Fluid Dynamics
A More Rigorous Test Problem For Viscous Hydrodynamics Codes
Alexander J. Dittmann and Geoffrey Ryan
We advocate for a more stringent test problem for codes that aim to solve the equations of viscous hydrodynamics. Specifically, we discuss a nonuniform-density version of the common (uniform-density) Gaussian velocity shear test, where density gradients transverse to the direction of velocity shear cause the velocity profile to drift over time. By employing a nonunifom density, this test provides a test that the full viscous stress (and velocity shear) tensors are calculated correctly from the conserved variables, and checks the correctness of the fluxes and source terms calculated therefrom. In Appendix A, we present a detailed exposition of the Navier Stokes equations, particularly their fluxes and source terms in a variety of common coordinate systems.
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Optics · Cross-listed from quant-ph
Generalised All-Optical Cat Correction
Ari John Boon, Olivier Landon-Cardinal and Nicolás Quesada
We have generalised an all-optical telecorrection protocol for the higher orders of the cat code, and show that with these higher orders we can achieve target performance at substantially reduced iteration counts at the cost of a higher mean
photon-number. We also introduce a probabilistic scheme for correcting deformation of the state, which highlights two interesting abilities of telecorrection: to encode new sets of transformations, and to change the basis of the code. We find that for a target channel fidelity of $99.9\%$ over a channel with $1\text{ dB}$ of loss, a third-order cat code requires $70$ times fewer telecorrection iterations than a first-order one, at a cost of a $3.6$-fold increase in mean
photon-number.
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Physics and Society
Does Entry of Food-and-Drink Establishments Raise Local House Prices? Event-Study Evidence from London
Wanqi Liu and Rong Zhao
Restaurants, cafes, pubs, and takeaways are among the most visible markers of neighborhood change, yet whether their arrival is capitalised into nearby housing values remains empirically unsettled. We assemble a London-wide panel linking Land Registry prices, non-domestic EPC lodgement timings for food-and-drink establishments, and neighborhood amenity measures at the LSOA level. Our preferred annual event-study design defines treatment as the first clean-onset year in which an LSOA records at least two eligible EPC lodgements for food-and-drink establishments, after a two-year lookback with no prior entries. In this specification, pre-trend tests are not rejected in either the stacked or Sun-Abraham
estimators, and log house prices rise gradually from about 0.5% in the event year to roughly 3.4–3.7% by years four and five. The results are consistent with local amenity capitalization following commercial entry, while remaining appropriately cautious about endogenous siting and concurrent redevelopment.
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Optics
Fourth-harmonic UV light generation in integrated silicon nitride microresonators
Alekhya Ghosh, Arghadeep Pal, Haochen Yan et al. (+6 more)
Integrated silicon nitride (Si3N4) resonators have emerged as a leading platform for nonlinear photonics, yet generating light at wavelength in the ultraviolet (UV) has remained elusive in single-resonator systems. Here we report the first observation of fourth-harmonic generation reaching the blue and ultraviolet spectral regions in an integrated Si3N4 microring resonator. We systematically investigate the input-power dependence of the wavelength ranges supporting second-, third-, and fourth-harmonic generation, and study the input-power-dependent variation of the circulating fourth-harmonic signal in the UV. These results extend the operational bandwidth of integrated Si3N4 nonlinear photonic platforms to the lower edge of the material transparency window, enabling on-chip UV frequency conversion. Near-ultraviolet generation around 400 nm will enable on-chip excitation of defect-based
quantum emitters in hexagonal boron nitride, enhance Raman spectroscopy through increased scattering cross-sections at shorter wavelengths, and support compact fluorescence-based bio-imaging platforms exploiting intrinsic cellular fluorophores.
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All authors Alekhya Ghosh, Arghadeep Pal, Haochen Yan, Toby Bi, Luca O. Trinchão, Qixuan Zhou, Gustavo S. Wiederhecker, Shuangyou Zhang, Pascal Del'Haye
Biological Physics · Cross-listed from cond-mat.stat-mech
Will a Large Complex System be a Maxwell Demon?
Matthew P Leighton
Emerging evidence suggests that physical systems operating as Maxwell demons, in which some subsystem of a larger system extracts heat energy from its environment in an apparent local violation of the second law, are commonplace throughout biology. Should these findings surprise us, or is Maxwell demon behavior inevitable in sufficiently large complex systems? In this Letter we pose the question of how likely it is that a random
stochastic system with many degrees of freedom will operate as a Maxwell demon, considering null models for both continuous and discrete random dynamics. Our results show the probability of a finding a demon decreases at least exponentially, and in some cases double-exponentially, with the number of degrees of freedom, ultimately suggesting that large complex demons can only arise through a process of selection.
