Papers

The main goal of this paper is to demonstrate the application of metric-based mesh adaptation to real gas problems and highlight the benefits particularly when complex geometries are considered. We use the Hessian of the temperature solution as an indicator to dictate where the mesh needs refinement or coarsening. In the context of hypersonic flow simulations, these methods are not widely adopted since unstructured meshes often result in poor surface heating predictions. The present work aims to demonstrate the great flexibility metric-based mesh adaptation provides when it comes to predicting complex flow features while still maintaining comparable surface heating predictions. We consider two test cases: (a) a supersonic flow over a hemisphere and show that comparable surface heating is obtained by applying mesh adaptation and by employing hexahedra instead of prisms in the boundary layer mesh; (b) we consider a more realistic test case of a hypersonic flow of a C02-N2 mixture past a 70 degree sphere cone atmospheric entry capsule. For the second test case, similar surface heating predictions are obtained compared to more conventional block structured DPLR simulations. Furthermore, for the adapted unstructured simulations, the geometries of the eight Reaction Control System (RCS) jet on the back shell were taken into account. This highlights the ability of these methods to deal with complex geometries that are typically out of reach for block structured approaches.

Arrays of ultracold neutral atoms, also known as Rydberg atom arrays, are rapidly developing into a powerful and versatile platform for quantum simulation. However, theoretical predictions about the bulk quantum phases of matter present in these systems have often diverged from experimental realizations on finite-sized arrays due to the strong effects of the boundaries. Here we propose a general, experimentally straightforward strategy to mitigate the effects of the boundaries and thus enable finite-sized arrays to stabilize bulk-like quantum order. Our scheme makes use of the properties of the ubiquitous disordered phase in Rydberg systems, driving the boundaries into an unbiased set of configurations that depend on the bulk physics. We numerically demonstrate the efficacy of this protocol in one- and two-dimensional systems on both ordered and critical phases.

Light with complex structures in polarization, phase and amplitude, has attracted a lot of attention in a broad range of applications and fundamental studies in classical and quantum optics. Along with the increased interest in structured light comes a need for efficient modulation platforms operating simultaneously for many modes. Multi plane light conversions (MPLC), i.e., multiple consecutive phase modulations in combination with free space propagation, have enabled such unitary transformations, which are usually built by bulky optical components, limited to scalar modulation, or rely on advanced nanofabrication techniques. Here, we demonstrate an efficient, monolithic MPLC architecture through direct laser writing in standard fused silica glass, resulting in a device with a compact form factor of only a few cubic millimeters. Our scheme is based on volumetric engineering of the glass's birefringence through laser-written nanogratings, which enables spatial control over full vectorial light structures. To showcase the approach's potential for integrated multimode-multipath optical networks, we demonstrate multi-mode unitary transformations, mode conversions, and complex beam-splitting for scalar light. We further extend the MPLC operation to vectorial light and implement various polarization-controlled spatial mode operations as well as the transformation of the topology of an optical Skyrmion. Finally, we highlight our scheme's promise for optical communications and implement a miniaturized multiplexer for spatial modes and polarization operating at telecom wavelength.

Metasurfaces have emerged as a powerful platform for compact optical sensors by replacing bulky lenses with flat arrays of subwavelength nanostructures. In precision optical metrology, the simultaneous mapping of a beam's polarization state and wavefront is crucial for real-time diagnostics of stress-induced birefringence and surface flatness. To achieve this in a compact footprint, existing metasurfaces typically partition their aperture into discrete zones, which inherently restricts the light-gathering efficiency and numerical aperture of the system. Here we demonstrate an inverse-designed metasurface that integrates full-Stokes polarimetry and Shack-Hartmann wavefront sensing within a single, continuous aperture in the visible spectrum. By leveraging an adjoint optimization approach to independently control the geometry and rotation of each nanostructure, we break the aperture-sharing paradigm and utilize the entire pixel area for all channels. When coupled with a shallow neural network to automate peak identification and correct for hardware non-idealities, our device yields a mean polarization reconstruction error of only 0.046 across 100 test states on the Poincaré sphere, while simultaneously maintaining the precise focal-spot tracking required for sensitive wavefront tilt detection. This work highlights the capacity of inverse design to generate multifunctional, non-intuitive flat optics that outperforms its traditional counterparts.

