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Physics Physics

physics Watch Physics

Thursday 4 June

Fluid Dynamics · Cross-listed from cs.LG

Multiscale POD of Transformer Attention Fields: Scale-Selective Analysis via Morlet Scalogram

Athanasios Zeris

We introduce scale-selective Proper Orthogonal Decomposition (POD) for transformer attention fields, inspired by the use of POD for extracting energetically dominant modes from turbulent flow ensembles. The Morlet continuous wavelet transform identifies dominant temporal scales in the attention lag structure across a document ensemble; POD then extracts the energetically dominant modes at each scale from the ensemble of attention fields. The resulting modes reveal layer-dependent scale organisation, with early layers emphasising fine scales and later layers shifting toward coarser scales. We define a spectral concentration index from the POD eigenvalue decay rate and show empirically that it differentiates layers by their attention field complexity. By the classical POD optimality theorem, the extracted modes minimise the average L2 reconstruction error over the ensemble (Theorem 1), giving a data-driven effective rank for each layer. The method requires no architectural modification and no linguistic annotations: dominant attention patterns emerge from ensemble statistics alone. The turbulence analogy is structural rather than physical: we borrow ensemble covariance and modal analysis, not fluid dynamics itself.
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Instrumentation and Detectors

Hyperon-Nucleon Spectrometer

Xiaozhi Bai, Xu Cao, Zhe Cao et al. (+237 more)

Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $\Lambda$ polarization puzzle, in which $\Lambda$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse polarization that remains theoretically unexplained. This whitepaper presents the proposal for the Hyperon-Nucleon Spectrometer (H-NS) at the High-Intensity heavy-ion Accelerator Facility (HIAF). Leveraging the high energy and high intensity of HIAF's proton and heavy-ion beams, the H-NS experiment will perform systematic studies of hyperon polarization phenomena and their underlying mechanisms in proton-proton ($pp$), proton-nucleus ($pA$), and nucleus-nucleus ($AA$) collisions in the fixed target mode. A wide-range beam energy scan, including proton beams from 3 GeV up to 9.3 GeV (HIAF) and up to 32 GeV (upgraded HIAF), will be conducted to examine the dependence of polarization on collision energy. The spectrometer is designed with specialized detectors capable of high-precision reconstruction of final-state baryon polarizations. Among its many interesting and important measurements, H-NS will simultaneously measure hyperon and proton spin observables to explore the polarization mechanism in hadronic interactions and the spin structure of baryons. Furthermore, the use of $pA$ and $AA$ collisions will enable detailed investigations of cold and hot nuclear matter effects on spin polarization. Its physics program and detector development will significantly benefit the future Electron-ion Collider in China.
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All authors Xiaozhi Bai, Xu Cao, Zhe Cao, Jinhui Chen, Kai Chen, Qibo Chen, Shi Chen, Xin Chen, Yuquan Chen, Zhenyu Chen, Jianping Dai, Heng-Tong Ding, Dongshuo Du, Shuxian Du, Limin Duan, Zhe Duan, Anhui Feng, Jie Feng, Yicheng Feng, Jinlin Fu, Xiaofeng Fu, Chaosong Gao, Liang Ge, Wenwen Ge, Lisheng Geng, Boxing Gou, An Gu, Yinghui Guan, Yutian Guan, Aiqiang Guo, Fengkun Guo, Lu Guo, Hao Han, Weijia Han, Yunxiang Hao, Wanbing He, Xionghong He, Zhixuan He, Defu Hou, Tingting Hou, Jinniu Hu, Shouyang Hu, Zhen Hu, Fei Huang, Kaixuan Huang, Linqin Huang, Mei Huang, Xuguang Huang, Yuanjing Ji, Xincai Kang, Jie Kong, Cheng Li, Demin Li, Haibo Li, Jibo Li, Lixuan Li, Min Li, Peilian Li, Peiyu Li, Ronghua Li, Suxian Li, Weilong Li, Wuyuan Li, Xin Li, Xiaomei Li, Xiaqing Li, Yang Li, Yangu Li, Yutie Liang, Zheng Liang, Zuotang Liang, Chuangxin Lin, Dexu Lin, Shoulong Lin, Ting Lin, Bo Liu, Bo-Chao Liu, Feng Liu, Hang Liu, Hongna Liu, Hui Liu, Kai Liu, Liuming Liu, Qian Liu, Tianbo Liu, Tong Liu, Xiang Liu, Yanwen Liu, Pengzhong Lu, Weijian Lu, Xiaofeng Luo, Xiao-Rui Lyu, Bo-Qiang Ma, Jianping Ma, Kuo Ma, Weihu Ma, Yugang Ma, Lijun Mao, Ruishi Mao, Yu Meng, Norihito Muramatsu, Maowu Nie, Xiaoyang Niu, Hua Pei, Ronggang Ping, Shi Pu, Tianlei Pu, Cheng Qian, Wenbin Qian, Yi Qian, Guangyou Qin, Jiajun Qin, Cong-Feng Qiao, Zan Ren, Dingyu Shao, Qianshun She, Diyu Shen, Guodong Shen, Wenhan Shen, Xiaomin Shen, Lina Sheng, Shusu Shi, Jinxing Song, Qintao Song, Yuan Song, Zihe Su, Baohua Sun, Hao Sun, Kai-Jia Sun, Liangting Sun, Peng Sun, Pengfei Sun, Xiangming Sun, Xu Sun, Yelei Sun, Zhipeng Sun, Zhiyu Sun, Shuwen Tang, Zebo Tang, Jing Tian, Ye Tian, Yu Tian, Yapeng Wan, Boqun Wang, Changxin Wang, En Wang, Enke Wang, Haozhen Wang, Hulin Wang, Jiansong Wang, Ling Wang, Qun Wang, Tianao Wang, Xiangang Wang, Xiang-Peng Wang, Xiaoyu Wang, Xinyu Wang, Xiongfei Wang, Xiuhua Wang, Yaping Wang, Shuyi Wei, Xianglun Wei, Xiangjie Wen, Fengjun Wu, Jia-jun Wu, Xin Wu, Bowen Xiao, Le Xiao, Zhigang Xiao, Guannan Xie, Yaping Xie, Hongxi Xing, Weizhi Xiong, Ji Xu, Lailin Xu, Nu Xu, Qinghua Xu, Xiaowei Xu, Junwei Yan, Wenbiao Yan, Wencheng Yan, Xiaoyu Yan, Bo Yang, Chi Yang, Haibo Yang, Herun Yang, Jiancheng Yang, Qian Yang, Shuai Yang, Tongjun Yang, Yadong Yang, Yuansheng Yang, Yuna Yang, Zaochen Ye, Zhihong Ye, Li Yi, Hang Yin, Junhao Yin, Yi Yin, Kejie You, Zhengyun You, Chunxu Yu, Yuhong Yu, Zhaoyang Yuan, Wangmei Zha, Honglin Zhang, Jianyu Zhang, Jin Zhang, Jinlong Zhang, Jinqun Zhang, Shisheng Zhang, Weibin Zhang, Xiang Zhang, Xueheng Zhang, Yapeng Zhang, Yateng Zhang, Yifei Zhang, Yumei Zhang, Yuqiao Zhang, Zhe Zhang, He Zhao, Hongyun Zhao, Lei Zhao, Yuxiang Zhao, Zhengguo Zhao, Yajun Zheng, Yangheng Zheng, Zhiyang Zheng, Daicui Zhou, Jian Zhou, Jiangpeng Zhou, Jing Zhou, Kai Zhou, Xianrong Zhou, Xiaorong Zhou, Yiyu Zhou, Sitao Zhu, Xiao Zhuang, Xinyu Zong, Bingsong Zou