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Computational Physics · Cross-listed from cs.LG
On Geometry Regularization in Autoencoder Reduced-Order Models with Latent Neural ODE Dynamics
Mikhail Osipov
We investigate
geometric regularization strategies for learned latent representations in encoder–decoder reduced-order models. In a fixed experimental setting for the advection–
diffusion–reaction (ADR) equation, we model latent dynamics using a neural ODE and evaluate four regularization approaches applied during autoencoder pre-training: (a) near-isometry regularization of the decoder Jacobian, (b) a
stochastic decoder gain penalty based on random directional gains, (c) a second-order directional
curvature penalty, and (d) Stiefel projection of the first decoder layer. Across multiple seeds, we find that (a)–(c) often produce latent representations that make subsequent latent-dynamics training with a frozen autoencoder more difficult, especially for long-horizon rollouts, even when they improve local decoder smoothness or related sensitivity proxies. In contrast, (d) consistently improves conditioning-related diagnostics of the learned latent dynamics and tends to yield better rollout performance. We discuss the hypothesis that, in this setting, the downstream impact of latent-geometry mismatch outweighs the benefits of improved decoder smoothness.
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Chemical Physics
Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
Naoya Kuroda, Kenji Ishihara, Tomoya Shiota et al. (+1 more)
Machine learning interatomic potentials (MLIPs) evaluate potential energy surfaces orders of magnitude faster while maintaining accuracy comparable to first-principles calculations, and universal MLIPs that cover most of the periodic table are becoming increasingly commonplace. However, existing large-scale
datasets have limited or no coverage of heavy elements such as minor actinides crucial in the
nuclear field, and universal MLIPs are typically limited to 89 elements. Here, we constructed a heavy element
dataset HE26 containing minor actinides, based on experimental and computational literature data. By integrating this with existing molecular and
crystal datasets, we developed an open-source universal MLIP covering 97 elements, the broadest elemental coverage to date. The resulting model showed strong performance on the inorganic MPtrj and organic OFF23 test sets and promising accuracy on HE26. The
dataset and model open a pathway toward the development of energy resources and the design of novel materials, such as actinide-based high-
entropy ceramics, in the
nuclear field.
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All authors Naoya Kuroda, Kenji Ishihara, Tomoya Shiota, Wataru Mizukami
Optics · Cross-listed from cond-mat.mes-hall
Simultaneous anti-bunched and super-bunched photons from a GaAs Quantum dot in a dielectric metasurface
Sanghyeok Park, Oleg Mitrofanov, Kusal M. Abeywickrama et al. (+9 more)
Semiconductor quantum dots host a rich
manifold of excitonic complexes, including neutral
excitons that emit anti-bunched single
photons and charged
exciton complexes capable of producing super-bunched
photons via cascade emission. Accessing both emission regimes from a single emitter would open routes to novel
quantum protocols, including advanced
quantum imaging. In practice, however, emission from charged
exciton complexes is intrinsically weak, often orders of magnitude dimmer than neutral
excitons, placing simultaneous dual-mode operation out of reach. Here, we overcome this limitation by
embedding the
quantum dot in a dielectric Mie-resonant metasurface that provides order-of-magnitude photoluminescence enhancement across both neutral and charged
exciton transitions of a single GaAs
quantum dot. Under identical non-resonant pumping conditions, the emission from the neutral
exciton yields anti-bunched emission ($g^{(2)}(0) < 0.5$) and the emission from positively charged
exciton complexes shows super-bunched emission ($g^{(2)}(0) > 3.5$) with comparable count rates (~12 kHz). Crucially, super-bunching emerges only when charged
exciton emission spectrally overlaps with the Mie resonances and vanishes in un-patterned slabs, demonstrating that photonic engineering, is essential for accessing these weak
quantum light states. These results demonstrate a scalable, position-tolerant platform for harnessing the full excitonic structure of solid-state emitters.
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All authors Sanghyeok Park, Oleg Mitrofanov, Kusal M. Abeywickrama, Samuel Prescott, Jaeyeon Yu, Stephanie C Malek, Hyunseung Jung, Emma Renteria, Sadhvikas Addamane, Alisa Javadi, Igal Brener, Prasad P Iyer
Physics and Society
Energy-Optimal Allocation of Storage in Transmission Grid Networks
Emile Emery, Sébastien Aumaître and Hervé Bercegol
The deployment of renewable energy technologies supposes the connection to the power grid of many new, distributed, and variable electricity production facilities. Among the investments deeply needed for a successful shift to clean energy, electricity storage systems are key to provide power reliably, continuously and economically. Here, we are concerned with the energy that must be invested and
embodied in storage devices and in production oversizing to cope with natural variations of renewable electricity production, and compensate for any gap between production and consumption. We developed a model to analyze the variation of energy expenses with the location in the grid, capacity of storage and production oversizing. We apply it to a time scale of fluctuations of a few hours that can be taken care of by Li-ion batteries to calculate the optimal storage capacity and production oversizing yielding a maximum value of the ESOI ratio [Energy Stored On energy Invested] at a given satisfaction rate of customer demand. We evaluate these values for a rescaled present-time French power mix and two idealized zero-emission mixes (100% PV and 100% wind). In parallel, using a recently developed model of French transmission grid, a centrality-based analysis shows that locating storage at nodes of maximal installed power minimizes additional Joule losses. These results generalize existing grid-level energy return frameworks to incorporate storage sizing, placement, and transmission losses into a unified assessment of future power grid configurations.
