Atomic Physics · Cross-listed from quant-ph
Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Pauline Mathiot, Elio Garnaoui, Axel-Ugo Leriche et al. (+37 more)
We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using
quantum kernel methods. The
algorithm is implemented on an atom-based logical
quantum processor, both at the physical and logical levels. We show that the kernel estimated from the logical implementation performs better than its physical counterpart on relevant metrics. We observe how such performance improvement can be traced back to specific noise-induced errors detected by the chosen encoding. We apply the computed
quantum kernel to the task of solving differential equations, confirming how the superior performance of a logical
quantum kernel is retained also at an end-to-end applicative level. Our findings show that experimental validation of end-to-end protocols can already highlight the positive impact of fault-tolerant implementations despite their higher
quantum resource count, and guide application-informed architectural choices.
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All authors Pauline Mathiot, Elio Garnaoui, Axel-Ugo Leriche, Evan Philip, Boris Albrecht, Clémence Briosne-Fréjaville, Lorenzo Cardarelli, Antoine Cornillot, Gwennolé Cournez, Luc Couturier, Julius De Hond, Rebecca El Koussaifi, Thomas Eritzpokoff, Florian Fasola, Antonio Andrea Gentile, Casper Gyurik, Clotilde Hamot, Loïc Henriet, Gaétan Hercé, Michael Kaicher, Lucas Lassablière, François-Marie Le Régent, Edgar Leroux, Yohann Machu, Hadriel Mamann, Luis Ortiz, Annie Paine, Thomas Pansiot, Arnaud Peloquin, Francisco Ponciano, Julien Ripoll, Raja Selvarajan, Adrien Signoles, Henrique Silvério, Siddhy Tan, Marie Taouzinet, Selim Touati, Louis Vignoli, Antoine Browaeys, Pascal Scholl
Data Analysis and Statistics · Cross-listed from cond-mat.dis-nn
Random Matrix Spectra from Boltzmann-Weighted Lattice Ensembles
Yaprak Önder, Abbas Ali Saberi and Roderich Moessner
We introduce a random
matrix framework for studying statistical-mechanical
lattice systems through spectral observables.
Equilibrium configurations sampled from a Boltzmann measure are mapped to
matrix ensembles whose covariance structure is inherited from the spatial correlations of the underlying model. This construction maps real-space correlation functions to a momentum-space variance profile, providing a direct bridge between statistical-mechanical correlations and correlated random
matrix ensembles. We derive this variance profile in finite-correlation-length and critical regimes, and compute spectral moments within a Wick-contraction expansion. A complementary self-consistent description of the bulk density is developed using the resolvent formalism. These analytical methods are benchmarked against Monte Carlo data for the two-dimensional Ising model and three-dimensional Edwards–Anderson
spin glasses. In both cases, the spectra evolve from the semicircle law at high temperature to model-dependent critical forms reflecting the structure of correlations. The framework, therefore, provides a quantitative spectral route to probing collective behavior in ordered and disordered statistical systems, while also defining a class of physically motivated correlated random
matrix ensembles.
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Atomic Physics
Non-equilibrium exciton dynamics in tailored molecular potentials of Rydberg ion crystals
Simon Euchner, Mathias B. M. Svendsen and Igor Lesanovsky
Trapped ions excited to high-lying electronic states combine strongly coupled collective vibrational and electronic degrees of freedom with long-ranged interparticle interactions. These ingredients enable the
quantum simulation of biochemical processes, associated with the dynamics of
excitons in non-perturbative parameter regimes. The key feature of such a
quantum simulator are electronic-state-dependent molecular potential surfaces which can be strongly coupled. This allows to shed light on a variety of mechanisms underlying
exciton transport. We illustrate this in a system of three trapped ions, which is amenable to an ab initio treatment. Given that ion traps can be routinely prepared with hundreds of ions, these
quantum simulators can immediately realise scenarios which are inaccessible by current numerical methods.
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Optics
Step-scan interferometry for high-fidelity hyperspectral nanoscopy
Gergely Nemeth and Ferenc Borondics
Fourier transform infrared nanospectroscopy (nano-FTIR) is a novel, increasingly adopted characterization method that leverages decades of established knowledge in infrared spectroscopy at the nanoscale. It opens up new possibilities in the characterization of composite materials and nanophotonic systems. Besides the rapid adoption and new possibilities, the nanoscale nature of these measurements poses new challenges for infrared spectroscopy. The current implementations of hyperspectral image acquisition at high spatial resolution suffer from significant artifacts due to thermal instabilities, which heavily affect positioning. As a result, the spatial and spectral fidelity of the measurements can be unreliable for long acquisitions. Here, we propose a new nano-FTIR measurement methodology based on step-scan interferometry and image registration. We demonstrate that the method provides superior spatial fidelity for photonics research and enables the collection of larger
datasets, paving the way for bringing machine learning to characterize nanoscale heterogeneity.