Optics · Cross-listed from cond-mat.mes-hall

Coherent room-temperature dipole synchronization in nanocavity sheets

Rakesh Arul, Piper Fowler-Wright, Lille Borresen et al. (+3 more)

Plasmonic nanocavities enable the synchronization of spatially distant emissive dipoles through strong near-field coupling in sub-nm gaps. We report formation of a room-temperature synchronized dipole state in locally-ordered plasmonic nanogap 2D arrays under non-resonant continuous-wave pumping. Unlike lasers, photonic Bose-Einstein condensates, or exciton-polariton condensates, this system exhibits spatial coherence across the dipoles, while rapid radiative and non-radiative emission suppresses temporal photon coherence. A change of behaviour is observed with increasing pumping, marked by the spatial spread of g(1) coherence, but without spectral narrowing or directional emission. This driven-dissipative system exhibits fast temporal coherence decay and complex spatial correlations, offering a new platform for studying synchronization at room temperature. Combining ultralow mode volumes, high Purcell enhancement, and scalable ambient operation, it opens pathways for novel photonic and quantum technologies.
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All authors Rakesh Arul, Piper Fowler-Wright, Lille Borresen, Brendon W. Lovett, Jonathan Keeling, Jeremy J. Baumberg

Physics Education

Resource Letter QIE-1: Research in quantum information education

Josephine C. Meyer, Simon Goorney, Tunde Kushimo et al. (+1 more)

In celebration of the 2025 UN International Year of Quantum Science and Technology, this Resource Letter surveys the rapidly-growing field of scholarship in quantum information science and engineering (QISE) education. It is primarily written as a guide for educators wishing to get started teaching QISE using research-based teaching methods, as well as for discipline-based education research (DBER) practitioners looking to get started in this field. Topics covered include scoping the field of QISE education, research into student reasoning in QISE, research-based and research-inspired curricular materials from the high school to graduate level, research-based assessments, simulation and gamification tools, and tools for incorporating discussion of the societal and ethical implications of quantum technologies into the classroom.
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All authors Josephine C. Meyer, Simon Goorney, Tunde Kushimo, Zeki C. Seskir

Atomic Physics · Cross-listed from astro-ph.SR

Depolarization and Polarization-Transfer Rates for Solar He I Lines due to Collisions with Neutral Hydrogen

Moncef Derouich and Saleh Qutub

Context. Neutral helium (He I) produces several spectral lines that are widely used for solar diagnostics. The role of collisions between He I atoms and neutral hydrogen (H I) in the modeling of solar He I lines remains insufficiently quantified. Accurate determination of collisional rates affecting atomic polarization is needed for solar spectropolarimetry. Aims. Our aim is to provide a set of multi-level and multi-term collisional depolarization, polarization-transfer, and population-transfer rates, due to isotropic collisions with neutral hydrogen, for He I levels and terms involved in the main solar He I diagnostic lines. Methods. The calculations are performed within the frozen-core approximation, in which the inner 1s electron is treated as a core with Lc = 0, Sc = 1/2, and Jc = 1/2, while the outer electron is treated as the active valence electron. Results. We compute both multi-level rates, describing depolarization and polarization transfer between fine-structure J-levels, and multi-term rates, which additionally account for coherences between different J-levels belonging to the same term. Conclusions. Our results provide the collisional input needed for the statistical equilibrium equations (SEE) of the polarization of the main He I solar lines, including the 10830 Å, D3 5876 Å, and related triplet transitions, and allow a quantitative reassessment of the role of neutral-hydrogen collisions in He I spectropolarimetry.
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Atmospheric and Oceanic Physics