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Fluid Dynamics
On the biogenic hydrodynamic transport of upward and downward cruising copepods
Yunxing Su, Rui Zhu, Eckart Meiburg et al. (+1 more)
Mesozooplankton aggregations undergoing vertical migrations in the upper ocean have been hypothesized to have an important role in the redistribution of carbon, nutrients, and oxygen via biogenic hydrodynamic transport (BHT). While laboratory studies have demonstrated how swarm-induced hydrodynamic instabilities can drive large-scale transport in strongly stratified environments, measurements are usually performed with model organisms that differ in morphology and swimming mode from ecologically relevant marine species. To bridge this gap, we conducted experiments with copepods and analyzed upward and downward trajectories to identify differences in flow fields, force distribution, and BHT for these two scenarios. Using two-dimensional bright-field Particle Image Velocimetry (PIV), we quantified the near-body velocity field and found that the average downward swimming speed significantly exceeds the average upward swimming speed, with the flow fields exhibiting direction-dependent characteristics. We incorporated these findings into a continuum squirmer model to estimate the swimmer-induced drift volume and mixing efficiency, focusing on the effects of the reduced
gravity of the swimmers and the density stratification of the surrounding fluid. Our simulations reveal that both the excess weight of the organisms and the fluid stratification strongly constrain the net BHT. This study provides a critical step toward integrating lab-based models of marine mesozooplankton with remote sensing data to incorporate vertical migrations into global ocean models with realistic biogeochemistry and assess their ecological significance in actively sustaining local ecosystems.
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All authors Yunxing Su, Rui Zhu, Eckart Meiburg, Monica M. Wilhelmus
Data Analysis and Statistics
Data Unfolding: From Problem Formulation to Result Assessment
Nikolay D. Gagunashvili
Experimental data in particle and
nuclear physics, particle astrophysics, and radiation protection dosimetry are collected using experimental facilities that consist of a complex system of sensors, electronics, and software. Measured spectra or cross sections are considered as Probability Density Functions (PDFs) that deviate from true PDFs due to resolution, bias, and efficiency effects. Unfolding is viewed as a procedure for estimating an unknown true PDF. Reliable estimates of the true PDF are necessary for testing theoretical models, comparing results from different experiments, and combining results from various research endeavors. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a blurred image, where the sharpness of the restored image serves as an indicator of quality. However, defining such external criteria can be challenging, particularly when a measurement has not been performed previously. This paper discusses various internal criteria for assessing the quality of the results independently of external information, as well as factors that influence the quality of the unfolded distribution.
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Chemical Physics · Cross-listed from cs.LG
Information Routing in Atomistic Foundation Models: How Task Alignment and Equivariance Shape Linear Disentanglement
Joshua Steier
What determines whether a molecular property prediction model organizes its representations so that
geometric and compositional information can be cleanly separated? We introduce Compositional Probe Decomposition (CPD), which linearly projects out composition signal and measures how much
geometric information remains accessible to a Ridge probe. We validate CPD with four independent checks, including a structural isomer
benchmark where compositional projections score at chance while
geometric residuals reach 94.6% pairwise classification accuracy. Across ten models from five architectural families on QM9, we find a linear accessibility gradient: models differ by $6.6\times$ in
geometric information accessible after composition removal ($R^2_{\mathrm{geom}}$ from 0.081 to 0.533 for HOMO-LUMO gap). Three factors explain this gradient. Task
alignment dominates: models trained on HOMO-LUMO gap ($R^2_{\mathrm{geom}}$ 0.44–0.53) outscore energy-trained models by $\sim$0.25 $R^2$ regardless of architecture. Within-architecture ablations on two independent architectures confirm this: PaiNN drops from 0.53 to 0.31 when retrained on energy, and MACE drops from 0.44 to 0.08. Data diversity partially compensates for misaligned objectives, with MACE pretrained on MPTraj (0.36) outperforming QM9-only energy models. Inside MACE's representations, information routes by
symmetry type: $L{=}1$ (vector) channels preferentially encode dipole moment ($R^2 = 0.59$ vs.\ 0.38 in $L{=}0$), while $L{=}0$ (scalar) channels encode HOMO-LUMO gap ($R^2 = 0.76$ vs.\ 0.34 in $L{=}1$). This pattern is absent in ViSNet. We also show that nonlinear probes produce misleading results on residualized representations, recovering $R^2 = 0.68$–$0.95$ on a purely compositional target, and recommend linear probes for this setting.