Gradual reverse-bias breakdown in metal-halide perovskite diodes and solar cells is thought to originate from hole tunneling through steep bands in an ionic depletion region near the electron transport layer after positively charged iodine vacancies accumulate near the hole-transport layer (HTL). However, typical reported mobile ion concentrations near $1\times10^{17}$ cm$^{-3}$ are too small to quantitatively explain significant tunneling current densities and (Zener) breakdown observed near $-5$ V. Here, we show that inferred mobile ion concentrations increase by more than 100$\times$, to over $1\times10^{18}$ cm$^{-3}$, within just three minutes of reverse bias at $-6.0$ V in p-i-n perovskite diodes. We attribute the increase in mobile ion concentration to iodide oxidation and the resulting iodine vacancy creation which must be balanced by reduction reactions near the HTL. Thin and sub-optimal HTL coverage leads to direct contact between the transparent conducting electrode and perovskite and facilitates electron transfer and reduction, enabling the creation of even larger inferred mobile ion concentrations ($\sim1\times10^{19}$ cm$^{-3}$) and leading to faster degradation under reverse bias. This explains previous work that showed increased breakdown voltages and improved reverse-bias stability by implementing thick, uniform HTLs.

When cells collide, they often exhibit "contact inhibition of locomotion" (CIL), a behavior in which cells repolarize and migrate away from the site of contact. Experimental CIL outcomes are highly variable - why? Here, we develop a minimal stochastic model to quantify how intrinsic noise in cell polarity, arising from the finite number of signaling molecules, influences CIL decision-making. We simulate polarization dynamics by tracking individual Rho GTPase proteins that diffuse and switch stochastically between the cell membrane and cytosol. In the absence of cell-cell contact, the polarity axis diffuses rotationally - the cell's orientation wanders - with a diffusion coefficient that decreases as Rho GTPase copy number increases. Assuming that cell-cell contact inhibits Rho GTPase activation, we investigate how contact geometry, duration, and strength affect CIL sensitivity. At low protein copy number, weak, brief, or spatially narrow contacts are masked by molecular noise. In contrast, at high protein copy number, intrinsic polarity noise is negligible, and randomness in CIL response is more likely to reflect the variability from collision to collision in the cell-cell contact properties.

Artificial Intelligence (AI) surrogate models provide a computationally efficient alternative to full-physics simulations, but no public datasets currently exist for training and validating models of high-explosive-driven, multi-material shock dynamics. Simulating shock propagation is challenging due to the need for material-specific equations of state (EOS) and models of plasticity, phase change, damage, fluid instabilities, and multi-material interactions. Explosive-driven shocks further require reactive material models to capture detonation physics. To address this gap, we introduce the High-Explosives and Affected Targets (HEAT) dataset, a physics-rich collection of two-dimensional, cylindrically symmetric simulations generated using an Eulerian multi-material shock-propagation code developed at Los Alamos National Laboratory. HEAT consists of two partitions: expanding shock-cylinder (CYL) simulations and Perturbed Layered Interface (PLI) simulations. Each entry includes time series of thermodynamic fields (pressure, density, temperature), kinematic fields (position, velocity), and continuum quantities such as stress. The CYL partition spans a range of materials, including metals (aluminum, copper, depleted uranium, stainless steel, tantalum), a polymer, water, gases (air, nitrogen), and a detonating material. The PLI partition explores varied geometries with fixed materials: copper, aluminum, stainless steel, polymer, and high explosive. HEAT captures key phenomena such as shock propagation, momentum transfer, plastic deformation, and thermal effects, providing a benchmark dataset for AI/ML models of multi-material shock physics.