MODIS Thermal Infrared Sounding (MOTIS): Estimating Tropical Cyclone Central Pressure from Warm-Core Anomalies

Jinghuai Yao, Chi Yan Kwok, Puyuan Du et al. (+2 more)

This study presents a novel framework for estimating the central sea-level pressure ($P_\mathrm{c}$) of tropical cyclones (TCs) using infrared radiometers. We leverage the long-overlooked combination of high spatial resolution and sounding capability of the Moderate Resolution Imaging Spectroradiometer (MODIS) to measure warm-core anomalies in TC eyes. We develop the MODIS Thermal Infrared Sounding (MOTIS) framework, which performs instrument-specific preprocessing and estimates $P_\mathrm{c}$ using multiple linear regression. MOTIS yields $r^2 = 0.945$ and RMSE = 4.3 hPa for high-intensity TCs with observed clear eyes (mean $P_\mathrm{c} = 937$ hPa), outperforming all existing methods for intense TCs. We construct a dataset of 3288 (1082 clear-eye) MOTIS estimates from 2002 to 2025 and demonstrate its potential to improve the quality of Best Track $P_\mathrm{c}$, roughly halving uncertainties in the absence of pressure observations. Although MODIS is nearing the end of its mission, the MOTIS framework could be extended to next-generation geostationary sounders to provide accurate real-time $P_\mathrm{c}$ estimation for high-intensity TCs.
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All authors Jinghuai Yao, Chi Yan Kwok, Puyuan Du, Yubo Wang, Derrick Herndon

Fluid Dynamics

Statistical orientation and distribution of columnar ice crystals in turbulent flows

A. Pumir, M. Z. Sheikh, K. Gustavsson et al. (+3 more)

We study the motion of columnar ice crystals that form in clouds over a range of low temperature. Our focus here is on elongated ice crystals, which are smaller than the size of the smallest eddies in the flow, with a moderate aspect ratio comprised between $3$ and $5$. We determine turbulent solutions of the Navier-Stokes equations over a range of turbulent kinetic energy dissipation characteristic of clouds ($4.41\;{\rm cm}^2/{\rm s}^3 \le \varepsilon \le 1120\;{\rm cm}^2/{\rm s}^3$) by using direct numerical simulations, and we follow the motion of crystals using simplified but realistic models for the motion of non-spherical, elongated particles. The influence of the fluid inertia leads to a preferential alignment of the crystals perpendicular to the direction of gravity, the alignment effect being opposed by the turbulent fluctuations. Along with the strong alignment of the crystal axis perpendicular to gravity, we observe only a weak alignment with the vorticity, much weaker than in the absence of gravity. The settling velocity depends only weakly on the orientation of the crystals, but is strongly enhanced when $\varepsilon$ increases, an effect that we attribute to preferential concentration in the flow. As the inertia of the columnar ice crystals considered here is significant, we observe a strong spatial clustering. Finally, we discuss the relevance of the effects identified here on the collision frequency between ice crystals in cloud conditions.
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All authors A. Pumir, M. Z. Sheikh, K. Gustavsson, E. Lévêque, B. Mehlig, A. Naso

Plasma Physics · Cross-listed from astro-ph.SR

Minor Ions as a Diagnostic of Solar Wind Heating: Inverted Mass-to-Charge Scaling in Imbalanced Turbulence

Michael F. Zhang, Evan L. Yerger, Matthew W. Kunz et al. (+1 more)

Alfvénic turbulence is vital to powering the solar wind and corona, yet eludes a comprehensive understanding of the kinetic processes by which it dissipates. Minor ions are sensitive tracers of these processes, showing extreme perpendicular temperatures and mass-weighted temperature trends that can either correlate or anticorrelate with mass-to-charge ratio, $A_i/Z_i$. We use a combination of quasilinear theory and 3D hybrid-kinetic simulations to explain these features and their correlations with properties of turbulence in the fast solar wind. When Alfvénic turbulence is imbalanced, its cascade to ion-Larmor scales is throttled by the helicity barrier. This barrier ultimately leads to high-frequency proton-cyclotron waves (PCWs), both oblique and parallel, the latter of which produce very flat electric-energy spectra ($\mathcal{E}_{E_{\perp}}\sim k_\parallel^{-\eta}$ with $\eta<2$) over the range of scales that are cyclotron resonant with minor ions. While steeper spectra lead to a positive correlation of heating with $A_i/Z_i$, the shallower spectra cause the dependence to invert, with $Q_i\propto Q_{\mathrm{p}}A_i(A_i/Z_i)^{\eta-2}$. Six simulations of balanced and imbalanced turbulence spanning $\beta_{\rm p0}=\{1,0.3,1/16\}$ corroborate this prediction, showing minor-ion heating rates that follow $(A_i/Z_i)^a$. Minor-ion heating is strongest and most perpendicular in our lowest $\beta_{\rm p0}=1/16$ simulation of imbalanced turbulence, reaching $T_{\perp{\rm O}^{5+}}/T_{\perp{\rm p}}\approx40$ and $T_{\perp{\rm O}^{5+}}/T_{\parallel{\rm O}^{5+}}\approx10$, consistent with low-coronal observations. Future minor-ion measurements should test whether intervals in which minor-ion thermal speeds decrease with increasing mass-to-charge ratio are associated with a history of large cross helicity, enhanced power in parallel PCWs, and a steep transition-range spectrum.
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All authors Michael F. Zhang, Evan L. Yerger, Matthew W. Kunz, Jonathan Squire