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Plasma Physics
Turbulent Nature of the Quasicontinuous Exhaust Regime for Fusion Plasmas
Kaiyu Zhang, Wladimir Zholobenko, Andreas Stegmeir et al. (+5 more)
We demonstrate a
turbulence mechanism that reconciles high
plasma confinement with efficient heat exhaust – a central challenge for fusion energy. Global two-fluid
turbulence simulations of the reactor-relevant Quasicontinuous Exhaust regime on the ASDEX Upgrade tokamak reveal that a quasicoherent mode drives mesoscopic oscillations of the pedestal boundary across the
magnetic separatrix and ejects ballistic filaments (blobs), reproducing both the mean profiles and turbulent fluctuations observed experimentally. This behavior arises from a synergistic interplay between kinetic ballooning modes and resistive X-point modes straddling the separatrix. These first-principles results place extrapolations to future fusion reactors on a firm physical footing.
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All authors Kaiyu Zhang, Wladimir Zholobenko, Andreas Stegmeir, Michael Faitsch, Konrad Eder, Christoph Pitzal, Frank Jenko, ASDEX Upgrade Team
Plasma Physics
On shear Alfvén wave-induced energetic ion transport in optimized stellarators
A. R. Knyazev, A. Lachmann, A. G. Goodman et al. (+4 more)
In this work, we investigate
prompt ion drift orbit losses caused by shear Alfvén waves (SAW) in quasi-symmetric (QS) and quasi-isodynamic (QI) stellarators optimized for
equilibrium confinement of energetic particles (EPs). We use the ideal reduced MHD model for SAW perturbations and study their impact on collisionless EP drift dynamics. We present a semi-analytical model for resonance between the passing EP and SAW, generalized to arbitrary quasi-symmetric configurations including the quasi-poloidal case relevant to QI
equilibria. Analysis reveals that an increase in the number of field periods suppresses stochasticity in quasi-helical (QH) and quasi-isodynamic, but not quasi-axissymmetric (QA) stellarators. We show that wave-induced transitions between passing and trapped orbits cause significant losses in QA and QH, but not in QI configurations. For the considered
equilibria at scales relevant to fusion power plants (FPPs), we numerically determine SAW amplitudes needed to induce
prompt loss of fusion-born alpha particles. Using the weighted Birkhoff averaging technique, we confirm that the onset of
prompt losses across all orbit classes occurs with the onset of stochasticity in ion motion. This motivates extending the stochasticity-onset criterion beyond passing orbits in future work.
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All authors A. R. Knyazev, A. Lachmann, A. G. Goodman, A. S. Hyder, M. Czekanski, D. Spong, E. J. Paul
Medical Physics
Using a Stifneck Select Collar$^{\mathrm{TM}}$ for hands-free semiautomatic blood flow measurements: a user study
Reinhard Fuchs, Nathalie Sumrah, Maximilian N. Möbius-Winkler et al. (+6 more)
Objective: The percentage of long-term survival in out of hospital cardiac arrest cases is remarkably low. One approach would be to increase the effectiveness of cardiopulmonary resuscitation (CPR), which is currently not measurable in a quantifiable way. The most significant challenge in providing a mobile solution for CPR evaluation is a mobile, hazard free sensor attachment with high usability. Methods: We present a sensor attachment solution usable for semiautomatic ultrasonic (US) Doppler measurements. Components are attached to a Stifneck Select Collar$^{\mathrm{TM}}$ (Laerdal). An inflatable cushion (TR-Band$^{\mathrm{TM}}$, Terumo) allows adjustable contact pressure. A clinical study was conducted in which the system was evaluated based on comfort, pain, sensor support, the viability of Doppler signals, and the absence of skin irritations. Results: The system was utilized in a prospective study involving 102 healthy probands. On a scale between 1 (Low) and 10 (Intense), ratings were 1.19 (SD 0.46), 6.52 (SD 1.78), and 9.95 (SD 0.32) for pain, comfort, and support, respectively. The average duration of application was 31.19 minutes (SD 16.75 minutes). Audible Doppler signals were achieved in 92.2 % of the probands, and Doppler curve evaluation was usable in 73.5 %. No skin irritations were observed. Conclusion: A hands free sensor attachment for a US probe was developed that caused no significant complaints by healthy study volunteers. Medical users assessed its attachment as robust. Significance: With its adjustable positioning and easy attachment, the Stifneck modification can form a basis for a mobile US Doppler device, capable of evaluating carotid artery flow during CPR.
