We develop an analytical theory of coherent (scaled quadratically with the number of particles) radiation and coherent radiation friction in a head-on collision of a dense charged particle bunch with an intense laser pulse. We demonstrate that the low-frequency coherent radiation in the forward and backward directions dominates the energy-momentum losses of a mildly relativistic bunch and can result in a substantial enhancement of the overall radiation friction as compared to the incoherent case. We derive the scaling laws for the average momentum losses of the bunch over the collision with respect to laser intensity, pulse duration, and particle bunch parameters, and show their robustness with respect to laser polarization and the shape of the particle distribution in the bunch.

A theoretical study is presented of nematic liquid crystals confined between two concentric cylindrical surfaces under a radially inhomogeneous electric field, focusing on the interaction among elastic anisotropy, cylindrical geometry, and flexoelectric surfaces. Two alignment regimes are considered: planar under positive dielectric anisotropy, and homeotropic under negative dielectric anisotropy, both with strong anchoring at the inner and outer cylinders. The molecular reorientation behavior is related to measurable electrical quantities by modeling the nematic cell as an equivalent parallel $\mathit{RC}$ circuit and deriving expressions for resistance, capacitance, and time-dependent current under a linearly ramped voltage. Numerical results reveal how the flexoelectric coefficients (magnitude and sign), the elastic anisotropy ratio, and the geometry (inner and outer radii) influence the threshold behavior and post-threshold dynamics of the director field, thereby affecting the electrical response. In particular, it is shown that the electrical response of cylindrical nematic devices can be controlled by adjusting flexoelectric coefficients and the elastic anisotropy in this geometry.

The origin of some mesophases of the ferroelectric nematic realm is not yet well understood. In this work, we study the highly polar liquid crystal MIO, a close structural analog of the prototypical ferroelectric nematogen DIO, which exhibits a ferroelectric smectic A to ferroelectric smectic C (${\mathrm{SmA}}_{\text{F}}–{\mathrm{SmC}}_{\text{F}}$) phase transition. Calorimetric, dielectric, and light-scattering experiments reveal that it is a second-order phase transition with mean-field behavior and is driven by the softening of the tilt elastic constant accompanied by the divergence of the amplitude of the associated dielectric mode.

Amorphous particulate matter constitutes a wide range of natural and synthetic materials. Despite this ubiquity, the way in which these systems' disordered microstructure couples to their often subtle and complex dynamical behavior is not yet fully understood, with profound consequences for phenomena ranging from landscape evolution to cellular unjamming during tumor metastasis. With this paper, we introduce tools from linear control theory that quantify system response to external input, and demonstrate their utility in elucidating the dynamics of jammed amorphous materials under stress. Our results indicate that average controllability, the response of a system to perturbation, strongly correlates with particle rearrangement in systems subject to quasistatic shear, implying that average controllability is an accurate predictor of rearrangement dynamics in certain contexts. Moreover, we show that the timescale over which average controllability is calculated can be tuned to optimize its predictive capacity for particle rearrangement. Values of the optimal timescale provide physical insight into the system; namely, that multiple rearranging particles participate on average in vibrational eigenmodes of lower and lower energy as the system is sheared until the rearrangement event. Broadly, our study demonstrates that linear control theory is a promising mathematical framework for predicting and designing mechanical response in disordered media.

We present an analytical investigation of the global helicity budget associated with the salt-fingering instability within the two-dimensional, three-component framework. Our analysis shows that in the region of parameter space corresponding to salt fingering, helicity amplification occurs when the Rayleigh ratio—quantifying the relative strength of salinity to temperature gradients—exceeds a critical threshold. This finding provides a possible theoretical explanation for the spontaneous emergence of helicity, observed in recent numerical simulations.