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Optics
Artificial Intelligence Reshapes Microwave Photonics
Peng Li, Xihua Zou, Jia Ye et al. (+2 more)
As a rapidly emerging interdisciplinary field that intrinsically integrates microwave and photonics, microwave photonics (MWP) provides disruptive solutions to overcome the fundamental bandwidth of conventional electronic systems. By exploiting the inherently ultra-wide bandwidth and low-loss characteristics of photonic technologies, MWP enables the generation, transmission, processing, and detection of microwave, millimeter-wave, and terahertz signals. Representative breakthroughs include fully photonic microwave radar systems, photonic analog-to-digital converters with bandwidth up to 320 GHz, and photonic wireless communication systems achieving data rate as high as 616 Gbit/s. Meanwhile, the rapid growth of artificial intelligence (AI) is reshaping scientific research, engineering, and daily life in unprecedented ways, such as AI for science/engineering and AI co-scientist/assistant. Correspondingly, AI is profoundly reshaping MWP in all aspects, ranging from signal generation, transmission to signal processing and detection. AI has revolutionized the design, simulation, fabrication, testing, deployment, and maintenance of MWP systems, delivering autonomous operation and exceptional efficiency beyond traditional systems. Motivated by these developments, this Review Paper provides the first comprehensive overview of AI-enabled MWP, systematically summarizing the state-of-the-art advances and presenting insights for both the academic community and the broader public.
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All authors Peng Li, Xihua Zou, Jia Ye, Wei Pan, Lianshan Yan
Atomic Physics · Cross-listed from quant-ph
High-order harmonic generation from an atom in a disordered environment
Simon His, Camille Lévêque, Jérémie Caillat et al. (+2 more)
Using one-dimensional simulations analyzed through the lens of open
quantum systems, we study the photoelectron's strong-field dynamics from an atom surrounded by a scattering environment stochastically structured. We theoretically investigate high-order harmonic generation from this situation. We show that local dephasing of the photoelectron wavepacket induced by elastic scattering leads to global decoherence. This drives a transition from
quantum to classical behavior, as witnessed by the photoelectron probability density localizing around specific trajectories of the classical analog system: unstable periodic orbits. This phenomenon mirrors
quantum scars traditionally observed in the eigenfunctions of time-independent systems, such as
quantum billiards. Here, it emerges in-situ within a time-dependent framework, manifesting directly in the real-time dynamics from the ground state rather than solely through spectral analysis.
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All authors Simon His, Camille Lévêque, Jérémie Caillat, Richard Taïeb, Jonathan Dubois
Fluid Dynamics
Effect of grid anisotropy, resolution, and subgrid-scale models in pseudo-spectral Large Eddy Simulations of low-level clouds
Davide Selvatici and Richard J.A.M. Stevens
We investigate the effect due to grid resolution and subgrid-scale model on large-eddy simulations of low-level clouds using a novel framework that combines pseudo-spectral advection with the anisotropic minimum dissipation (AMD) subgrid-scale model. We use two field campaigns as reference, DYCOMS-II RF01 and ASTEX, which cover both non-precipitating and precipitating stratocumulus cloud regimes across different time scales. Our results demonstrate that the AMD model combined with pseudo-spectral advection produces robust and accurate predictions across varying grid resolutions without parameter tuning. We identify a recommended grid anisotropy where vertical spacing is approximately three times finer than horizontal spacing, balancing accuracy and computational efficiency. Finally, an error analysis based on cloud
liquid water content and vertical velocity variance reveals good agreement with theoretical predictions for isotropic grids, while grid anisotropy effectively improves convergence rates.