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All authors Reinhard Fuchs, Nathalie Sumrah, Maximilian N. Möbius-Winkler, Georg Stachel, Michael Schultz, Ulrich Laufs, Thomas Neumuth, Michael Unger, Karsten Lenk
Geophysics
Probabilistic and Alarm-Based Evaluation of a b-Value-Driven Deep Learning Earthquake Forecast
Jonas Köhler, Wei Li, Johannes Faber et al. (+2 more)
We evaluate the
forecasting performance of a deep learning model, originally introduced as a pattern-extraction framework, that operates on the spatiotemporal
evolution of seismic b-values in a short-term
forecasting context. Model output is rescaled to account for training on balanced
datasets and evaluated relative to a spatial base-rate model using the Brier Skill Score (BSS). Absolute skill values are small, but mean BSS values are consistently positive, including at locations where Mw geq 5 earthquakes occurred during the test period, indicating information content beyond historical seismicity alone. Alarm-based evaluation using Molchan diagrams shows elevated event capture rates at low alarm fractions (5.88 percent of events captured at 1 percent area under alarm), indicating discrimination exceeding random and purely spatial reference models under constrained alarm conditions. Comparison with ETAS-derived triggered probabilities further reveals a weak positive correlation, suggesting partial sensitivity of the model output to seismic regimes characterized by enhanced clustering and recent activity, while remaining distinct from classical aftershock-based descriptions. Together, these results indicate that spatiotemporal variations in b-values contain a persistent, though limited, signal relevant to probabilistic earthquake
forecasting, yielding marginal but consistent improvements over baseline models across complementary evaluation frameworks.
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All authors Jonas Köhler, Wei Li, Johannes Faber, Georg Rümpker, Nishtha Srivastava
Applied Physics · Cross-listed from cond-mat.mes-hall
Current-control of chaos and effects of thermal fluctuations in magnetic tunnel junctions
Ryo Tatsumi, Shinji Miwa, Hiroaki Matsueda et al. (+1 more)
We theoretically investigate the chaotic behavior of
spin-torque
ferromagnetic resonance in
magnetic tunnel junctions (MTJs) with perpendicular
magnetic anisotropy under thermal fluctuations. By calculating the Lyapunov exponent based on the Landau-Lifshitz-Gilbert equation, we demonstrate that an MTJ characterized by a double-well potential, composed of uniaxial
magnetic anisotropy and an external
magnetic field, exhibits chaotic magnetization dynamics that can be controlled by means of the DC current bias. Furthermore, we find that thermal fluctuations help to induce these chaotic magnetization dynamics, which can be regarded as noise-induced chaos. This research provides a basis for
brain-inspired computing using spintronic devices and advances the understanding of the interplay between thermal fluctuations and chaos in magnetization dynamics.
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All authors Ryo Tatsumi, Shinji Miwa, Hiroaki Matsueda, Takahiro Chiba
Chemical Physics
A Perturbative Super-CI Approach for orbital optimization in Two-Component relativistic CASSCF
Yang Guo and Achintya Kumar Dutta
In this work, we develop a new orbital optimization approach, perturbative Super-CI (Super-CIPT), for the two-component complete active space self-consistent field (2C-CASSCF) method. By variationally optimizing spinor orbitals and consistently incorporating
spin–orbit coupling (SOC) at the orbital level, the 2C-CASSCF method enables a simultaneous treatment of relativistic effects and static correlation. The Super-CIPT approach demonstrates robust convergence behavior and is applicable to systems under strong SOC. The inclusion of Gaunt or Breit term via the atomic mean field approximation yields the most accurate results, with errors dropping below 2% for halogens. We systematically assess the performance of 2C-CASSCF on
spin-orbit splittings (SOSs) of selected p-block elements. Results show that 2C-CASSCF outperforms conventional one-component (1C) CASSCF. This work establishes 2C-CASSCF with Super-CIPT as a reliable and efficient approach for multireference relativistic
quantum chemistry.
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Space Physics · Cross-listed from cond-mat.soft
Nature of granular drag in microgravity
Tivadar Pongo, Tianhui Liao, Jinchen Zhao et al. (+4 more)
The influence of
gravity on the drag force acting on a projectile impacting granular media is investigated experimentally via embedded inertial measurement unit (IMU) sensor and numerically through discrete element method (DEM) simulations. As
gravity approaches zero, inertial drag dominates, yielding qualitatively different scaling laws and cavity dynamics. Analogous to fluid dynamics, we define a dimensionless granular drag coefficient $C_{\rm gd}$, which is found to stay largely at a constant $\sim 1.2$ in microgravity while an additional term inversely proportional to impact velocity arises in the presence of
gravity. The constant term can be understood from momentum transfer along the penetration direction while the additional term suggests the influence of internal stress built-up due to
gravity. Similar discrepancy is also found for the initial peak of the drag force. This analogy provides novel insights into the nature of granular drag in microgravity and sheds light on future space missions.