Filtering methods are fundamental tools for extracting the backbone of complex networks. Systems displaying group interactions, however, open the way to new types of filtering approaches. Here, we introduce a statistical filtering method for higher-order networks that identifies statistically validated maximal interacting sets—maximal sets of nodes that consistently interact together within group interactions. Using properly designed benchmarks, we show that our approach is highly effective in systems where the maximal sets are likely to be diluted into interactions of larger sizes that include occasional participants. Applications to real-world data reveal that the identified sets of nodes are characterized by higher levels of similarity and topical coherence, highlighting the ability of our method to provide new insights on the organization of real-world higher-order networks.

Neural circuits exhibit structured connectivity, including an overrepresentation of reciprocal connections between neuron pairs. Despite important advances, a full understanding of how such partial symmetry in connectivity shapes neural dynamics remains elusive. Here we ask how correlations between reciprocal connections in a random, recurrent neural network affect phase-space complexity, defined as the exponential proliferation rate (with network size) of the number of fixed points that accompanies the transition to chaotic dynamics. We find a striking pattern: partial antisymmetry strongly amplifies complexity, while partial symmetry suppresses it. These opposing trends closely track changes in other measures of dynamical behavior, such as dimensionality, Lyapunov exponents, and transient path length, supporting the view that fixed-point structure is a key determinant of network dynamics. Thus, positive reciprocal correlations favor low-dimensional, slowly varying activity, whereas negative correlations promote high-dimensional, rapidly fluctuating chaotic activity. These results yield testable predictions about how connection reciprocity may shape neural dynamics and function.

Photonic and polaritonic systems offer a fast and efficient platform for accelerating machine learning (ML) through physics-based computing. To gain a computational advantage, however, polaritonic systems must meet the following: (1) exploit features that specifically favor nonlinear optical processing; (2) address problems that are computationally hard and depend on these features; and (3) integrate photonic processing within broader ML pipelines. In this work, we propose a polaritonic machine learning approach for solving graph-based data problems. We demonstrate how lattices of condensates can efficiently embed relational and topological information from point cloud datasets. This information is then incorporated into a pattern recognition workflow based on convolutional neural networks (CNNs), leading to significantly improved learning performance compared to physics-agnostic methods. Our extensive benchmarking shows that photonic machine learning achieves over 90% accuracy for Betti number classification and clique detection tasks, a substantial improvement over the 35% accuracy of bare CNNs. Our study introduces a distinct way of using photonic systems as fast tools for feature engineering, while building on top of high-performing digital machine learning.

Vibrational properties of ordered athermal solids near yielding remain poorly understood. We show that yielding in a sheared crystal is governed not by a single localized instability but by directionally extended multimode softening that forms a cross-shaped low-frequency region in wave number space. Near yielding, the acoustic dispersion $\omega \sim k$ is replaced by $\omega \sim k^2$ along the soft direction, and the vibrational density of states crosses over from Debye to non-Debye scaling, with a diverging length scale. We analytically derive these scaling laws.

Stochastic fluctuations often act as promoters of activity in excitable and oscillatory systems, giving rise to phenomena such as coherence resonance. Here, we show that in the low-intensity regime, noise plays a dual role in weakly coupled neuronal units: It can both inhibit collective spiking and, with increasing intensity, restore coordinated activity. For a ring of diffusively coupled, oscillatory FitzHugh-Nagumo neurons, we demonstrate how the interplay of noise and coupling strength organizes a rich set of collective dynamical states. We systematically classify emergent dynamical scenarios by an in-depth time-series analysis that integrates multiple complementary measures. We are able to automatically identify five distinct dynamical clusters in parameter space: pure noise, quiescent state, noisy synchronization, complete synchronization, and intermittent switching. Our workflow provides a general, automated framework for characterizing collective dynamics in coupled oscillator networks.

This work presents a general thermodynamic approach to describe particle diffusion on a lattice, a model used to study transport processes in solids and on surfaces. By treating each lattice site as an open thermodynamic system, the effects of microscopic particle interactions are represented through the chemical potential. A fundamental relationship between the Onsager matrix $\left(L\right)$ and its ideal-system counterpart ($L_{\text{id}}$, where interactions are neglected) using the determinant of the covariance matrix is demonstrated. This framework allows for the calculation of transport coefficients using the combination of their ideal values and thermodynamic properties. The general result is successfully applied to reproduce the Darken equation for substitutional diffusion in solids and to derive the nondiagonal diffusion matrix of the Zhdanov model for surface diffusion of Langmuir particles. In the last case, analytical predictions are further validated through numerical simulations across various interaction potentials.