Coherent surface scattering imaging (CSSI) is an emerging experimental technique uniquely suited to probing the structure of thin nanostructures. In these experiments, a specimen is placed on a substrate, and a series of X-ray diffraction patterns is collected at grazing incidence angles as the specimen is rotated. However, reconstructing the specimen's 3D structure from the data is challenging due to dynamical scattering effects induced by the experimental geometry and the lack of direct phase measurements. Specifically, the data involves nonuniformly sampled Fourier-transform values of the specimen density, and failure to effectively address this nonuniformity can lead to errors or degraded performance. Here we introduce a mathematical inversion framework that combines iterative-projection-based phasing techniques with new fast nonuniform Fourier inversion methods to efficiently reconstruct isolated 3D structures from their CSSI rotation-series data. We also analyze the theoretical properties of CSSI reconstruction to derive requirements on experimental parameters and characterize solution uniqueness. We validate our approach using CSSI data simulated from a conical Siemens star and a porous medium, demonstrating that high-resolution 3D structures can be reconstructed even in the presence of significant dynamical scattering, from data collected at as few as one or two incident angles. More broadly, the presented nonuniform reconstruction framework provides a foundation for solving challenging generalizations of the phase problem in which measurements involve nonlinear combinations of nonuniformly sampled Fourier values.

Three-dimensional (3D) wide-field fluorescence microscopy is a widely used modality for volumetric imaging, but suffers from characteristic out-of-focus blur. Existing reconstruction methods either struggle to operate on high-dimensional volumes or fail to provide credibility characterization of the reconstruction. In this work, we introduce Volumetric Transport (VOLT), a 3D-native probabilistic framework for wide-field fluorescence microscopy reconstruction. VOLT combines a transport-based formulation that maps degraded measurements to clean volumes via stochastic interpolants with a 3D-native anisotropic network that separates lateral and axial processing. This design operates directly in voxel space and achieves improved scalability to large volumes without relying on slice-wise approximations. We develop both stochastic (SDE) and deterministic (ODE) variants within the same framework. We validate VOLT on simulated wide-field microscopy datasets. Our results show that VOLT significantly improves reconstruction quality in both lateral and axial directions while providing voxel-wise credibility estimates.

It is a widely accepted paradigm that collisionless magnetic reconnection proceeds at a universal fast rate of $\sim0.1$ when normalized to a properly defined reconnecting magnetic field and Alfvén speed, effectively independent of the macroscopic system size. This conclusion, derived primarily from kinetic simulations of classical Harris current sheets with kinetic-scale thickness, stands in contrast to results from forced reconnection and island coalescence, where the rate significantly depends on the system size. Here, we reconcile this disparity by performing a rigorous scaling study using both particle-in-cell and Hall magnetohydrodynamic simulations. We demonstrate that when the global magnetic configuration is self-consistently preserved by scaling the initial current sheet thickness proportionally with the system size, the “universal” fast rate disappears. Instead, the reconnection rate decreases as the system size increases. These results indicate that dependence on macroscopic scales is not peculiar to specific geometries but is a fundamental property of collisionless reconnection, effectively unifying the Harris sheet with other configurations exhibiting size-dependence.

We demonstrate a low noise, high-gain, resonant optical amplifier that combines injection locking with feed-forward cancellation of residual phase noise. The wavelength-agnostic architecture uses a commercial semiconductor diode laser as a power amplifier while preserving the spectral purity of a weak reference. Although injection locking enforces phase coherence, finite residual phase noise within the locking regime limits high-fidelity transfer of low phase noise from the reference laser to the injection-locked laser, particularly at large gain. Here, the residual phase error is measured via optical heterodyne detection and canceled using feed-forward phase correction. Compared to injection locking alone, the amplifier achieves up to 38 dB phase-noise reduction at Fourier frequencies above 1 kHz for injection ratios down to -57 dB. This approach enables ASE-free amplification of low-power, low-noise optical references, including individual lines from optical frequency combs.