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All authors Tivadar Pongo, Tianhui Liao, Jinchen Zhao, Valentin Dichtl, Simeon Voelkel, Raul Cruz Hidalgo, Kai Huang
History and Philosophy of Physics
The Informational Observer in Relational Quantum Mechanics
Bethany Terris
Relational
Quantum Mechanics (RQM) treats
quantum states as observer-dependent facts rather than absolute properties. While this relational stance is conceptually attractive, it raises concerns about empirical confirmation, particularly in multi-observer scenarios. Existing responses within RQM focus on securing agreement between observers by strengthening the status, stability, or accessibility of recorded outcomes. However, they leave open a more basic question: what grounds the persistence of an observer across time? Scientific observation presupposes stable records and the capacity to relate outcomes across successive measurements. We argue that the minimal definition of the observer in RQM as a merely interacting physical system is insufficient to support this requirement. We propose a complementary account of the observer that distinguishes physical interaction from informational coherence, and show how this distinction supports empirical confirmation in Wigner's friend-type scenarios.
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Fluid Dynamics
Phase-field investigation of non-isothermal solidification coupled with melt flow dynamics
Timileyin David Oyedeji, Aaron Brunk, Yangyiwei Yang et al. (+3 more)
Solidification, coupled with melt flow, plays a critical role in determining the microstructure and properties of materials in several manufacturing processes. Phase-field models coupled with the Navier-Stokes equations are widely used to model and simulate these dynamics. However, most existing models neglect essential thermodynamic couplings, particularly the capillary (Korteweg) stress in the momentum equation. This stress, which arises from the coupling between the phase field and the melt flow, accounts for thermal capillary effects during non-isothermal solidification. Neglecting it leads to models inconsistent with non-
equilibrium thermodynamics and incapable of capturing capillarity-driven melt flow. In this work, we present a thermodynamically consistent, non-isothermal phase-field model for solidification coupled with melt flow, incorporating cross-coupling terms and explicitly including the Korteweg stress in the momentum equation. Model validation is performed for solidification-only cases, followed by simulations of dendritic growth under melt flow. The results show that thermal capillary effects induce flow near the interface, influencing dendrite tip velocity and morphology. Simulations under forced convection further demonstrate asymmetric dendrite growth due to the imposed flow field. Additionally, we numerically demonstrate the influence of viscosity interpolation schemes on enforcing the no-slip boundary condition in phase-field models with melt flow.
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All authors Timileyin David Oyedeji, Aaron Brunk, Yangyiwei Yang, Herbert Egger, Holger Marschall, Bai-Xiang Xu
Chemical Physics · Cross-listed from quant-ph
Nuclear interference by electronic de-orthogonalisation
Matisse Wei-Yuan Tu, Angel Rubio and E.K.U. Gross
Interference is a universal consequence of superposition, yet in composite
quantum systems it can encode correlations between subsystems. We show that in coupled electron-
nuclear dynamics, interference in the
nuclear density can arise dynamically even when it is initially absent. Starting from a superposition of orthogonal Born-Oppenheimer electronic states, we demonstrate within the exact factorisation framework that genuine non-adiabatic electron-
nuclear correlations induce de-orthogonalisation of the electronic factors, thereby generating interference terms in the
nuclear density. Such interference has no counterpart in adiabatic
evolution. Unlike conventional
nuclear wave-packet interference or interference that merely reflects electronic coherence in a chosen basis, the effect identified here is a manifestation of the compositeness of the full electron-
nuclear state.
Nuclear density interference thus emerges as a direct dynamical signature of correlated
quantum motion in composite systems.
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Computational Physics
Mind the Gap: Where Analog Ising Machines Cease to Minimize the Ising Hamiltonian
E.M. Hasantha Ekanayake, Arvind R. Venkatakrishnan, Francesco Bullo et al. (+1 more)
The design of nonlinear dynamical systems whose gradient flows minimize the Ising
Hamiltonian has emerged as a compelling paradigm for realizing Ising machines, forming the foundation of architectures including coherent Ising machines, simulated bifurcation machines, oscillator-based Ising machines, and dynamical Ising machines. Here, we identify a fundamental structural feature shared by these systems a functional parameter gap defined by the separation between the destabilization of the trivial state and the stabilization of Ising-encoded states. We demonstrate that this separation creates a finite parameter interval in which convergence to an Ising-encoded solution is no longer functionally guaranteed, and the resulting
evolution is dictated by the spectral structure of the Jacobian at bifurcation. Subsequently, by introducing a hybrid dynamical framework that reshapes the bifurcation
topology, we establish a principled pathway for modulating this parameter gap. The parameter gap thus emerges as a unifying structural principle for the analysis, design and optimization of analog Ising machines.