Thermodynamic uncertainty relations (TURs) impose a universal trade-off between current precision and entropy production in autonomous steady states, constraining in particular the power, efficiency, and constancy of heat engines. We demonstrate strong violations of the long-time TUR in a minimal autonomous heat engine composed of a discrete ratchet generating work against a constant bias and an underdamped harmonic oscillator acting as an internal stochastic control. In the regime of timescale separation, the model becomes exactly solvable and yields a closed analytical expression for the TUR ratio, where the influence of the continuous degree of freedom is fully captured by the Fano factor of oscillator zero crossings. We show that increasingly deterministic internal control drives the TUR ratio arbitrarily close to zero while the engine operates near maximal current and efficiency. In an appropriate limit, the model reduces to the classical pendulum-clock system of Pietzonka [Phys. Rev. Lett. 128, 130606 (2022)].

The optimal fingerprint method (OFM) serves as a potent approach for detecting and attributing climate change. However, direct experimental validation remains challenging due to the system's inherent complexity. Here we experimentally validate this method using a precisely controlled magnetic resonance system of spins, which serves as a minimal physical analog of forced noise response. Based on linear response theory (LRT), we derived the system's Green's function from spin projection noise measurements and successfully applied it to attribute the magnetic fields, yielding excellent agreement with predictions. Furthermore, our measurements confirm the existence of an optimal detection direction that maximizes the signal-to-noise ratio, a key theoretical prediction underlying the OFM. This work serves as a laboratory demonstration of LRT and OFM in detection and attribution studies, aiming to connect theoretical models with experimental observations. These findings may provide useful references for climate change science and its broader interdisciplinary applications in ecosystems, finance, social sciences, quantum sensing, and other fields.

We investigate conductance fluctuations across the integer quantum Hall transition in graphene from the perspective of multiscale stochastic dynamics. Using a tight-binding description combined with the Landauer-Büttiker formalism, we generate longitudinal and transverse transmission time series by sweeping magnetic flux and Fermi energy in disordered nanoribbons with armchair and zigzag terminations. The interplateau regime exhibits non-Gaussian, intermittent fluctuations whose increments display scale dependence and broad-tailed statistics. Multifractal detrended fluctuation analysis reveals a broad singularity spectrum, indicating the coexistence of multiple scaling exponents. To rationalize these features, we model the increment distributions within an H-theory approach that leads to analytical expressions in terms of Meijer $G$ functions, while excellent agreement with the data is achieved by incorporating discrete statistical mixtures that account for clustered variance dynamics. Our results establish a quantitative connection between the quantum Hall transition in graphene and a cascadelike hierarchy in the conductance fluctuations, and clarify the role of disorder in enabling this multiscale regime.

As a deep learning-based framework, the physics-informed neural network (PINN) has been widely used to solve partial differential equations (PDEs) in various fields. However, due to causality violation and activation function saturation, PINN typically suffers from poor performance in long-time integration tasks. To address these issues, we propose a variant of PINN named sliding-interval PINN (SI-PINN). First, SI-PINN decomposes the temporal domain and employs a sliding-interval strategy to avoid activation function saturation. It also integrates a pretraining scheme to mitigate causality violation in PINN, thereby enabling long-time integration of PDEs. To validate the effectiveness of our SI-PINN, the proposed strategy is evaluated on a set of typical PDEs and compared with existing methods. Numerical results demonstrate that it enables accurate long-time integration across various systems. Moreover, in most cases we examined, it achieves superior accuracy to that of existing methods. The proposed SI-PINN is expected to provide a promising strategy for long-time integration of PDEs.

