We investigate the reconnection process involving a quantum vortex ring colliding at different angles with a quantum straight vortex line. The dynamics of this phenomenon is well described by the Gross-Pitaevskii equation. Two families of initial configurations are explored, one by varying the angle $\alpha$ between the vortex ring and the horizontal plane, the other by changing the angle $\sigma$ between the vortex ring and the vertical plane through the straight vortex and the ring center. Results show that sound generation is maximal when the ring is initially parallel to the straight vortex, whereas the kinetic energy is predominantly composed of the incompressible contribution when the angle $\alpha$ is increased. We provide evidence for the existence of a critical angle $\overline{\alpha }$ above which a gradual decrease in kinetic energy accompanied by an increase in the curvature of the ring after the second reconnection appears. Since curvature is associated with the velocity of the nodal lines, we conclude that the loss of the fluid's incompressible kinetic energy is compensated by the increase in the kinetic energy of the nodal lines. We also observe that for angles greater than $\overline{\alpha }$, the kinetic and internal energy components exchange their rate, while the quantum energy increases at a constant rate regardless of the angle. Changing the angle $\sigma$ may result in both reconnection and absorption of the vortex ring, the latter occurring only for a restricted range of negative angles $\sigma$. A critical negative value $\overline{\sigma }$ is found beyond which the growth rate of the kinetic and internal energy changes sign. This occurs precisely when reconnection is replaced by absorption. We also observe that while changing the sign of $\alpha$ ensures symmetric behavior, changing the sign of $\sigma$ instead leads to an asymmetry due to the effect of the straight vortex on the ring motion. In particular, for negative values of $\sigma$, the fluid kinetic energy decreases because the straight vortex opposes the ring, with a stronger and more evident effect as $\sigma$ moves to less negative values.

Isolated microscopic magnetic particles are used to induce local perturbations in dense colloidal suspensions by rotating an external magnet. Confocal microscopy enables tracking of both the magnetic probe particle and adjacent colloidal particles. A probe particle moves with a circular trajectory. Knowing the external force and measuring the amplitude and phase of the probe motion allows us to infer the elastic and viscous moduli of colloidal suspensions at various volume fractions. These measurements are in qualitative agreement with previous results from conventional rheology. To further analyze the system's response, the oscillatory amplitude of colloidal particles is evaluated as a function of distance from the probe, revealing a $1/r$ decay in amplitude, consistent with a homogeneous viscoelastic material. These observations confirm that continuum descriptions of the colloidal samples are effective down to length scales comparable to the particle diameter.

Polymer-Attenuated Coulombic Self-Assembly (PACS) is a flexible experimental approach for generating crystals from simple colloidal building blocks. The central components are charged spherical particles coated with a polymer brush that prevents irreversible aggregation. Whether oppositely charged colloids crystallize, and which structures they form, depends on several factors, including colloid concentration, charge, and size, as well as the salt concentration of the solution. Molecular dynamics (MD) simulations are a powerful tool for predicting the outcomes of PACS assembly experiments and provide particle-level insights into the assembly processes. Here we present an open-source simulation framework, PACSim, that enables MD simulation studies of assembly by PACS across a range of experimentally relevant scenarios. PACSim is built on top of OpenMM, a flexible MD simulation framework that readily supports the implementation of different interaction potentials, as well as integration with other tools such as enhanced-sampling and machine-learning frameworks. We describe the motivation for PACSim, outline its features, report methodological advancements inspired by this framework, and provide examples of its use.

The permeability of the fluid between packs of particles is an important parameter for a range of natural and industrial applications. Here, we start from experiments in which soft spherical particles—“soft balls”—are compressed uniaxially, resulting in particle-particle flattening and an overall decrease in interstitial volume fraction $\varphi$ down to very low values. During deformation, the soft particle packs were scanned in three dimensions using x-ray tomography; we segment these data to isolate the fraction between the particles. We then run lattice Boltzmann fluid flow simulations through the domains to determine the permeability. Additionally, we determine the specific surface area $s$ and $\varphi$. Together, we use these parameters to test and validate a semiempirical extension to a theoretical percolation model, to propose a framework for predicting permeability in deforming soft particle systems. Once validated, we compare this model to the permeability of the fluid in packs of hard particles and during particle sintering; we conclude that this model is applicable to a wide range of initially particulate systems with all the geometric information encoded in $s$.