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All authors E.M. Hasantha Ekanayake, Arvind R. Venkatakrishnan, Francesco Bullo, Nikhil Shukla
Atomic Physics · Cross-listed from quant-ph
Fingerprint Recognition of Partial Discharge Signals in Deep Learning Enhanced Rydberg Atomic Sensors
Yi-Ming Yin, Qi-Feng Wang, Yu Ma et al. (+16 more)
Partial discharge originates from microscopic insulation imperfections in high-voltage apparatus and is widely considered a critical marker of incipient deterioration. Conventional partial discharge detection methods are typically constrained by limited bandwidth and often rely on predefined feature extraction, which impedes reliable recognition of broadband transient signals. In this work, we employ a Rydberg atomic sensor to directly capture time-domain responses of partial discharge emissions and construct distinctive spectral fingerprints for different types. A 1D ResNet deep learning model is then applied to recognize these fingerprints from time-domain signals without manual feature engineering. Under increased source-
antenna distances, where spectral features are significantly attenuated, the model attains a recognition accuracy of approximately 94% across four partial discharge categories, demonstrating robustness to attenuation and noise. We further validate the approach in a simulated early-warning scenario, where partial discharge signals mixed with noise are analyzed and the model successfully generates predictive alarms. These results underscore the potential of integrating Rydberg-based broadband sensing with data-driven analysis for non-invasive, high-sensitivity diagnostics of electrical insulation systems.
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All authors Yi-Ming Yin, Qi-Feng Wang, Yu Ma, Tian-Yu Han, Jia-Dou Nan, Zheng-Yuan Zhang, Han-Chao Chen, Xin Liu, Shi-Yao Shao, Jun Zhang, Qing Li, Ya-Jun Wang, Dong-Yang Zhu, Qiao-Qiao Fang, Chao Yu, Bang Liu, Li-Hua Zhang, Dong-Sheng Ding, Bao-Sen Shi
Optics
Pancharatnam Berry Phase as the Origin of Vector Nature Observed in Hermite Gaussian Superposition States
A. Srinivasa Rao
Superposition of orthogonal Hermite Gaussian (HG) modes in orthogonal linear polarization states is one of the techniques used for the experimental realization of lower order optical vector beams and vector
vortex lattices. To date, it has been widely believed that the vector nature arising from this technique originates from the spatial intensity distribution of the superposed HG modes. Here, we report that the vector characteristics observed during the characterization of vector modes generated via HG mode superposition arise from the azimuthally inhomogeneous intensity distribution and the polarization-dependent Pancharatnam Berry (PB) phase. Our analytical calculations confirm that the vector nature is not inherently present in the superposition state; rather, it becomes observable due to polarization-based optical elements used in the characterization process. This insight provides a fundamental clarification of the physical origin of the experimentally observed vector nature in HG-mode superposition.
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Atomic Physics
Polarization Effects in Laser-Assisted (e,2e) Collision on H-atom by Twisted Electrons
Neha and Rakesh Choubisa
The dynamics of fast (e, 2e) collisions, induced by the impact of twisted electron beams, on atomic hydrogen, is analyzed in the presence of a
laser field with circular and linear polarization. For the (e,2e) differential cross-section calculations we use Volkov and Coulomb-Volkov wave functions for scattered and ejected electrons, respectively, while the
laser-atom interaction is treated in first-order perturbation theory. The formalism is developed for the asymmetric coplanar geometry in the first Born approximation. We investigate the influence of
laser field polarization and provide a comparative analysis of Triple Differential Cross-Sections (TDCS) for circularly and linearly polarized
laser fields as a function of ejected electron angle. The overall magnitude of the cross-section is larger for circular polarization as compared to linear polarization. Some notable changes in the angular distributions of TDCS were also observed for the circular polarization as compared to that for linear polarization. We further extend the study to coherent superpositions of twisted electron beams to examine OAM effects for macroscopic target which shows that the TDCS$_{av}$ is strongly sensitive to the difference of the projectiles OAM as well as on the phase difference between them.
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Optics
Size Scaling Law for Radiation Losses of Modes in Photonic Crystal Surface Emitting Devices
Qingyuan Zhang, Ming Lü, Jiang Hu et al. (+4 more)
Photonic-
crystal surface-emitting
lasers (PCSELs) have garnered significant
attention due to their ability to generate
laser beams with ultra high power and low divergence. This is because they support high power single mode lasing with volumes orders of magnitude larger than those of conventional
semiconductor lasers. The finite lateral size in a PCSEL is a primary factor limiting its lasing mode volume and consequently, its output power. We demonstrate that the scaling relation between the total cavity loss $\alpha=\alpha_\perp + \alpha_\parallel$ and the device size $L$ is such that the surface radiation loss scales as $\alpha_{\perp} \sim O(L^{-2})$, while the edge radiation loss $\alpha_{\parallel} \sim O(L^{-3})$. Both scaling relations can be explained by the second order expansions of the complex frequency $\omega(k)$ of the band diagram. Our results constitute an explicit guideline for PCSEL designs to optimize various optical properties.