We present a computational study of diffusion synthetic acceleration (DSA) for the monoenergetic, isotropically scattering $S_N$ transport equations, discretised in space by a polytopic discontinuous Galerkin method. Using a discrete ordinates angular discretisation, we construct the DSA correction with an interior-penalty diffusion operator and compare a classical symmetric interior penalty (SIP) formulation with a modified interior penalty (MIP) variant, together with homogeneous Dirichlet and Marshak (Robin) diffusion boundary conditions imposed weakly in the DG framework. We quantify the observed convergence behaviour of the resulting source iteration across variations in optical thickness, scattering ratio, angular quadrature, mesh refinement, polynomial degree and mesh anisotropy on families of bounded Voronoi meshes. The results show that MIP-based DSA remains robust across the parameter ranges tested, whereas SIP-based DSA can lose robustness in the intermediate regime. In challenging optically thick, highly scattering settings, the observed convergence factors for the MIP-based schemes are typically below $0.6$.

Two-path complementarity in polarized interferometric fields is reconsidered by retaining the complete path–polarization density matrix instead of reducing the description to the path degree of freedom from the outset. The familiar relation connecting the Cartesian visibility components, path predictability, and reduced-state mixedness is recovered as a marginal consequence of the reduced path state and is not interpreted as a new complementarity law. Attention is focused instead on the full path–polarization description in a real reference basis adapted to the path and linear-polarization degrees of freedom. Within this framework, the normalized purity separates naturally into path, polarization, and path–polarization-correlation contributions, while the antisymmetric sector provides a sector-resolved measure of cohesion. The resulting decomposition identifies which parts of the complete state store phase-sensitive interferometric coherence and which contributions are removed when polarization is traced out. The formalism therefore provides a sector-resolved accounting of complementarity within the full path–polarization state and clarifies the connection between reduced visibility loss, polarization marking, path–polarization correlations, and quantum-eraser recovery. The present article establishes the framework and associated purity decomposition; a more detailed exploration of full path–polarization complementarity and its dynamical aspects is left for future work.

Understanding social polarization requires integrating insights from psychology, sociology, and complex systems science. Agent-based modeling provides a natural framework to combine perspectives from different fields and explore how individual cognition shapes collective outcomes. This study introduces a novel agent-based model that integrates two cognitive and social mechanisms: the desire to be unique within a group (optimal distinctiveness theory) and the tendency to simplify complex information (cognitive compression). In the model, virtual agents interact in pairs and decide whether to adopt each other's opinions by balancing two opposing drives: maximizing opinion diversity within their local social group while simplifying the overall opinion landscape, with both evaluated using Shannon entropy. We show that the combination of these mechanisms can reproduce real-world patterns, such as the emergence of distinct heterogeneous opinion clusters. Moreover, unlike many existing models where opinions become fixed once opinion groups form, individuals in our model continue to adjust their opinions after clusters emerge, leading to ongoing variation within and between opinion groups. Computational experiments reveal that polarization emerges when local group sizes are moderate (consistent with Dunbar's number), while smaller groups cause fragmentation and larger ones hinder distinct cluster formation. Higher cognitive compression increases unpredictability, while lower compression produces more consistent group structures. These results demonstrate how simple psychological rules can generate complex, realistic social behavior and advance understanding of polarization in human societies.