We address the effective nonequilibrium drag force field that emerges from the microscopic interparticle interactions in steady states of counterdriven binary fluid mixtures. Using power functional scaling arguments for adaptive Brownian dynamics computer simulation results, we establish quantitatively the crossover between near-equilibrium linear response and far-nonequilibrium square root asymptotics. An algebraic expression captures both limiting cases and remains applicable in the crossover regime. Using simulation results as benchmarks, we verify that a local power functional approximation based on the scaling law reproduces the spatial nonequilibrium structure formation in inhomogenously driven systems. The crossover scenario transcends dynamical density functional theory and it sheds light on general nonequilibrium scaling of driven fluids.

We investigate analytically and numerically correlation functions of the phase of light waves that propagate through turbulent media. We examine the case of strong scintillations that occur at large values of the Rytov dispersion ${\sigma }_R^2$, which determines a level of distortions of the light wave. Then, it is possible to relate the pair correlation function of phase gradients to the known pair correlation function of the envelope dependent on the distance $r$ assuming Gaussianity of the envelope of the beam. Our direct numerical simulations show that the profile of the pair correlation function for phase gradients gradually approaches the theoretical expression as the value of ${\sigma }_R^2$ increases, if $r<r_0$ where $r_0$ is the Fried length, which is the correlation length of the wave. For larger $r$ the behavior of the computed correlation function is quite different because of destroying the Gaussianity.

We propose an algorithm based on modulable hidden variables and adaptive step lengths, inspired by statistical physics and renormalization group principles, to study the emergent Wigner-Dyson level statistics in a driven many-body system. We apply this method to the boundary-driven Bose-Hubbard chain to illustrate the competition between coherent driving, hopping, and on-site interactions. Unlike random matrix models where chaos is induced through quenched disorder, here ergodicity emerges intrinsically from many-body configuration hybridization induced by the interplay between the driving field $F$ and the nonlinearity $U$. The resulting system follows statistics approaching the Gaussian orthogonal ensemble (GOE) limit. Using a self-attention-inspired Gaussian relevance map, the algorithm organizes the discrete site-occupation features and iteratively redistributes a coarse-grained spectral measure. This reduces the computational overhead relative to calculating the full eigenspectrum, although the truncated Hilbert-space dimension $D$ itself still grows exponentially with system size. The deterministic feedback stabilizes the macroscopic spectral variance, while a separate stochastic step restores the local fluctuations required for a GOE-like surrogate spacing distribution. The reconstruction loop therefore does not require a full eigendecomposition, while exact diagonalization is retained here only for finite-size validation.

Substrate curvature and cell-substrate adhesion are two key physical determinants of directional collective cell migration, yet their joint regulatory mechanism remains unresolved. Here, we establish a physical framework based on the instantaneous force balance to reveal the dominance switching mechanism between these two factors in collective cell migration. Higher curvature sets a threshold that gates migration initiation by redistributing forces, while adhesion primarily governs curvature-independent migration velocity after cells surpass the threshold by mediating the balance between driving and resistive forces. This framework yields experimentally consistent predictions for in vitro systems and helps understand a diversity of physiological and pathological processes that involve collective cell dynamics.

A promising route to superconducting artificial neural networks is a hybrid digital-analog architecture that combines digital single-flux-quantum (SFQ) communication with compact analog nonlinear processing. The study focused on the dynamic conversion of a discrete signal passing through a digital-to-analog-to-digital (DAD) converter, in which the role of the analog cell was performed by a $\mathrm{\Sigma }$-neuron with a nonlinear transfer function—the basic cell of perceptron-like neural networks. Furthermore, the DAD converter, the elementary functional block of the hybrid architecture, combines a digital-to-analog converter (DAC) and an analog-to-digital converter (ADC), and reencodes the analog $\mathrm{\Sigma }$-neuron wave forms as an SFQ pulse sequence. Circuit-level simulations demonstrate how input values encoded by SFQ pulse trains are converted into analog signal levels, transformed by the $\mathrm{\Sigma }$-neuron, and mapped back to pulse-based outputs. As a key experimental step, we fabricated and characterized a redesigned $\mathrm{\Sigma }$-neuron and measured a sigmoid-like transfer characteristic suitable for activation-function implementation. The extracted response was incorporated into system-level simulations to assess the influence of realistic device parameters on the conversion process. We delineate the operating-range matching requirements for the DAC, neuron, and ADC blocks, supporting the feasibility of the proposed interface as a building block for perceptron-like superconducting neural networks with digital inputs and outputs. Finally, we developed two perceptron networks, one using a mathematical sigmoid activation and the other the measured $\mathrm{\Sigma }$-neuron transfer characteristic, which reached classification accuracies of $97.0\%$ and $91.9\%$, respectively, on the MNIST handwritten digit dataset.