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Fluid Dynamics
Beyond Vorticity: An Angular Momentum Perspective on Fluid Flow
Ahmed Farooq
While vorticity is the classical tool for analyzing rotational fluid kinematics, it inherently focuses on local, differential
spin. This paper introduces a complementary framework based on the angular momentum density field, $\mathbf{L} = \mathbf{r} \times \mathbf{u}$, deriving generalized transport equations that explicitly balance macroscopic torque and rotational momentum. This $\mathbf{L}$ perspective offers several distinct theoretical advantages over traditional velocity/vorticity formulations. Specifically, this approach: (i) provides a novel decomposition of the viscous torque into a diffusive component and a local
spin dissipative term; (ii) shows the mechanism by which lift is generated in viscous boundary layers by vorticity acting as a source of angular momentum; it also explains stall (iii) reformulates the hydrodynamic impulse to yield a remarkably clean separation of terms into dilatational, volumetric, and rotational flux components; The $\mathbf{L}$ formalism provides the kinematic closure necessary to unify non-circulatory added mass and circulatory lift within a single, dimensionally consistent budget. (iv) enables the direct calculation of the viscous added mass force, accounting for the inertial resistance of boundary layers and separated wakes; (v) simplifies geophysical fluid dynamics by absorbing the
planet's rotation, traditionally treated as an artificial virtual vorticity term which directly gets absorbed into the conserved axial angular momentum $m$, revealing the fundamental physics of global circulation through explicit torque balances; (vi) identifies the rotlet as a fundamental Green's function for the $\mathbf{L}$ transport equation in the Stokes regime; and (vii) demonstrates that both oblique shocks and
vortex sheets act as singular sources of $\mathbf{L}$ that turn the macroscopic flow.
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Computational Physics
Lumina: An AI-Augmented Multiscale Material Informatics Framework for Extreme Aero-Chemo-Thermo-Mechanical Regimes
Pradeep Kumar Seshadri, Vigneshwaran N, Sudaroli Dhananjeyan et al. (+8 more)
Predictive simulations and experimental design involving extreme aero-chemo-thermo-mechanical regimes require high-fidelity material representation across diverse physical states. However, data for metals,
polymers, and propellants, explosives, and pyrotechnics (PEP) remain fragmented, obstructing traceability for formulators, experimentalists, and simulation engineers. This work introduces Lumina, a modular Python-based informatics framework that centralizes multiscale material data from atomistic simulation
datasets to macro-scale experimental records, within a unified repository. Lumina employs a hierarchical XML-based schema and a dynamic runtime parsing mechanism to enable schema-independent parameter extraction. Beyond storage, the platform provides computational modules to visualize model fits, allowing experimentalists to optimize design of experiments (DoE) and formulators to validate chemical behaviors against
benchmarks. This structured architecture serves as a high-fidelity pipeline for training machine learning models and enhancing the accuracy of predictive simulations. To streamline multi-disciplinary workflows, Lumina integrates a conversational AI assistant for intelligent material
retrieval and natural
language querying. By consolidating multiscale data into an extensible ecosystem, Lumina provides a scalable foundation for data-driven discovery and predictive modeling in advanced defense and aerospace engineering.
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All authors Pradeep Kumar Seshadri, Vigneshwaran N, Sudaroli Dhananjeyan, Karthikeyan S, Navbila K, Sridhar S, Subhadevi K, Hari Sree Charan H, Abdul Azeez A, Jeswin Mickle, Harsha C
Physics and Society
How hate spreads online and why it returns: Re-entrant phases driven by collective behavior
Chen Xu, Pak Ming Hui, Chenkai Xia et al. (+1 more)
The 2025 Bondi Beach mass-shooting was perpetrated by individuals inspired by ISIS (Islamic State) propaganda that increasingly featured anti-Semitic hate content following the October 2023 start of the Israel-Palestine war. Similar stories hold for other types of hate attacks, e.g. against Muslims on May 18, 2026. There is an urgent need to get ahead of future threats by understanding how and when a newly created piece of hate content will spread system-wide online. We present a two-species coalescence-fragmentation model with Susceptible-Infected-Recovered dynamics that incorporates the following published empirical features: (1) New pieces of hate content tend to be generated and promoted by a subset of in-built communities on less regulated platforms. (2) These `hate' communities create links (hyperlinks) with each other and with non-hate communities across all platforms to form dynamically evolving clusters (i.e. coalescence) across which new hate content can then spread. (3) These clusters can get broken up by moderator shutdowns (i.e. fragmentation). We present numerical solutions and derive two levels of approximate mean-field theory: Effective Medium Theory (EMT) and Beyond Effective Medium Theory (BEMT). Both numerical and analytic solutions reveal that system-wide spreading is governed by re-entrant threshold phases: as the fraction of hate communities varies, the system can transition from spreading to no-spreading and back to spreading. The derived analytic formulae give explicit insight into how these phase boundaries might be manipulated to prevent system-wide spreading. More broadly, the re-entrant phase behavior warns that policies which steadily reduce the number of hate communities can initially succeed but then backfire if pushed further, suggesting that blanket requirements for platforms to simply do `more' are over-simplistic.