Machine learning emulators have shown extraordinary skill in forecasting atmospheric states, and their application to global ocean dynamics offers similar promise. Here, we adapt the GraphCast architecture into a dedicated ocean-only emulator, driven by prescribed atmospheric conditions, for medium-range predictions. The emulator is trained on NOAA's UFS-Replay dataset. Using a 24 hour time step, single initial condition, and without using autoregressive training, we produce an emulator that provides skillful forecasts for 10-15 day lead times. We further demonstrate the use of Mahalanobis distance as loss that improves the forecast skill compared to the Mean Squared Error loss by explicitly accounting for the correlations between tendencies of the target variables. Using spatial correlation analysis of the forecasted fields, we also show that the proposed correlation-aware loss acts as a statistical-dynamical regularizer for the slow, correlated dynamics of the global oceans, offering a better background forecast for downstream tasks like data assimilation.

We demonstrate dynamical control of the effective spin-spin interaction, dominated by Fermi-contact interaction, in a hybrid spin system via parametric modulation. We show that, in an alkali-noble-gas comagnetometer, periodic modulation of the direction of the electron spin polarization with respect to the nuclear polarization leads to a Floquet-induced renormalization of the spin-exchange coupling, governed by a zeroth-order Bessel function. This effect enables continuous tuning and suppression of the effective interaction strength without altering the intrinsic properties of the system. We develop a theoretical model that supports the experimental measurements. The results establish a general mechanism for controlling interaction strengths in hybrid atomic systems and provide new opportunities for precision measurements and quantum memories.

Prompted by misconceptions in the recent literature, we review the justifications for naturalness arguments and Occam's razor found in Bayesian statistics. We discuss the automatic Occam's razor that emerges in Bayesian formalism, bringing together points of view from diverse fields, including statistics, social sciences, physics and machine learning. In pedagogical calculations, we demonstrate that this automatic razor disfavors unnatural models in which predictions must be fine-tuned to agree with observation.

A crucial question throughout statistics is whether an observed correlation between two variables is a direct correlation or only an indirect one mediated by a confounder. We organize the existing nonlinear measures of direct correlation into two families, each with a systematic construction: (i) removing the direct correlation from the joint distribution and quantifying the resulting distributional shift, and (ii) intervening on one variable via do-calculus and quantifying the response of the other. For every Kullback-Leibler-based measure in either family we propose a Jensen-Shannon-based regularized analogue; the regularized measures take values in $[0,1]$, satisfy the metric property, and are free of the singularities of the Kullback-Leibler divergence. We analyze the achievable upper bound of each regularized measure under the observed marginals, and derive the maximal value each measure can attain when only the alphabet sizes of the variables are fixed; the maxima admit closed forms built on a single binary-entropy function. The measures are compared on a decision-making model and on three public datasets (Titanic survival, UCI Adult income, and the 1973 Berkeley graduate admissions), with bootstrap confidence intervals for every reported value.

Measuring field-aligned currents (FACs) using magnetic field observations provides a powerful means to probe the multi-scale interactions between the magnetosphere, ionosphere and thermosphere. In this study, we benchmark the curlometer technique using Swarm spacecraft observations and simulations of the coupled magnetosphere-ionosphere system. We begin by correlating current density curlometer estimates derived from Swarm tetrahedra with varying spatial scales and barycentre locations. This confirms an apparent departure from stationarity for FACs at spatio-temporal scales below 100 km where measurements appear highly uncorrelated. We then analyse simulated magnetic perturbations, where four-point measurements can always be constructed. This shows how, even at meso-scales of hundreds of kilometres, time-shifted FAC estimates can diverge significantly from this ground truth. In both observational and simulated data we find poor tetrahedral configurations can produce spurious perpendicular currents due to numerical instability in the inversion process. This can be mitigated using appropriate quality metrics and high-quality FAC reconstructions still achieved with a tetrahedral face well-aligned to the local magnetic field. These results highlight the dynamic nature of FACs at large as well as small scales, and underscore the substantial advantages of true four-point observations for their accurate analysis.