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All authors Qingyuan Zhang, Ming Lü, Jiang Hu, Qi Dai, Rui Zhu, Xiaojun Xu, and Chaofan Zhang
Geophysics
Disentangling regional impacts of joint teleconnections using causal representation learning
Fiona R. Spuler, Marlene Kretschmer, Magdalena Alonso Balmaseda et al. (+2 more)
Understanding teleconnections of large-scale modes of
climate variability is relevant for seasonal predictability and support a dynamical understanding of climatic changes. While numerical model experiments are the most common approach for investigating counterfactual
climate responses, their conclusions are subject to model biases. Data-driven approaches offer a complementary perspective. Deep learning can extract reduced-dimensional patterns but usually lacks causal interpretability, while causal methods can disentangle signals in the presence of confounding yet are typically based on simple indices. Treating dimensionality reduction and causal inference separately thereby risks losing the teleconnection signal of interest. This paper introduces DAG-VAE, a causal representation learning approach that embeds a physics-informed directed acyclic graph in the latent space of a variational autoencoder. Combining deep learning with causal inference, the method jointly learns nonlinear reduced representations of large-scale modes of variability and their causal interactions. We apply DAG-VAE to disentangle the influences of the Pacific and Indian Oceans on the short rains over the Greater Horn of Africa. Trained on seasonal hindcasts, the method identifies dynamically meaningful representations and recovers spatial response patterns consistent with SST-replacement experiments. Trained on reanalysis data, DAG-VAE identifies a different response pattern to direct influence of the tropical Pacific, highlighting potential model biases and the value of DAG-VAE as a complementary, data-driven approach for estimating spatial causal response patterns from observations. Finally, we demonstrate the ability of the method to generate data-driven counterfactuals of extreme short rain seasons, with potential applications for forecast-based early action and scenario planning.
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All authors Fiona R. Spuler, Marlene Kretschmer, Magdalena Alonso Balmaseda, Masilin Gudoshava, Theodore G. Shepherd
Computational Physics
Floating-point consistent cross-verification methodology for reproducible and interoperable DDA solvers with fair benchmarking
Clément Argentin, Patrick C. Chaumet, Michel Gross et al. (+1 more)
The discrete dipole approximation (DDA) is a widely used and versatile numerical method for solving electromagnetic scattering by arbitrarily shaped objects. Despite its popularity, quantitative comparisons between independent implementations remain challenging due to differences in linear-system conventions, solver settings, and default numerical parameters. In this work, we introduce a unified software-assisted methodology for cross-verification and benchmarking of three major open-source DDA solvers: DDSCAT, ADDA, and IFDDA. We demonstrate how machine-precision agreement can be achieved across implementations by aligning all free parameters and provide practical equivalence tables enabling reproducible and interoperable simulations. Using this methodology, we perform systematic CPU and GPU performance comparisons covering OpenMP, MPI, and CUDA/OpenCL parallelization. Beyond benchmarking, our approach serves as a practical guide for configuring consistent DDA simulations and for understanding how precision, solver choice, and hardware architecture affect runtime, scalability, and accuracy in computational light-scattering studies. The software package also supports regression testing and bitwise reproducibility verification for future code releases.
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All authors Clément Argentin, Patrick C. Chaumet, Michel Gross, Maxim A. Yurkin
Applied Physics
Bare and stretched string tyre models with distributed FrBD dynamics
Luigi Romano
This paper presents a novel class of string tyre models with FrBD friction dynamics. By modelling the distributed carcass and tread deformations with string-like equations, the resulting formulation leads to a system of semilinear
parabolic partial differential equations (PDEs) that describe the
evolution of the tyre states without explicitly distinguishing between stick and slip regimes. Rigorous stability and passivity analyses are also conducted using a Lyapunov-based approach, establishing boundedness of the distributed states and energy dissipation during rolling contact. The proposed Lyapunov function admits a clear physical interpretation as the total elastic energy stored in the tyre, enabling a direct link between mechanical energy storage and frictional dissipation due to slip losses. The steady-state and transient behaviours of the model are investigated both numerically and experimentally, revealing that the new formulation can satisfactorily reproduce nonlinear relaxation phenomena excited by step slip inputs. The resulting model provides a physically interpretable, mathematically well-posed, and computationally efficient basis for advanced vehicle dynamics simulations and control-oriented applications.
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