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All authors Chen Xu, Pak Ming Hui, Chenkai Xia, Neil F. Johnson
Optics
Low-Divergence Quasi-Gaussian Emission at Watt-Level Power from a Large-Diameter Ring-Aperture VCSEL
Marta Więckowska, Luke Graham, James Guenter et al. (+8 more)
The far-field emission of large-area vertical-cavity surface-emitting
lasers (VCSELs) is commonly associated with multimode, high-divergence beam profiles, limiting applicability in high-brightness free-space systems. We investigate angular emission characteristics of a 1 mm-diameter ring-aperture watt-class VCSEL and establish a theoretical framework capturing the formation of its far-field radiation patterns. Modeling the near field as an azimuthally modulated ring distribution and evaluating the far field within the Fresnel approximation, we demonstrate that a quasi-Gaussian far-field profile emerges from combined lower-order azimuthal modes, even in a highly multimode cavity. Experimentally, we observe a current-driven transition of the far-field distribution from a high-divergence ring at low injection levels to a narrow central beam at elevated currents. At high drive currents, the emission approaches a near-Gaussian profile with a full width at half maximum of 8{\deg}, while maintaining watt-class output power. Angle-resolved spectroscopy associates the central emission with longer-wavelength, lower-order modes, whereas the outer ring originates from shorter-wavelength, higher-order contributions. Combined with electroluminescence measurements and wavelength-dependent
photon lifetime analysis, these results demonstrate that spectral and angular emission are determined by the interplay between wavelength-dependent material gain and angle-dependent cavity losses. This approach establishes a general framework for controlling beam divergence and modal content in large-area VCSELs, enabling high-power operation with near-Gaussian, low-divergence beam profiles.
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All authors Marta Więckowska, Luke Graham, James Guenter, Jim Tatum, Freddie Castillo, Karolina Olucha, Justyna Maleszyk, Magdalena Marciniak, Michał Dobrski, Tomasz Czyszanowski, Michał Wasiak
Plasma Physics · Cross-listed from astro-ph.SR
Multi-diagnostic convergence: a single measurement in weakly collisional plasmas
Victor Edmonds
When multiple electron temperature diagnostics converge on the same value, the standard inference is that the measurement is robust. We show that this convergence is a structural consequence of the shared ionization bottleneck in any
plasma where the electron Knudsen number exceeds $\sim 0.01$: all diagnostics downstream of collisional ionization report the effective temperature $T_{\rm eff}$, not the core temperature $T_{\rm core}$. Their agreement is a single measurement reported $N$ times. We introduce a taxonomy: Type A (ionization-gated, $T_{\rm eff}$), Type B (bulk-sampling, $T_{\rm core}$), Type C (distribution-resolving). The ratio $R = T_A/T_B$ yields $\kappa = 3R/[2(R-1)]$ directly. Applied to the
solar corona ($R = 2.4$, $\kappa \approx 2.5$) and the tokamak scrape-off layer, single kappa distributions ($\kappa \approx 2$–$10$) reproduce published bi-Maxwellian EEDF decompositions to 3–8% RMS with one fewer parameter, and Thomson scattering confirms the predicted Type B temperature. We test applicability in planetary nebulae (the 80-year CEL–ORL abundance discrepancy). Knudsen calculations with the Shoub $v^4$ mean-free-path scaling show ionizing electrons are collisionless in the corona even when the bulk is fluid; in PNe, both ionizing ($\sim 55$ eV) and excitation ($\sim 5$ eV) electrons are collisional over nebular scales, identifying PNe as the falsification boundary; in the SOL, non-local parallel transport maintains tails even where local collisionality is high. For $\kappa \approx 3$–$5$, the raw Spitzer–Härm formula with spectroscopic $T_e$ overestimates parallel heat flux by factors of 3–25$\times$; flux-limited models inherit the bias through their boundary conditions, relevant to ITER divertor predictions. Every diagnostic campaign on a weakly collisional
plasma should include at least one Type B measurement.
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Chemical Physics
Information-Theoretic Appraisal of Electron Densities
Abdulrahman Y. Zamani and Kevin Carter-Fenk
We present an information-theoretic assessment of atomic and molecular densities in the ground state and under a range of physical scenarios–excitation, confinement, and ensemblization. Comparisons across densities obtained from single-reference methods are facilitated through information
entropy measures evaluated in position space. We demonstrate that the J-divergence serves as a key metric for benchmarking electron densities against coupled cluster and configuration interaction references. Mean-field orbital information is further compared with that of Brueckner and Dyson orbitals, and informational changes in multiple self-consistent-field solutions are examined under various
symmetry-breaking conditions. We also explore the relationship between entropic measures of electron delocalization and the accuracy of the CO dipole moment computed with different methods. Our work offers insights into the selection of optimal reference determinants for a given chemical application and highlights potential benefits of incorporating information-
entropy concepts in the development of new density functionals.