Preparing correlated quantum states is essential for emerging technologies, but remains challenging in many-body systems. Here we propose a dissipative protocol that engineers nonreciprocal, energy-selective transitions to steer dipolar quantum systems toward desired many-body states. This is realized by introducing two types of controllable dissipative auxiliary atoms that act as nonreciprocal excitation and de-excitation channels, respectively, enabling a directional walk in Hilbert space. This approach enables stabilization of states across the many-body spectrum, not limited to the ground state and requiring no a priori knowledge of the Hamiltonian. Our approach is designed for neutral atoms in dipolar Rydberg arrays, but applies broadly to setups with similar capabilities, providing a flexible and scalable framework for state preparation in programmable platforms.

We highlight the important role of the Fourier transform in deriving inversion formulas for the integral transforms of tomographic imaging. We demonstrate this principle by deriving inversion formulas for the divergent beam transform and the V-line transform, the latter arising in contemporary models of single-scattering optical tomography.

We present IncompressibleNavierStokes.jl, an open-source Julia package for solving the incompressible Navier–Stokes equations on staggered Cartesian grids. The package features matrix-free, hardware-agnostic kernels that are compiled from a single source for multi-threaded CPU or GPU execution, and hand-written adjoint kernels for all discrete operators, enabling efficient reverse-mode automatic differentiation through the entire solver. This differentiability allows neural network closure models to be trained a-posteriori while embedded in a large-eddy simulation. Memory optimizations permit double-precision direct numerical simulations at resolutions up to $840^3$ on a single GPU. The software design, numerical methods, hardware performance, and integration of neural network closure models are described, and results for turbulent channel flow are validated against reference data.

AtomTwin$.$jl is an open-source Julia package for developing and simulating quantum protocols, hardware configurations and building digital twins for neutral-atom quantum processors and related atomic quantum devices. AtomTwin operates between mathematical models and physical devices; modeling atoms, optical tweezers, laser fields, atomic motion, interactions, and noise processes natively from physical geometry and parameters, without requiring users to define Hamiltonians manually. The package provides hardware-level instruction sequences, high-performance solvers for coupled quantum and classical dynamics, and a ready-to-use model for ytterbium-171 atoms in an extensible framework designed to accommodate a greater variety of atomic species and hardware components in the future. This paper describes the software architecture, performance benchmarks against existing toolboxes, and a demonstrated end-to-end application: preparation of a logical Bell state in the $[[4,2,2]]$ error-detecting code with four $^{171}$Yb atoms in moveable tweezers.

A linearly polarized Gaussian beam, carrying zero net spin angular momentum, is conventionally not expected to exert optical torque or induce rotational motion in birefringent microparticles. When such a beam is tightly focused, the constituent left- and right-circular polarization components separate spatially due to spin-orbit interaction, commonly known as the spin Hall effect of light. However, this separation is at wavelength scales and is also axially symmetric, resulting in zero net spin angular momentum, and concomitantly no optical torque near the focal plane. Here, we demonstrate that this limitation can be overcome using several commonly encountered asymmetric illumination modalities that break the axial symmetry of the focusing system, thereby disrupting the symmetric separation of the spin components for the same linearly polarized Gaussian beam. As a consequence, trapped microparticles experience a tunable optical torque and exhibit rotational motion with distinct rotational frequencies at the same input power. The particles also undergo controlled reversal of the rotation direction simply by rotating the incident plane of polarization using a half-wave plate. Despite their apparent diversity, all these methods share the same physical origin rooted in asymmetric illumination. These results establish an experimentally accessible and minimal strategy for realizing controllable optical rotation devices exploiting spin-orbit optomechanics without requiring intrinsic angular momentum in the light.