Fluid Dynamics

An experimental study on the heat transport in porous media convection

Jing Dong, Lu Zhang and Ke-Qing Xia

We investigate the heat transport in porous media convection over a wide Rayleigh–Darcy number range of $26.8\leq Ra\leq 2.62\times 10^5$, and a Darcy number range of $6.18\times10^{-7}\leq Da\leq 1.21\times 10^{-5}$. In the experiments, we employ 3D-printed lattice structures as the solid porous matrix and water as the working fluid. Quantitative analyses of the porous medium Nusselt number $Nu_m$ and local temperature statistics reveal that the present system undergoes a transition through five distinct regimes: I. Conduction, II. Convection, III. Oscillation, IV. Transition, V. Classical Rayleigh–Bénard convection. This transitional process bridges the gap between Rayleigh–Darcy-like behaviour and Rayleigh–Bénard-like behaviour in porous media convection. By varying the permeability of the matrix, we further examine the role of the Darcy number $Da$, which turns out to have a profound impact on the transitional processes across different regimes. Flow field measurements reveal that the flow structures within Regime IV and Regime V evolve from several horizontally stacked convection rolls to a single-roll structure, and the pore-scale Reynolds number both exceeds unity in these two regimes. Finally, we report the corresponding phase diagram in the $Ra$-$Da$ space.
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Fluid Dynamics

Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks

Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza et al. (+1 more)

Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult to infer accurately due to the need for precise computation of near-wall velocity gradients. Passive scalar fields, such as concentration or temperature, are advected by the same underlying velocity field and have the potential to uncover hidden flow physics metrics such as WSS. In this work, we demonstrate such reconstruction from spatially limited passive scalar observations using two fundamentally different inverse frameworks: a differentiable physics framework based on discrete adjoint, PDE-constrained optimization, which enforces the governing equations as hard constraints, and physics-informed neural networks (PINNs), which treat them as soft constraints. Benchmark problems include a 2D canonical backward-facing step (2D-BFS) and a 3D patient-specific stenotic coronary artery. For the 2D-BFS case, evaluated under three measurement scenarios (near-wall, far-field, and combined), PINN achieves high accuracy when near-wall data are available but fails when restricted to far-field measurements, whereas the differentiable physics approach recovers accurate WSS across all scenarios. In the 3D patient-specific case, the differentiable physics framework outperforms PINNs, yielding accurate WSS reconstruction. These results establish that measurement location and inverse formulation jointly determine reconstruction fidelity in scalar-based near-wall flow inference. The proposed framework opens a path toward estimation of near-wall hemodynamics from scalar transport data, with broader applicability to fluid flow problems where passive scalars can be observed.
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All authors Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza, Amirhossein Arzani

Biological Physics · Cross-listed from cs.LG

Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events

Rishal Aggarwal, David Ryan Koes, Nicholas M. Boffi et al. (+1 more)

Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data remains nontrivial. We introduce Flux Matching, a framework that learns two complementary objects directly from reactive trajectory data: a current velocity $u(z)$, whose streamlines trace the dominant reaction pathways, and a scalar potential $h(z)$, obtained from a weighted Helmholtz-Hodge decomposition of the reactive current, that serves as a data-driven reaction coordinate. Both minimize quadratic functionals over the reactive path ensemble, analogous to the flow matching loss in generative modeling, and require no knowledge of the underlying dynamics or stationary distribution. Unlike committor-based methods, $u$ and $h$ remain well-defined under projection onto non-Markovian collective variables, and their level sets in turn provide adaptive interfaces for improved sampling with enhanced sampling methods. Flux Matching is validated through the generation of current velocity trajectories and rate constant calculations on molecular systems.
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All authors Rishal Aggarwal, David Ryan Koes, Nicholas M. Boffi, Eric Vanden-Eijnden

Fluid Dynamics

Turbulence-based parametrization of animal flight

Ariane Gayout, Eize J. Stamhuis and Casper J. van der Kooi

Animals capable of powered flight range in wingspan from a few hundred microns to a few meters. The inertial turbulence to which these animals are exposed features vortices ranging from a few hundred micrometers to hundreds of kilometers in size. Yet, the impact of ambient turbulence on animal flight is virtually uncharted and most studies on animal flight are conducted in still air or under laminar conditions. Here, we propose a novel parameterization that links animal flight with turbulence, through a proxy for the energy injected into the atmosphere, $E_{sp}=b^3 f^2$, with $f$ the animal's flapping frequency and $b$ the wingspan. We model this parameter using a scaling relation in the shape of a power law $E_{sp} \propto k^\alpha$, with $k=1/b$ the wavenumber corresponding to the animal inverse wingspan. Literature provides four theoretical predictions on the exponent $\alpha$: two connected to aerodynamic and energetic aspects of flight, $\alpha_{aero}=-2$ and $\alpha_{power}=-5/3$, and two linked to physiological limits. Drawing from experimental data of over 400 species spanning 13 insect orders and two vertebrate classes, we recover $\alpha_{power}=-5/3$ as the best scaling relation across the animal kingdom. Grouping per animal clade however reveals a secondary power law with $\alpha=-5/2$ exponent for invertebrate orders, with a family-dependent coefficient. This new scaling relation suggests a yet-unknown universal physical mechanism in insect flight, likely depending on wing morphology and mechanical properties.
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Physics and Society

Assessing Power System Vulnerability to Climate-Related Stressors and Shocks: The Case of Indonesia

Hariadi Aji, Nihit Goyal, Stefan Pfenninger-Lee et al. (+1 more)