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Biological Physics
Label-free SERS Discrimination of Native Proline Hydroxylation at Single-molecule peptide by Deep Learning-assisted plasmonic nanopore
Yingqi Zhao, Kuo Zhan, Pei-Lin Xin et al. (+6 more)
Post-translational modifications (PTMs) play essential roles in regulating
protein structure, function, and cellular signalling. However, peptide level discrimination of hydroxylation at the single-molecule level remains difficult. Here, we report a particle-in-pore single-molecule surface-enhanced Raman spectroscopy (SERS) platform combined with peak occurrence frequency (POF) analysis and a one-dimensional convolutional neural network (1D-CNN) for discriminating hydroxylated and non-hydroxylated HIF peptide fragments. Three peptide pairs containing the Pro-564 hydroxylation site, with lengths of 7, 9, and 15 amino acids (AAs), were investigated. POF analysis revealed reproducible hydroxylation-dependent spectral changes in the 7AA and 9AA peptide pairs, which were attributed to changes in adsorption conformation and surface interactions. CNN-based classification achieved post-evaluation accuracies of 72.98%, 78.55%, and 89.74% for the 7AA, 9AA, and 15AA peptide pairs, respectively, with AUC values above 0.80 for all the pairs, indicating a reliable discrimination. Gradient-weighted feature visualization further showed that CNN-sensitive regions overlapped with recurrent POF features, supporting the chemical relevance of the learned classification patterns. Notably, for the 15AA peptide pair, the enhanced citrate-associated band suggests that hydroxylation can substantially alter peptide-gold nanoparticle adsorption behaviour. This adsorption-mediated effect may amplify hydroxylation-induced spectral differences and contribute to the improved discrimination accuracy despite the increased structural complexity. These results demonstrate that the particle-in-pore sensor, assisted by deep learning, can capture hydroxylation-induced spectral and adsorption changes in peptide fragments, providing a promising strategy for ultrasensitive analysis of weak PTM signatures in peptides.
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All authors Yingqi Zhao, Kuo Zhan, Pei-Lin Xin, Yuge Liang, Enock Adjei Agyekum, Matti Putkonen, Shuai Li, Francesco De Angelis, Jianan Huang
Applied Physics
AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translation
D.-M. Mei, K. Acharya, C. M. Adhikari et al. (+51 more)
Materials discovery and biomedical translation increasingly require models that can reason across composition, processing, structure, biological response, manufacturability, safety, and governance constraints. Existing materials and biomedical data ecosystems are powerful but remain poorly coupled for AI-guided discovery. Here we present AIMBio, a conceptual framework for an AI-native, FAIR, and governance-aware decision layer that links materials provenance, biomedical context, knowledge graphs, uncertainty-aware machine learning, and human-in-the-loop active learning. The framework formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty and introduces practical requirements for metadata, model documentation, risk-tiered governance, evaluation metrics, and phased implementation. To make the roadmap testable, we add a minimum viable prototype specification and a worked pilot for AI-guided nanomaterials for drug delivery. AIMBio is positioned as exploratory and preclinical discovery infrastructure, not as clinical decision-support software; any clinical or regulated-device use would require separate validation, change control, and regulatory review. The central contribution is a publishable platform blueprint for converting fragmented materials and biomedical records into auditable, experimentally actionable, and translationally responsible discovery workflows.