Active agents are likely to accumulate near confining obstacles due to collective motion. Here we investigate how the nature of the microscopic drive—self-propulsion or velocity alignment—selects distinct accumulation patterns, leading to either delocalized or compact clustered states. We first characterize the dynamical regimes emerging from the interplay of these two driving mechanisms under perfectly reflective or smooth boundary conditions. We then introduce boundary friction and observe a drastic change in the accumulation patterns, with dynamical phases that are absent in the previous case. By connecting emergent macroscopic structures to their underlying interactions, this work provides a practical route to infer the dominant microscopic mechanism ruling boundary-mediated collective behavior, with potential applications ranging from single-cell migration to bioinspired robotics.

Finite-time information erasure is governed by probability transport through configuration space, not only by final logical populations. We show that a two-dimensional Brownian memory can lower fast-erasure cost by reorganizing probability currents through a transverse degree of freedom. Using physics-informed routing control (PIRC), we optimize conservative protocols at fixed erasure time and final-error criterion, and compare them with tilt-only and static minimum-energy-path (MEP) constrained driving. Constrained scans over transverse-gate strength reveal two regimes: MEP and PIRC enter the target-error band where tilt remains infeasible and, when all three protocols have comparable final error, PIRC has lower entropy production than both baselines. The reduction is not obtained by following the static MEP alone, but by suppressing off-channel dissipation and reshaping the transient current field.

In critical lattice models, distance ($r$)-dependent correlation functions contain power laws $r^{-2\mathrm{\Delta }}$ governed by scaling dimensions $\mathrm{\Delta }$ of an underlying continuum field theory. In Monte Carlo simulations and other numerical approaches, the leading dimensions can be extracted by data fitting, which can be difficult when two or more powers contribute significantly. Here a method utilizing covariance between multiple lattice operators is developed where the $r$-dependent eigenvalues of the covariance matrix reflect scaling dimensions of individual field operators. This disentangling of scaling dimensions is demonstrated explicitly for conformal field theories. The computational scheme is first tested on the critical point of the two-dimensional Ising model, where the two primary scaling dimensions and their respective two lowest descendant dimensions are extracted. The three-dimensional Ising model is studied next, revealing the two relevant primaries and their lowest descendants to high precision. For a more challenging case, the tricritical Ising point in two dimensions is studied with the Blume-Capel (diluted Ising) model. Here the scaling dimensions of all three fully symmetric (under lattice point group and spin-inversion transformations) primary operators are successfully isolated along with the leading descendants. The eigenvectors in the space of the two relevant primary operators are also studied and give useful information on the boundary between the ordered and disordered phases in the neighborhood of the tricritical point. Finally, the crossover from regular to tricritical Ising scaling is investigated on several points on the phase boundary of the Blume-Capel model away from its tricritical point. The scaling of the eigenvalues corresponding to tricritical descendant operators are found to be remarkably stable even far from the tricritical point. The covariance method represents a simple extension of standard analysis of correlation functions and can significantly enhance the utility of Monte Carlo simulations and other computational methods in studies of classical and quantum criticality.

We theoretically investigate how information flows when two particles interact with each other. Understanding the physical mechanisms of directional information flow is crucial for advancing information thermodynamics and stochastic computing. However, the fundamental connection between mechanical motion and causal information transfer remains elusive. To focus only on essential effects of physical dynamics, we examine two interacting Brownian particles confined in a one-dimensional potential. By simulating their Langevin dynamics, we quantify the causal information exchange using transfer entropy. We demonstrate that a mass asymmetry inherently breaks the symmetry of information flow, inducing a net directional transfer from the heavier to the lighter particle. Physically, the heavier particle, possessing larger inertia and higher active information storage, retains the memory of its trajectory longer against thermal fluctuations, thereby acting as a source of information. We analytically clarify that this net transfer is governed by a competition between the difference in memory capacity and the predictability of the particle trajectories. Furthermore, we reveal that the net information flow scales logarithmically with the mass ratio. These findings provide essential insights into the physical significance of transfer entropy and the nature of information flow in general physical systems.