Climate change and extreme weather are increasing the vulnerability of power systems globally, particularly in emerging economies such as Indonesia. Yet, existing studies often assess these impacts in isolation, focusing on individual components or specific hazards, leaving system-level implications under-examined. To address this gap, we develop an integrated, spatially explicit approach to assess an energy system's climate-related vulnerabilities and their impacts, and apply the approach to Indonesia. We quantify climate-based vulnerabilities in generation and transmission infrastructure as well as in demand by distinguishing stressors (temperature rise) and shocks (disruptive hazards due to sea-level rise, flooding, and landslides). Through geospatial data analysis, derating models, and regression analysis, we examine existing and planned assets and demand under historical and future climate conditions. Results indicate that both existing and planned generation are likely to experience stress, which implies a reduction in usable capacity, even as electricity demand increases due to temperature rise, and transmission assets face potential disruption from climate change-induced shocks. Together, these effects erode reserve margins by up to 36 percentage points under the 10-year plan, indicating a substantial reduction in system resilience. The largest system, Jawa-Madura-Bali, experiences a 20.8 percentage point decline, leaving a remaining margin of 26.5%, below the 10-year planning threshold. Importantly, the findings suggest that a growing share of future capacity expansion may be absorbed by climate-induced losses, implying that adaptation-related investments may increasingly be required simply to maintain existing supply levels rather than meet future requirements. We conclude that there is an urgent need to embed climate considerations more explicitly into power sector planning.
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All authors Hariadi Aji, Nihit Goyal, Stefan Pfenninger-Lee, Igor Nikolic

Geophysics · Cross-listed from cond-mat.soft

Investigating frictional instability due to pressurization in granular media: insights from coupled computational fluid dynamics discrete element method

Bimal Chhushyabaga and Behrooz Ferdowsi

Fluid pressurization can reactivate subcritically stressed granular layers in faults, slopes, and injection-perturbed reservoirs, but grain-scale feedbacks among pressure diffusion, drainage, and contact-network degradation remain unresolved. Here, 3D coupled CFD-DEM simulations investigate pore-pressure-induced reactivation of confined, fluid-saturated granular shear layers under imposed shear stress. Strain-controlled tests define the Mohr-Coulomb strength envelope; stress-controlled simulations then impose subcritical shear stresses while basal pore pressure increases under drained and undrained conditions. Instability is governed not by pore pressure alone, but by its coupled evolution with effective stress, drainage, dilation or compaction, hydraulic connectivity, and granular fabric. Undrained boundaries retain excess pore pressure, whereas drained boundaries maintain vertical gradients and suppress excess pressure. Internal fields reveal alternating dilation and compaction bands and reorganization of a porosity-derived permeability proxy, showing that hydraulic pathways evolve during deformation. Micromechanical diagnostics identify localized particle rotation, force-chain reorganization, porosity redistribution, and coordination-number variations controlled mainly by imposed shear-stress level rather than drainage. Second-order fabric metrics show that post-failure weakening coincides with loss of directional force-chain organization, especially at lower shear. Friction-velocity and friction-porosity trajectories indicate a transition from dilatancy-dominated strengthening to pore-pressure-driven weakening. Viscous-number scaling partially organizes the low-Iv creeping response, 10^-8 <= Iv <= 10^-5, but not onto a unique local rheology. These results clarify how drainage-controlled hydromechanical feedbacks and fabric degradation convert pore-pressure forcing into instability.
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Fluid Dynamics

A high-order Fourier Continuation (FC)-based spectral incompressible Smoothed Particle Hydrodynamics (ISPH) scheme for general boundary conditions in wall-bounded domains

Meixuan Lin, Georgios Fourtakas and Benedict D.Rogers

In this paper, a high-order Fourier Continuation (FC) algorithm is introduced into the spectral smoothed particle hydrodynamics (SPH) scheme to simulate the wall-bounded incompressible flows. This work aims to extend the spectral ISPH scheme towards the high-order simulation of flows with non-periodic wall boundary conditions. Herein, a polynomial-based Fourier continuation technique is applied to the velocity and pressure to make the domain both periodic and Cp smooth. The spatial SPH discretisation is performed subsequently in the frequency space on the FC-extended domain by building upon the convolution theorem using fast Fourier transform (FFT). The incorporation of Neumann boundary conditions is straightforward, and more generally, the FC method enforces periodicity across the domain regardless of the boundary condition type. The convergence order, additional computational cost, and implementation technique of the FC method are also discussed. Combined with a projection-based time integration scheme and a spectral PPE solver, the FC-based spectral ISPH framework is validated against several classical CFD benchmarks. The principal finding of this work is that the incorporation of FC techniques enables the spectral ISPH scheme to simulate wall-bounded flows with high-order convergence, and accurately capturing complex vortex dynamics. This work therefore represents a step towards a fully high-order spectral Lagrangian SPH solver with complex geometries
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Plasma Physics

Plasma wakefield dynamics of self-generated electron bunch trains

Salome Benracassa, Sheroy Tata, Yinren shou et al. (+2 more)

Laser plasma accelerators can deliver high-energy, quasi-monoenergetic electron beams over centimeter-scale distances. In this work, we report on the generation of narrow, quasi-monoenergetic electron bunch trains with periodic energy spacing issued from downramp injection in a laser driven wakefield accelerator. The periodicity in energy is shaped via relativistic lengthening of the wakefield during the acceleration phase, while the spatial periodicity is obtained via injection into multiple plasma periods. At the end of the accelerator, a rotation in phase-space is performed to compress each bunch in energy, producing narrow periodic spikes in the spectrum. The experimental observations are supported by particle-in-cell simulations, which reproduce the formation and evolution of the periodic bunch trains, providing an insight into the underlying plasma dynamics.
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All authors Salome Benracassa, Sheroy Tata, Yinren shou, Aaron Liberman, Victor Malka

Fluid Dynamics

Reward hacking in physical reinforcement learning revealed by turbulent drag reduction