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All authors D.-M. Mei, K. Acharya, C. M. Adhikari, M. Adhikari, S. Aryal, B. V. Benson, K. Bhatta, S. Bhattarai, N. Budhathoki, A. M. Castillo, D. Chakraborty, S. Chhetri, S. Choudhury, T. A. Chowdhury, R. D. Cruz, B. Cui, S. Dhital, K.-M. Dong, R. Gapuz, A. Ghasemi, E. Z. Gnimpieba, B. D. S. Gurung, H. A. Hashim, R. I. Harry, K.-E. Hasin, M. K. Hassanzadeh, M. K. Jha, D. Kim, K.-C. Kong, B. Lama, A. Mahat, N. Maharjan, A. Majeed, J. Mammo, M. M. Masud, K. S. Moore, A. Nawaz, H. Oli, S. A. Panamaldeniya, L. Pandey, R. Pandey, Z. Peng, A. Prem, M. M. Rana, K. Rana Magar, R. Rizk, C. S. Tadi, L.-W. Wang, Y. Yang, G.-L. Yin, C.-X. Yu, D. Zeng, M. Zhou, Q. Zhou
Optics
Hybrid-Integrated DFB-Laser-Coupled 1 * 8 Thin-Film Lithium Niobate Modulator Array for High-Speed Parallel Optical Transmitters
Qiyue Hu, Junxia Zhou, Zhe Wang et al. (+9 more)
Thin-film lithium niobate (TFLN) electro-optic modulators are attractive for high-speed optical interconnects, but scalable transmitter architectures require not only high modulation bandwidth but also multi-channel optical power distribution and practical
laser-to-chip integration. Here, we demonstrate a hybrid-integrated 1 * 8 TFLN electro-optic modulator array passively butt-coupled to a 1550 nm distributed-feedback
laser. The chip integrates a three-stage cascaded 1 * 2 multimode-interference splitter, spot-size converters, eight traveling-wave Mach-Zehnder modulators, thermal tuning electrodes, and on-chip 50 {\Omega} terminations. The cascaded splitter provides uniform optical power distribution with a maximum normalized power deviation of 9.7%, while the optimized electrodes enable electro-optic 3 dB bandwidths exceeding 40 GHz for all channels. The measured half-wave voltages are 3.60-3.83 V, corresponding to V{\pi}L products of 2.52-2.68 V cm for a 7 mm modulation length, and the extinction ratio reaches approximately 25 dB. The bare-chip insertion loss is 15.19-16.55 dB, and DFB
laser bonding introduces an additional coupling loss of approximately 5 dB while preserving channel uniformity. These results establish a practical TFLN-based multi-channel modulator platform and represent a step toward compact hybrid-integrated optical transmitters for high-speed parallel interconnects.
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All authors Qiyue Hu, Junxia Zhou, Zhe Wang, Botao Fu, Jinming Chen, Yunpeng Song, Dewei Zhang, Yuheng Chen, Jinxin Huang, Min Wang, Jia Qi, Ya Cheng
Fluid Dynamics
A Fixed-Grid Affine-Constrained Multiwavelet Coefficient Method for Buckley–Leverett Shock Capturing
Christian Tantardini and Evgueni Dinvay
We present a fixed-grid conservative affine-constrained modal/multiwavelet coefficient method for one-dimensional Buckley–Leverett saturation transport. The saturation is evolved directly in a local orthonormal coefficient basis with a mean/detail structure: the first mode carries the conservative
cell average, whereas higher modes carry zero-mean local details. The
hyperbolic inflow condition is imposed as a linear trace constraint on the coefficient vector and enforced by affine lifting. For $(p>1)$, the boundary reprojection is applied in the detail subspace of the inflow
cell, so that the prescribed trace is restored without modifying the conservative
cell-average update. The transport operator is discretized in conservative weak form with monotone numerical fluxes, and shock-induced oscillations are controlled by a troubled-
cell limiter acting on modal details. The method is validated on a Berea-core waterflood
benchmark against an independent pywaterflood reference solution using the same Corey fractional-flow closure, physical parameters, and pore-volume-injected scaling. The affine-constrained coefficient solver reproduces the reference breakthrough curve and saturation profiles, preserves the imposed inflow trace to roundoff accuracy, controls saturation bounds through mean-preserving detail rescaling, and gives small accumulated global mass-balance defects. Mesh-refinement, flux-comparison, and modal-order studies show that $(p=2)$, corresponding to a piecewise-linear local representation, provides the most favorable accuracy–cost compromise among the tested orders for this shock-dominated
benchmark.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Efficient purely organic phosphorescent emitters for programmable luminescent tags: from building blocks to donor-acceptor-donor structures
Uliana Tsiko, Sebastian Kaiser, Jannis Fidelius et al. (+4 more)
Purely organic room-temperature phosphorescence (RTP) emitters are key components of programmable luminescent tags (PLTs), photonic devices for rewritable information storage and UV dosimetry. In this work, we systematically explore the design space of donor-acceptor and donor-acceptor-donor organic phosphorescent emitters in symmetric and asymmetric architectures. Phenoxathiine (PX) is introduced as an alternative donor to thianthrene (TA), combined with benzophenone (BP) or pyridine (Py) as acceptors. Through photophysical characterization,
quantum chemical simulations, and PLT device testing, we identify structure-property relationships and, in particular, investigate the impact of the individual moieties on the emission properties and stability. The RTP emission wavelength is primarily tunable through the donor moiety: PX-based emitters emit sky-blue ({\lambda}_P = 480 nm), while TA-based emitters emit in the green ({\lambda}_P = 520 nm) due to an increased Stokes shift. The acceptor unit strongly influences the phosphorescence
quantum yield, with Py-based emitters systematically outperforming BP-based ones. All newly synthesized PX-containing emitters show sufficient performance in PLT devices, though with reduced photostability compared to TA-based counterparts. Together, these results demonstrate that systematic donor-acceptor design enables predictable control over RTP emission properties, advancing the rational development of high-performance RTP-based photonic devices.