Quantum Fisher Information (QFI) sets the ultimate precision limit for parameter estimation and is therefore a central quantity in quantum metrology. In time-dependent many-body systems, however, maximizing QFI is a highly non-trivial task due to the combined effects of non-commutativity, control complexity, and the exponential growth of the Hilbert space. In this work, we present a physics-informed neural network (PINN) framework to address this problem through the learning of counter-diabatic quantum dynamics. Our approach combines a variational PINN formulation with a Magnus-expansion treatment of time-ordered evolution, enabling the adiabatic gauge potential and the scheduling function to be inferred directly from the underlying physics while enforcing the Euler-Lagrange structure of the protocol. The method is applied to several families of driven spin Hamiltonians, including nearest-neighbor, dipolar, and trapped-ion-inspired interactions, for systems of up to six qubits. The numerical results show that the proposed framework systematically improves over reference solutions based only on the Euler-Lagrange condition, yielding high normalized QFI together with favorable fidelity and extremal-balance metrics while preserving small phsical residuals. The analysis further shows that learning the scheduling function provides a clear performance advantage in most cases, and reveals non-trivial finite-size effects, with $q=3$ emerging as a particularly challenging regime. Although scalability remains limited by the exponential growth of the operator space and by automatic-differentiation costs, the results demonstrate that PINNs constitute a viable and physically grounded route for learning metrologically optimal control strategies in interacting quantum systems.

Nonlinear metasurfaces provide a route to compact frequency conversion by replacing phase matching and long interaction lengths with resonantly enhanced light matter interaction in subwavelength structures. Extending this capability to continuous wave (CW) operation is particularly important for applications requiring narrow linewidth, stable frequency, and stationary optical fields, but remains extremely challenging. Here, we demonstrate CW second-harmonic generation on a transmission mode, etchless thin film lithium niobate platform enabled by a patterned silicon rich nitride metasurface. This hybrid design combines guided mode resonance coupling, low optical loss, and CMOS compatible processing while keeping most of the optical mode confined in the unpatterned lithium niobate, yielding a measured quality factor of ~2300. Clearly resolved SHG is achieved under sub-kW/cm^2 CW pumping, with a normalized conversion efficiency of 0.156 % cm^2/GW in the low power regime. Interestingly, our work reveals that CW resonant SHG in metasurfaces can exhibit pronounced transient dynamics, including power dependent resonance evolution, overshoot, and nonideal scaling. These findings establish etchless LN SRN metasurfaces as a promising platform for compact CW nonlinear photonics, and show that resonance dynamics are central to the operation and evaluation of CW driven nonlinear metasurfaces.

Injection locking of optoelectronic oscillators (OEOs) with large delay gives rise to phase dynamics that lie beyond the scope of classical single mode locking theory, including the spontaneous formation of persistent $2\pi$ phase kinks. In this work, a multiscale theoretical framework is developed that explains the origin, structure, and stability of these phase slip phenomena in injection locked (IL) OEOs operating in large-delay regime. Starting from a complex envelope delay differential equation that explicitly incorporates hard-limiting gain saturation and RF BPF dynamics, a reduced phase-only description valid for nearly constant oscillation amplitude is derived. Exploiting the separation between fast round-trip dynamics and slow inter-round-trip evolution, a two-timescale reduction yields a continuum of Adler equations governing phase difference between injected signal and oscillator as a function of round-trip time. Analytical solutions obtained for weak injection and small detuning show how initially smooth phase profiles sharpen into localized $2\pi$ phase kinks, with p kinks appearing per round trip when the injection is tuned near the pth adjacent mode. Time-domain simulations of full complex-envelope model validate the predicted phase kink formation mechanism and reveal essential role of RF resonator dynamics in determining their persistence. While the reduced phase-only model correctly predicts kink sharpening, resonator-induced amplitude excursions associated with steep phase gradients can erase the kinks when the complex-envelope trajectory fails to encircle the origin, restoring conventional phase locking. These results provide a unified physical interpretation of $2\pi$phase kinks in IL-OEOs and delineate the limits of phase-only models in the presence of large, fast phase transients, identifying a regime of frequency locking without phase locking in large delay oscillators.