Chiral fluids can exhibit anomalous transport associated with an odd mobility tensor, which gives a tracer a characteristic transverse (Hall-like) drift. Interactions with the surrounding fluid can reverse both this Hall drift and the advective drift along the driving direction, yet the microscopic collision-level mechanism behind these reversals remains unclear. Here we introduce a collision-level framework to interpret drift reversal, combining underdamped Brownian dynamics simulations with a reversal-collision effectiveness map. The map identifies the host-tracer interaction-force orientations that favor reversal. A coarse-grained description of the host-particle velocity-direction statistics then connects this particle-level mechanism to the many-particle dynamics. We find that increasing the odd mobility strength shifts the angular probability distribution toward reversal-effective sectors, promoting drift reversal. Particle inertia and packing fraction modulate the two reversals asymmetrically: larger inertia strengthens the reversed advective drift while suppressing the Hall reversal. Increasing packing fraction produces a nonmonotonic response in which both reversals first intensify and then weaken, with both drift components beginning to recover their normal directions at high densities. Together, these results give a particle-level description of drift reversal in interacting chiral fluids, and clarify how inertia and packing fraction influence nonequilibrium transport in systems with broken time-reversal and mirror symmetries.

Soft robots rely on continuous control to achieve complex functionality. Their autonomous behavior is currently limited by a lack of embodied computation and the ability to store information of past events. Inflatable soft actuators typically relax once pressure is removed and, thus, do not exhibit state-space behavior when combined. As a result, rather than embodying memory and logic, inflatable soft robots implement these functions in software. Prior work has shown that mechanical instabilities can endow soft structures with discrete states, yet accessing all possible combinations of states in coupled systems remains challenging and often requires precise geometric tuning of structures. Here we demonstrate that geometric symmetry can be exploited to create truly bistable inflatable actuators with snap-up and snap-down pressures that are equal in magnitude and opposite in sign, enabling permanent memory and fully accessible state spaces using a single pressure input. By gluing two identical conical shell actuators back to back, we obtain nearly point-symmetric pressure–volume characteristics, whose absolute snapping pressures scale directly with material stiffness. As a result, nested pressure–volume curves, where actuators with higher snap-up pressures also have lower snap-down pressures, are achieved simply by varying the shear modulus of the material while keeping the geometry constant. We validate this principle through numerical simulations and experiments and show that three serially connected nested actuators can reliably access all eight global states and function as a pneumatic 1-to-3 demultiplexer. More broadly, this symmetry-based design strategy enables scalable mechanical memory and logic in soft robotic systems, reducing control complexity and opening pathways toward fully embodied pneumatic computation.

Reconstructing the causal structure of physical systems from observational data constitutes a fundamental inverse problem. Here we show that the reconstruction dimension—defined as an upper bound on the number of recoverable components—is determined by the group-representation structure of the observation spaces and reconstruction maps. This formulation provides an explicit and operational characterization of reconstructability and reconstruction dimension, extending ideas that are often understood only intuitively in equivariant representation theory. As a concrete example, we demonstrate the reconstruction of the local velocity-gradient tensor from orientational measurements of particles suspended in flows, where the observation and velocity-gradient tensor spaces form SO(3) representations with constrained equivariant maps between them. Using an SO(3)-equivariant neural network (implemented with e3nn), we show that the reconstructable subspaces predicted by the representation decomposition are qualitatively consistent with those found in practice. Our formulation shows that the representation structure constrains reconstructability by determining an upper bound sector by sector, while our numerical results suggest that the actual saturation of the bound depends on the physics and data geometry. Beyond providing a useful theoretical framework, this work also connects the abstract representation-theoretic structure to concrete inverse reconstruction problems in fluid physics.

We construct a family of exactly solvable relativistic kinetic theories in $1+1$ dimensions whose hydrodynamic sector continuously interpolates between Fick's and Cattaneo's laws of diffusion. The interpolation is controlled by a single parameter $a\in [0,1]$, which tunes the microscopic scattering dynamics from infinitely soft but infinitely frequent scatterings ($a=0$), reproducing standard diffusion, to maximally hard but finite-rate scatterings ($a=1$), yielding hyperbolic Cattaneo-type transport. For intermediate values of $a$, the dynamics combines frequent weak scatterings with rare strong randomizing events, providing a concrete microscopic realization of mixed diffusive-telegraphic behavior. Remarkably, the full quasinormal mode spectrum can be obtained analytically for all $a$. This allows us to track explicitly how purely diffusive modes continuously deform into damped propagating modes as the collision structure is varied.