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All authors Uliana Tsiko, Sebastian Kaiser, Jannis Fidelius, Tim Achenbach, Jan J. Weigand, Sebastian Reineke, Karl Sebastian Schellhammer
Chemical Physics · Cross-listed from cond-mat.soft
Microscopic Nonaffine Deformation Theory of LAOS in Polymers
Dario Nichetti and Alessio Zaccone
We develop a molecularly motivated framework connecting large-amplitude oscillatory shear (LAOS) nonlinearities in entangled
polymers to frequency-dependent nonaffine relaxation in disordered solids. The central idea is that the first harmonic in LAOS measures the residual phase-locked elastic response, whereas the higher harmonics encode the
Fourier signature of strain-dependent nonaffine relaxation. The finite-amplitude modulus is interpreted as a local tangent stiffness of the evolving microstructure, in the spirit of elastoplastic and incremental nonaffine models. For entangled
polymers, the analogue of the decreasing coordination number in cage-breaking theories of glass mechanics is identified not with the tube-orientation tensor itself, but with the fraction of surviving tube constraints. This distinction leads naturally to a crossover description controlled by a characteristic strain amplitude $\gamma_c$, rather than by universal fixed power-law exponents. The fitted value $N_{\max}\simeq1.72$ indicates that the present experimental data approach a strong but not fully saturated nonlinear state, remaining below the ideal limiting value predicted for complete constraint collapse. Finally, a constraint-counting argument combining an eight-chain affine network representation with the central-force nonaffine isostatic threshold gives a limiting estimate $|\mathrm{NLI}|_{\max}=3$. The results support the interpretation of the NLI as a
Fourier-resolved dynamic nonaffinity parameter and establish a bridge between tube-based
polymer dynamics, LAOS harmonic analysis, elastoplastic rheology, and microscopic nonaffine
lattice dynamics.
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Medical Physics
Physics-informed neural networks for quantitative assessment of cancellous bone microstructure from photoacoustic signals
Shoukun Lyu, Haohan Sun, Shibo Nie et al. (+5 more)
Artificial intelligence (AI) empowers innovative diagnostic tools for common diseases, yet its clinical application in skeletal health evaluation is constrained by unsatisfactory accuracy, owing to the inherent porous and poroelastic biophysical features of bone. To address such bottlenecks amid global population aging, this study targets skeletal health and develops a reliable AI framework for precise bone microstructural characterization. We proposed Biot-PINN, a physics-informed neural network embedded with Biot's poroelasticity theory to characterize mechanical responses and wave propagation in poroelastic bone tissues. By decoding photoacoustic signals encoding bone mineral and microstructural features, the framework enables automatic bone microstructural grading. Experimental results reveal that Biot-PINN reaches an accuracy of 97%, markedly surpassing traditional data-driven approaches and providing a robust solution for early skeletal health diagnosis.
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All authors Shoukun Lyu, Haohan Sun, Shibo Nie, Weiya Xie, Ying Gu, Shiying Wu, Ya Gao, Qian Cheng
Optics
Demonstration of Broadband Non-Resonant Time-Crystal Amplification in Microwaves
Thomas R. Jones, Ludmila J. Prokopeva, Alexander V. Kildishev et al. (+2 more)
We report an optically modulated experimental realization of a photonic time
crystal (PTC) in the microwave regime, demonstrating for the first time that the PTC exponential growth can overcome losses and finite-size constraints of a practical spatio-temporal system and yield stable positive terminal gain over a continuous broadband frequency range. The developed experimental platform is a purely time-modulated capacitor (TMC) microwave
circuit based on a microstrip transmission line, in which synchronized optical modulation of reverse-biased photodiodes generates strong (94.5 %) temporal modulation of the effective capacitance at 200 MHz. Broadband amplification consistent with a momentum band gap (MBG), a defining signature of photonic time-
crystal physics, is observed, with a peak gain of 3.8 dB over a 65 MHz bandwidth. In addition, a narrow parametric resonance appears at the center of the band gap, reaching 4.8 dB. This sharp peak is associated with the spatial inhomogeneities of the lumped-element realization, while the corresponding homogeneous distributed system retains the Floquet-mode structure of a photonic time
crystal. We show that finite microwave TMC implementations inherit the defining physics of PTCs, including phase-invariant non-resonant amplification and slow-light behavior inside the momentum band gap, while finite-size and loss mechanisms transform the ideal semicircular PTC gain profile into a continuous asymmetric non-Lorentzian gain band characterized by a Pearson type IV distribution.