Giorgio Maria Cavallazzi, Miguel Pérez-Cuadrado and Alfredo Pinelli

Reinforcement-learning controllers optimise specified rewards, but in physical systems those rewards often capture only part of the true control objective. Three mechanisms through which this mismatch can produce apparent success without physical improvement are identified: incomplete accounting that omits relevant costs, constraint enforcement outside the policy that corrupts credit assignment, and observations that fail to resolve the relevant dynamics. All three are demonstrated in active drag reduction of wall-bounded turbulence, where the conservation constraint and full energy budget can be measured directly. A memoryless learnt policy reports drag reduction while raising total dissipation, collapsing to non-physical flow configurations. A recurrent multi-agent controller with the zero-mean projection embedded in the actor, temporal memory matched to the relevant timescales, and an actuation cost that bounds the wall power delivers a physically consistent control. Progress in physical reinforcement learning requires the reward, constraints, observations and evaluation metrics to represent unequivocally the physical objective.
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Fluid Dynamics

Flapping instability of elastic disks in Stokes flows

Yijiang Yu, Hugo Perrin, Michael D. Graham et al. (+1 more)

Fluid-structure interactions at low Reynolds number can lead to a much richer phenomenology than previously expected. Here, we study the dynamics of a freely suspended, thin elastic disk in a shear flow, where the plane of the disk is initially parallel to the flow plane. Using a combination of experiments and simulations, we demonstrate that beyond a critical flow strength the disk deforms, performing flapping dynamics, in which the disk curves up and down periodically relative to the horizontal shear plane. The bifurcation diagram obtained by simulation reveals several oscillatory solutions, including a wiggling motion that is predicted by a linear stability analysis. The flapping dynamics is shown to be a subcritical instability whose key ingredient is the finite extensibility of the disk. The behavior we observe has implications for emerging investigations on the flow dynamics of sheet-like particles, such as 2D polymers and 2D crystalline materials immersed in viscous fluids.
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All authors Yijiang Yu, Hugo Perrin, Michael D. Graham, Lorenzo Botto

Plasma Physics

A Surrogate Model for Proton Spectrum Prediction to Map Transitions in Laser-Ion Acceleration

Chengqi-Zhang, Yang He, Mamat Ali Bake et al. (+2 more)

We present a physics-guided, decoupled dual-branch surrogate model to predict continuous proton energy spectra from laser-driven ion acceleration. Integrating a $\beta$-VAE for spectral feature extraction with a parallel multi-layer perceptron for scalar boundary enforcement, the framework achieves a predictive accuracy of $R^2 = 0.94$ for the maximum cutoff energy and $R^2 = 0.94$ for the total particle flux, with a median per-sample spectral $R^2 = 0.985$ (in $\log_{10}$ space) across the full 2000-bin energy distribution. The model incorporates uncertainty quantification via deep ensembles, serving as a quantitative probabilistic diagnostic tool with calibration errors below 6.2%. Within the 1D longitudinal framework, the surrogate reproduces spectral signatures consistent with the transition from Target Normal Sheath Acceleration (TNSA) to the volumetric heating dynamics of Relativistically Induced Transparency (RIT) and Breakout Afterburner (BOA) regimes, as validated against kinetic diagnostics from 1D particle-in-cell simulations. This approach establishes a computationally efficient baseline for future multi-fidelity optimization and provides an engine for closed-loop parameter control in high-repetition-rate laser facilities.
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All authors Chengqi-Zhang, Yang He, Mamat Ali Bake, Xilin-Wang, Bai-Song Xie

Plasma Physics

Wave drag in moving plasmas: recent developments and prospects

Renaud Gueroult, Aymeric Braud and Julien Langlois

Wave propagation in a medium differs depending on whether this medium is at rest or moving with respect to an observer. Motion can notably lead to modifications of the wave trajectory, of its polarization, or of its transverse structure. Although these effects are well documented in isotropic dielectrics, they remain largely unexplored and unaccounted for in plasmas, despite the fact that simple models suggest they could in fact be large under certain conditions, as well as recent experimental observations. Here we first review existing models for motion effects on plasma waves, then identify a number of basic challenges that lie in the way of using these models to quantify motion effects in realistic configurations, and finally discuss possible workarounds.
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Fluid Dynamics

Deep reinforcement learning with spatial and temporal awareness for active boundary control of buoyancy-driven convection

Giorgio Maria Cavallazzi, Miguel Pérez Cuadrado and Alfredo Pinelli

Deep reinforcement learning (DRL) applied to thermal convection control consistently produces degenerate actuation: wall-temperature policies whose outputs are saturated, pseudo-random, or spatially incoherent. Two compounding deficiencies are responsible: multilayer-perceptron policies that discard spatial flow structure, and memoryless policies that cannot distinguish self-induced flow changes from background evolution. Together they degrade the actuation into forms whose relation to the convective topology cannot be read off and which are not realisable at the actuator, even when cell coalescence (the merging of convection rolls into fewer, larger structures), which would reduce Nu, is accessible to boundary actuation. The present framework addresses both causes through four targeted design choices: convolutional policy networks, Gated Recurrent Unit (GRU) memory, off-policy training (TD3/MADDPG), and action-smoothness constraints. A systematic 2x2 factorial design isolates the contribution of each component. On Rayleigh-Benard convection at Ra = 10,000, all four configurations achieve cell coalescence and reduce Nu to as low as 1.83 (26% below the uncontrolled baseline) in 350 episodes, without the full-field data augmentation required by prior work. Crucially, coalescence is achieved even by the single-agent configuration, demonstrating that the multi-agent formulation is not a prerequisite once the policy architecture is sufficiently expressive. Applied to double-diffusive convection in the salt-finger regime, the framework spontaneously discovers a travelling-wave actuation whose phase speed adapts to the evolving mixing state of the flow, enhancing heat transfer by 19.1% and reducing salinity variance by 21.0%.
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Computational Physics · Cross-listed from stat.ML

Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks

Cornelius Otchere and Michael Shields

Physics-Informed Neural Networks inherently suffer from task interference because they rely on a shared parameter space to satisfy both governing differential equations and boundary conditions. We analyze this structural conflict using the Fisher Information Matrix to quantify the effective degrees of freedom ($d_{eff}$) in a physics-constrained model. Unlike the classical $d_{eff}$ which measures how many parameter directions are informed by data against a statistical prior, our $d_{eff}$ measures the dimension of the parameter directions unconstrained by the differential operator. For operators with finite-dimensional kernel, we show that $d_{eff}$ converges to the kernel dimension exactly, independent of network width, depth, or activation function, recasting it from a fit diagnostic into a structural invariant of the underlying continuous operator. For operators with infinite-dimensional kernel, $d_{eff}$ instead measures the network's finite-dimensional representational bandwidth for that kernel rather than recovering an integer invariant. Importantly, $d_{eff}$ also serves as an a priori structural diagnostic. Driving $d_{eff}$ of a well-posed problem to zero certifies that the physics and boundary constraints have absorbed the network's free directions. Building on this characterization, we introduce subspace projection strategies for boundary adaptation. Rather than retraining from scratch, we project parameter updates into the null space of the pre-trained physics operator so that new boundary conditions are satisfied without disturbing the learned physics. Gradient-based fine-tuning can match or exceed this but needs more wall-clock time and tuning, whereas subspace projection delivers near-equivalent quality in seconds to minutes. We validate on linear and nonlinear operators, demonstrating accurate adaptation to initial and boundary shifts and unencountered constraint types.
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Computational Physics · Cross-listed from cs.LG

On the training of physics-informed neural operators for solving parametric partial differential equations

Nanxi Chen, Chuanjie Cui, Airong Chen et al. (+2 more)

Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying solely on paired input-output simulation data. By incorporating physical constraints into the training objective, PINOs combine the cross-instance generalization of neural operators with the data efficiency of physics-informed learning. Despite this promise, how to train PINOs efficiently and robustly remains less well-understood than the training of either data-driven neural operators or physics-informed neural networks (PINNs). To bridge this gap, we examine key components of the PINO training pipeline, including architecture design, optimizer choice, loss balancing, and collocation-point sampling strategy. We study three representative operator backbones, Deep Operator Network (DeepONet), Fourier Neural Operator (FNO), and Continuous Vision Transformer (CViT), across five diverse parametric PDE systems. Our results show that CViT provides consistently strong and stable performance across the considered benchmarks. Beyond architecture, we find that several optimization pathologies previously identified in PINN training naturally arise in PINOs, including gradient conflicts and causal violation. We also find that mitigation algorithms developed for PINNs remain effective in the PINO setting. We further compare physics-informed and data-driven training under different data regimes, revealing that a carefully designed physics-informed training pipeline can match, and in some cases, outperform purely data-driven neural operators. Taken together, these findings provide a systematic empirical understanding of the optimization challenges in PINO training and inform a practical pipeline for efficient and robust physics-informed operator learning. Code and data are available at https://github.com/NanxiiChen/PI-CViT.
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All authors Nanxi Chen, Chuanjie Cui, Airong Chen, Sifan Wang, Rujin Ma

Atomic Physics · Cross-listed from cond-mat.quant-gas

Charge-Conjugation Violation and Population Asymmetry in Bipartite Fermionic Lattices

Di Xiao, Xue-Ting Fang, Lushuai Cao et al. (+2 more)

Charge conjugation violation (CCV) is a central concept in particle physics and appears also for quasiparticles in quantum many-body systems, which typically relies on an embedded external symmetry breaking to the underlying system. An open question is how an intrinsic CCV mechanism could emerge and what its macroscopic consequences would be. We establish sublattice kinks in bipartite fermionic lattices as a concrete setup showing intrinsic CCV. The intrinsic CCV of the sublattice kink is based on the graph-topological nature of the underlying Hamiltonian, with no explicit symmetry breaking taking place. It leads to a population asymmetry of different configurations and imprints a hidden leaf-like structure in the eigenenergy spectrum. The population asymmetry also leads to an imbalanced sublattice-kink production triggered by the vacuum-instability in the quench dynamics. Our work demonstrates the graph topology as the microscopic origin of intrinsic CCV, with the population asymmetry as the macroscopic consequence, of which the proposed setup is highly amenable to experimental implementation via cold-atom quantum simulators.
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All authors Di Xiao, Xue-Ting Fang, Lushuai Cao, Zhong-Kun Hu, Peter Schmelcher

Classical Physics

Constitutive Settings with regard to Energy- and Entropy-Balances in Non-Equilibrium Thermodynamics: the Thermodynamical Verification

Wolfgang Muschik

Constitutive equations have to be in agreement with the energy- and entropy- balances. For achieving that, the procedure of thermodynamical verification is introduced: Because heat flux and entropy flux as well as the time differentials of internal energy and entropy are not independent of each other, energy- and entropy-balances are connected with each other by so-called internal settings laying down the theoretical frame of the applied material description which is characterized by additional constitutive settings.
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Fluid Dynamics

Multiple critical Froude numbers for the centrifugal effects on heat transport in rotating Rayleigh-Bénard convection

Zhi-Cong Kang, Guang-Yu Ding, Lu Zhang et al. (+1 more)