In conventional generative adversarial networks (GANs), the discriminator typically employs a sigmoid activation to map features to probabilities. However, this activation suffers from the vanishing-gradient problem, which can lead to training instability and mode collapse. This work reformulates the final-layer activation of the discriminator as cumulative distribution functions (CDFs) of random variables, thereby introducing a learnable noise-scale parameter. We show analytically that, for CDF families with bounded reversed hazard rates in the negative tail, a noise-scale value smaller than unity scales the gradient of both the saturating and nonsaturating generator losses by a factor proportional to its reciprocal. Rather than fully resolving the vanishing-gradient problem, this mechanism provides more informative gradient signals to the generator when the discriminator is close to its optimum. Experiments on a two-dimensional Gaussian mixture and on MNIST show that the proposed CDF-based activation effectively improves mode coverage and training stability. Meanwhile, experiments on CIFAR-10 using a deep convolutional GAN (DCGAN) indicate that, when gradient flow is already stabilized by architectural features, the choice of final-layer activation plays a more limited role. An ablation study on MNIST further distinguishes the benefits of the proposed method from that of conventional noise regularization techniques. These results demonstrate that the proposed CDF-based activation also contributes a meaningful scheme for stabilizing adversarial training.

Fluctuations can drive otherwise continuous phase transitions to first order through the Brazovskii mechanism. We study how these fluctuation-induced transitions are modified in active systems by introducing nonequilibrium spatiotemporally correlated noise. We show that, while the transition remains fluctuation-induced first order, activity systematically suppresses these fluctuation effects, shifting the transition to higher temperatures and rendering it increasingly weakly first order. As a result, ordering is enhanced without inducing a spinodal instability of the isotropic phase, as confirmed by direct numerical simulations. In the strong-activity limit, fluctuation effects disappear and mean-field behavior is recovered. Our results identify activity as a generic control parameter for tuning the strength of fluctuation-induced first-order transitions.

Living systems often function with regulatory interactions, but the question of how activity, stochasticity, and regulations work together for achieving different goals still remains puzzling. We propose a stochastic model of an active tracking particle with information processing, where the entropy production and information flow are discussed, with the generalized fluctuation theorem serving as a benchmark for verifying the probability setups. Based on the model, the system performance, in terms of the first passage steps, the total energy consumption, and the thermal efficiency, is analyzed in the variable space of measurement error and control field, leading to discussions on optimal controls of the system. Not only elucidating the basic concepts involved in a stochastic active system with information processing, this prototypical model could also inspire more elaborated modelings of natural smart organisms and industrial designs of controllable active systems with desired physical performances in the future.

Quantum-induced stochastic dynamics arises when a quantum system, strongly decohered by its coupling to a quantum environment, evolves as a genuine classical stochastic process. Depending on the environment's quantum state, the environment can act as a dynamic bath, capable of simultaneously exchanging heat and performing work. We formulate a thermodynamic framework for this semiclassical regime, defining heat, work, and entropy production. We derive a modified Second Law that accounts for nonequilibrium quantum features, such as squeezing. The framework is exemplified by an optomechanical setup, where we characterize the thermodynamics of the nonstationary noise induced by the cavity field.

The emergence of quantum chaos in a system of trapped interacting bosons with externally impressed rotation is studied through spectral form factor (SFF) and power spectrum using exact diagonalization. Two distinct interaction regimes are considered: the moderate, when the interaction energy is small compared to the trap energy, and the strong, when the interaction energy is comparable to the trap energy. In the moderate interaction regime, the SFF for the nonrotating case exhibits a dip-plateau structure with an absence of linear ramp, indicating integrable behavior, while for the single-vortex state the SFF exhibits a discernible linear ramp consistent with pseudo-integrable behavior. In the strong interaction regime, the nonrotating case exhibits emergence of a linear ramp with small time span in SFF, indicating that the system has moved further towards chaotic regime but continues to be pseudo-integrable. For the single-vortex and the multivortex states in a strong interaction regime, the span of the linear ramp in SFF increases progressively with rotation, indicating the system has moved into a strong chaotic regime consistent with Gaussian orthogonal ensemble. The power spectrum results with the exponent lying in the interval $1\lesssim \alpha \lesssim 2$ are consistent with the SFF findings. An understanding of the observed crossover from integrable to quantum chaos is presented in terms of the macroscopic occupation of a single-particle quantum state—the Bose-Einstein condensation—and its depletion driven by interaction and rotation.