially distributed reorientation times and also develop a general renewal-theory framework to obtain the full large deviations, using this to comment on the possibility of dynamical phase transitions.

Extending the quantum formulation of Deffner and Jarzynski [Phys. Rev. X 3, 041003 (2013)] to a more general setting for studying the thermodynamics of information processing including initial correlations, we generalize the second law of thermodynamics to account for information processing in such autonomous systems. We consider a composite quantum system consisting of a principal system, heat bath, memory, and work source, and adopt an autonomous Hamiltonian framework. We present the following three main results. We first derive constraints on the total Hamiltonian that ensure the work source to act as a catalyst preserving its original randomness, given that the total unitary evolution must have a unitary partial transpose. Second, we generalize the quantum speed limit for the joint dynamics of system and memory to the quantum thermodynamic speed limit, from which we obtain a dynamical version of Landauer's bound. Finally, we also interpret this quantum thermodynamic speed limit in the context of quantum hypothesis testing.

We investigate phase separation dynamics in a binary mixture driven by a moving cooling source from which cold thermal fronts propagate radially into the surrounding mixture. The motion of the source introduces two distinct velocity scales: $v_s$ associated with the translation of the source and $v$ related to the propagation of the cooling thermal fronts. Using a modified Cahn-Hilliard-Cook model that explicitly couples the time-dependent temperature and concentration fields, we demonstrate that the competition between these two velocities fundamentally controls the morphology and kinetics of phase separation. Our numerical simulation results reveal a rich variety of nonequilibrium patterns whose characteristics depend not only on the ratio $v_s/v$ but also on the absolute values of $v_s$ and $v$. Same value of $v_s/v$ yields distinctly different patterns for different $v$. The spatial extent and shape of the quenched region, determined by the temperature profile delineating spatial regions with local temperatures above and below the demixing temperature dictates the domain morphology. Furthermore, unlike conventional isothermal phase separation, the system does not exhibit dynamical scaling of the equal-time two-point correlation function with a single growing length scale, reflecting the intrinsically nonstationary nature of phase separation driven by a moving quench source. Our results establish moving thermal sources as an effective means of controlling phase-separation pathways and provide a strategy for engineering desired pattern structures by tuning the two velocities.

We propose a computational strategy that incorporates time-lagged sampling into the reverse process of diffusion-based machine learning models. This approach aims to maintain ensemble diversity and prevent ensemble collapse related to underestimation of uncertainties, particularly in situations with limited ensemble sizes. We demonstrate the method's effectiveness using the Lorenz '96 system as a theoretical testbed, focusing on its highly nonlinear regime without explicitly resolving multiscale interactions. Our approach facilitates stable forecast-analysis cycles at small ensemble sizes without requiring covariance inflation or localization by enhancing the ensemble representation of uncertainties inherent in diffusion models and stochastic dynamics. The proposed models maintain superior temporal coherence. While the probability density function is narrowed under severely undersampled ensembles, stochastic variants recover distributional accuracy as ensemble size increases, whereas deterministic variants show persistent limitations but provide substantial computational efficiency. The method generalizes successfully to systems with increased multiscale complexity and maintains stable long-term performance without drift. Results indicate that active diversity preservation in ensemble generation can enable robust data assimilation in undersampled situations, with potential applications in geophysical flows, plasma systems, and turbulence modeling.

Inertial fusion energy promises nearly unlimited, clean power, if it can be achieved. However, the design and engineering of fusion systems requires controlling and manipulating matter at extreme energies and timescales; the shock physics and radiation transport governing the physical behavior under these conditions are complex, requiring the development, calibration, and use of predictive multiphysics codes to navigate the highly nonlinear and multifaceted design landscape. We hypothesize that artificial intelligence reasoning models can be combined with physics codes and emulators to autonomously design fusion fuel capsules. In this article we construct a multiagent system where natural language is utilized to explore the complex physics regimes around fusion energy. The agentic system is capable of executing a high-order multiphysics inertial fusion computational code. We demonstrate the capacity of the multiagent design assistant to translate natural-language design goals into simulation execution, physics-emulator construction, analysis, and iterative refinement of capsule-geometry parameters, ultimately achieving simulated ignition within the computational design workflow.

We present experiments on chiral active polar particles, realized as vibrated granular rods, revealing evidence for “skipping orbits” at hard boundaries. These classical edge states exhibit a net circulation opposite to the particles' intrinsic rotation and lead to a pronounced accumulation at the boundary, stronger than for their achiral counterparts. The directed nature of these orbits provides a simple yet high-fidelity mechanism for chiral sorting—even for solitary particles, unlike in [T. Barois et al., Phys. Rev. Lett. 125, 238003 (2020)]. We propose a unified theoretical framework for boundary interactions of both chiral and achiral particles. In this model, an effective outward radial force, proportional to motility and chirality, explains the observed boundary-hugging. Our theory predicts, and our experiments confirm, a transition in the pairing of two particles of the same chirality, from apolar spinners to polar circle walkers, with increasing packing fraction of an ambient medium of beads.

This paper investigates the dynamic properties of a confined quasi-two-dimensional granular fluid at moderate densities, modeled within the framework of the Enskog kinetic equation. The confinement is not treated explicitly at the geometrical level; instead, it is incorporated in an effective way through the so-called $\mathrm{\Delta }$ model (a collisional model that includes energy injection through modified collision rules). The referred model is extended in this paper to account for the influence of an interstitial gas via a viscous drag force and a stochastic Langevin-like term. By applying the Chapman-Enskog method, the Navier-Stokes transport coefficients and the cooling rate are derived analytically considering the leading terms in a Sonine polynomial expansion. The study focuses on steady-state conditions and examines how the combined effects of inelastic collisions and external driving influence transport properties such as the viscosity and the thermal conductivity. Theoretical predictions for the steady temperature and the kurtosis are validated against direct simulation Monte Carlo results, showing excellent agreement. The findings reveal that the external driving significantly alters the transport coefficients compared to dry (no gas phase) granular systems, challenging previous assumptions that neglected these effects. Additionally, a linear stability analysis demonstrates that the homogeneous steady state is stable across the explored parameter space.

Phase reduction provides a principled relationship between high-dimensional oscillator dynamics and low-dimensional phase models such as the Kuramoto model. However, connecting analytical results derived from the Kuramoto framework to experimental data remains an open challenge, as many physical platforms violate the weak-coupling assumptions underlying standard phase reduction. Here, we present an experimental platform based on electronic quadrature oscillators whose phase dynamics are, under certain assumptions, equivalent to the Kuramoto model. Crucially, this equivalence is achieved for both weak and strong coupling through a nondiffusive coupling circuit, and the design approach supports arbitrary (weighted and directed) network topologies while preserving scalability and oscillation regularity. Across different coupling schemes, we demonstrate that the platform reliably reproduces phase transitions predicted by the Kuramoto model for both global and cluster synchronization. This cost-effective platform enables systematic experimental validation of analytical and numerical results for synchronization in complex networks.