The influence of centrifugal effects in rotating Rayleigh-Benard convection is investigated using direct numerical simulations. We find that the Nusselt number decreases beyond a critical Froude number, Fr_c*. This critical value depends on both the Rayleigh number Ra and the aspect ratio Gamma, following power-law scalings with each parameter. We interpret Fr_c* as the onset of centrifugal effects within the thermal boundary layers. This interpretation is supported by the thickening of the boundary layers and a reduction in the planar heat flux. We compare Fr_c* with two previously proposed critical Froude numbers. The first, Fr_Hu, marks the onset of centrifugal effects in the bulk, as evidenced by changes in local heat flux and radial vortex motion. For Fr_Hu < Fr < Fr_c*, centrifugal effects primarily redistribute heat within the bulk and have little influence on the global heat transfer. The second, Fr_Horn, is based on a global force-balance argument. The similar dependence of Fr_c* and Fr_Horn on the aspect ratio suggests a close connection between the global force balance and the onset of centrifugal effects in the thermal boundary layers. These results demonstrate that centrifugal forcing influences the bulk flow and the thermal boundary layers differently in rotating Rayleigh-Benard convection. While relatively weak centrifugal forcing modifies the bulk dynamics, substantially stronger forcing is required to alter boundary-layer properties and global heat transport.
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All authors Zhi-Cong Kang, Guang-Yu Ding, Lu Zhang, Ke-Qing Xia

Optics · Cross-listed from quant-ph

Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link

Martin Clason, Joakim Argillander, Didrik Bergström et al. (+8 more)

Quantum key distribution (QKD) is increasingly considered for deployment in realistic communication networks, where long distances, heterogeneous fiber infrastructure, and coexistence with classical traffic present substantial challenges. Here, we demonstrate trusted-node QKD between Linköping University and the Stockholm hub of the Swedish national quantum communication infrastructure over 270 km of deployed single-mode fiber, extended by a 33 km multi-core fiber (MCF) segment emulating a metropolitan access link, for a total distance of 303 km. The two sub-links use commercial QKD systems whose receivers are interfaced with external superconducting nanowire single-photon detectors, enabling operation at losses beyond those supported by standard internal gated-mode detectors. We operate the link while actively switching the QKD channel between two MCF cores, with co-propagating Ethernet traffic and injected broadband optical noise in the other cores. The results demonstrate the integration of commercial QKD into demanding, dynamically reconfigurable fiber infrastructure relevant to future hybrid quantum-classical networks. Finally, using the generated secret keys, we illustrate how limited and time-varying QKD throughput affects one-time-pad-protected image transmission: image fidelity depends strongly on the available QKD-generated key budget and the choice of compression algorithm, highlighting application-level challenges for QKD-based encryption in realistic scenarios.
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All authors Martin Clason, Joakim Argillander, Didrik Bergström, Daniel Spegel-Lexne, Giulio Foletto, Ashraf El Hassan, Mohamed Bourennane, Onur Günlü, Katia Gallo, Rui Lin, Guilherme B. Xavier

Atmospheric and Oceanic Physics · Cross-listed from quant-ph

Quantum-limited estimation of atmospheric turbulence via spatial mode decomposition

A. Hrebeniuk, M. Klen, I. Karuseichyk et al. (+2 more)

We establish the ultimate precision limit for estimating the optical spatial coherence radius (Fried parameter) within a quantum metrological framework. In the weak field regime, we show that spatial-mode decomposition – originally introduced for superresolution imaging – enables substantially more precise estimation than conventional direct imaging when the receiver aperture is smaller than the coherence radius.
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All authors A. Hrebeniuk, M. Klen, I. Karuseichyk, N. Treps, A. A. Semenov

Medical Physics · Cross-listed from cs.AI

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming

Shah Pallav Dhanendrakumar, Saikat Pal and Sitikantha Roy

Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved diagnostics, prognosis, and treatment planning. Mechanistic models provide valuable scientific insight into the disorders, but in practice they are often simplified with assumptions or computationally expensive and slow to solve. However, while purely data driven approaches provide speed and scalability, they require large, high quality data to train and generally suffer from interpretability and generalization issues. This perspective paper presents a structured overview of hybrid modeling strategies, which combine deep learning models with physics based solvers, and are categorized into parallel, series, and parallel-series architectures. Three main approaches that have been emphasized are residual modeling for missing or incomplete physics, Neural Ordinary Differential Equations (NODEs) for continuous time dynamics approximation, and solver in the loop that accelerates traditional solvers with neural approximations. These hybrid models integrate the governing differential equation based formulations and deep learning to characterize the evolution of neurological disorders, and promise advanced personalized neurological modeling. In addition, the study explores and proposes different hybrid configurations to improve diagnosis accuracy, predict disease progression, and inform treatment strategies across a range of neurological disorders. These capabilities outperform standalone mechanistic or purely data driven approaches, making hybrid modeling a powerful tool, especially in applications involving modeling the progression and treatment responses in neurological conditions such as brain tumors, Alzheimer's disease, and stroke.
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Computational Physics · Cross-listed from math.NA

A tensor-train multidimensional inverse Laplace transform

Martin Mikkelsen and Michael Kastoryano

Laplace transforms and their numerical inverses arise throughout applied mathematics, physics, finance, and probability theory. Numerical inversion, however, quickly becomes intractable in high dimensions because the number of quadrature evaluations grows exponentially with dimension. We develop a tensor train (TT) formulation of the multidimensional inverse Laplace transform. The method constructs a TT approximation of the transformed function on the complex quadrature grid and then performs the inversion through a sequence of tensor contractions. Under suitable low-rank assumptions, this reduces the computational cost from exponential to polynomial in the dimension, provided that the relevant bond dimensions remain bounded. The method has only a small number of tunable parameters and admits error estimations. We demonstrate its performance in numerical experiments, benchmarked against Monte Carlo estimates and exact references, for multivariate normal-inverse Gaussian, Wishart, and correlated Gamma-type distributions.
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