The control of energy dissipation in non-spherical particle contact remains an unresolved problem. Unlike spherical contact, where the interaction reduces to a one-dimensional normal oscillator, both the effective inertia and the effective stiffness depend on the evolving contact geometry, and the impact dynamics are intrinsically coupled across translational, rotational, and tangential directions. Classical damping formulations are therefore structurally incompatible with the contact dynamics they are intended to represent. This work addresses the problem from first principles. By projecting the dynamics onto contact degrees of freedom, the interaction is shown to be governed by an instantaneous contact dynamics with a configuration-dependent projected mass and intrinsic translational–rotational coupling. Building on the exact energy–phase transformation for monotone conservative contact, we show that consistent dissipation requires a unique damping structure aligned with the underlying contact energy. The analysis leads to two central consequences. First, the admissible damping law is not empirical but fixed by the harmonic structure revealed in transformed space. Second, the appropriate coefficient of restitution for non-spherical particles is the contact-point restitution $e_{cn}$, whereas the total energy restitution $e_E$ is a geometry-dependent outcome that includes coupling-induced energy transfer. Numerical evidence based on smooth single-contact impacts confirms the theory: the resulting formulation controls $e_{cn}$ consistently across impact configurations, while the apparent variability of $e_E$ follows directly from the coupled dynamics.

We present a boundary integral formulation for steadily propagating semi-infinite plane strain tensile and shear fractures in poroelastic media. By combining fundamental solutions of plane strain poroelasticity for an instantaneous fluid source and instantaneous edge dislocations (normal and slip modes) with temporal and spatial superposition principles, we derive boundary integral equations for steadily moving fractures under the adopted hydraulic boundary conditions. These equations relate the tractions (normal and shear stresses) and the pore fluid pressure on the fracture surfaces to the fracture opening, slip, and the fluid displacement function. Assuming prescribed traction and pore fluid pressure profiles, we develop a numerical methodology to solve the governing equations for fracture opening, slip, and the fluid displacement function. The formulation is systematically verified on several relevant problems, including a tensile fracture with exponential normal loading, a stress-free tensile fracture with an imposed exponential pore fluid pressure, and a shear fracture under uniform shear loading over a finite region, demonstrating excellent agreement with analytical and semi-analytical solutions. The resulting boundary integral framework provides an accurate and efficient tool for analyzing semi-infinite steadily propagating cracks in permeable poroelastic media. By supplementing the formulation with appropriate closure relations and additional physics, such as lubrication flow in hydraulic fractures or frictional strength evolution in shear fractures, it can be used to investigate a broad range of coupled fracture-fluid problems. The approach may also be adapted to other classes of elasto-diffusive problems by modifying the underlying physical parameters.

Recent Magnetospheric Multiscale (MMS) observations report approximate equality between electric and magnetic field energy spectral densities, $\varepsilon_{0} P[\delta E]/2 \approx P[\delta B]/(2\mu_{0})$, at sub-electron scales in reconnection-driven magnetotail turbulence, interpreted as relaxation toward thermodynamic equilibrium. We derive the electric-to-magnetic energy ratio from the linear polarization of kinetic Alfvén waves and whistler-mode waves in the two-fluid framework and show that it saturates at $\mathcal{R}_{\infty}=(V_{A}/c)^{2}(m_{i}/m_{e})(\beta_{e}/2)$ deep in the sub-electron regime. Setting $\mathcal{R}_{\infty}=1$ yields the universal threshold $V_{A}/c \gtrsim \sqrt{2/[(m_{i}/m_{e})\beta_{e}]}$, which no non-relativistic space plasma satisfies. For typical magnetotail parameters, $\mathcal{R}_{\infty}\approx 2\times 10^{-3}$, approximately 500 times below the observed value, a discrepancy rooted in the non-relativistic ordering $(V_{A}/c)^{2}\ll 1$. Noise-floor estimates show that Search Coil Magnetometer and Electric Double Probe sensitivity convergence produces a spurious apparent equipartition throughout this regime. The observed equality likely reflects nonlinear dynamics, incoherent superposition of electromagnetic and electrostatic fluctuations, or instrumental noise contamination.