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All authors Thomas R. Jones, Ludmila J. Prokopeva, Alexander V. Kildishev, Mordechai Segev, Dimitrios Peroulis
Optics
A Universal Magnetoelectric Limit for Chiral and Tellegen Bi-Isotropic Scatterers
Jorge Olmos-Trigo
We reveal the existence of a universal upper bound on the magnetoelectric coupling of any bi-isotropic nanoparticle. The bound arises solely from energy conservation, making it independent of the specific material properties of the nanoparticle and illumination conditions. Moreover, the bound does not rely on reciprocity, being identical for reciprocal (
chiral) and non-reciprocal (Tellegen) nanoparticles. We further show that the
chiral Mie coefficient of spherical particles of arbitrary optical size obeys the same bound across all multipolar scattering channels. Our results introduce a universal metric on the magnetoelectric coupling of bi-isotropic objects, setting identical limits on
chiral and Tellegen light-matter interactions at the single particle level.
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Chemical Physics · Cross-listed from cond-mat.soft
Thermodynamic and structural behavior of one-dimensional divalent patchy hard rods: Wertheim's first-order thermodynamic perturbation theory vs exact results
Ana M. Montero, Andrés Santos, Péter Gurin et al. (+1 more)
We investigate the thermodynamic and structural properties of divalent patchy hard rods confined to a one-dimensional channel by modeling the bonding sites as attractive square-well (SW) patches located at the rod tips. The zero-range sticky limit is recovered by letting the well width vanish while keeping the stickiness parameter finite. While Wertheim's first-order thermodynamic perturbation theory becomes exact in this sticky limit, it fails for finite-range site–site interactions. Because the present model is mathematically equivalent to an exactly solvable one-dimensional nearest-neighbor fluid, we use the exact solution to reformulate the thermodynamics in terms of association-theory variables, including the fraction of unbonded sites and a generalized law of mass action. Finite-range SW sites produce a richer structural behavior than sticky sites, including monotonic and oscillatory asymptotic decay of the pair correlation function, separated by the Fisher–Widom line. In the monotonic regime, the correlation length exhibits an absolute maximum defining the Widom line, while in the oscillatory regime it may display a local maximum and minimum, whose locus defines the “Extrema of the Correlation length under Oscillatory decay” line. These features disappear in the sticky limit, where the system remains entirely in the oscillatory regime. We also show that the high-pressure behavior of the correlation length changes from $\xi\sim p^2$ for finite-range SW sites to $\xi\sim p^3$ in the sticky limit.
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All authors Ana M. Montero, Andrés Santos, Péter Gurin, Szabolcs Varga
Computational Physics · Cross-listed from cs.CV
Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data
Islam Mansour, Ronny Haensch, Irena Hajnsek et al. (+1 more)
Integrating machine learning (ML) with physical models (PM) has emerged as a promising way of retrieving geophysical parameters from remote sensing data. In this context, a ML model for estimating forest height from TanDEM-X interferometric coherence measurements has recently been proposed, that constrains the learning process through a PM. While the features used for training and inversion where selected to ensure the physical consistency of the solutions, they could not resolve all height / structure and baseline / terrain slope ambiguities in the data. To improve this, the extension of the feature space with optical Landsat data is proposed able to provide complementary information on forest type or structure. The extended model is applied and validated on several TanDEM-X acquisitions over the Gabonese Lopé national park site and assessed against airborne
LiDAR measurements. Results show a 13.5% reduction in RMSE and a 16.6% reduction in MAE compared to the original hybrid model, confirming the added value of multispectral inputs.
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All authors Islam Mansour, Ronny Haensch, Irena Hajnsek, Konstantinos Papathanassiou
Plasma Physics
Photodiode based multi-modal diagnostic for low-energy neutral beam injection in the LTX-$\beta$ spherical tokamak
A. Maan, Tosh Le, D.P. Boyle et al. (+8 more)
We present a compact photodiode-based diagnostic array developed to study low-energy neutral beam injection in the LTX-$\beta$ spherical tokamak. The in-
vacuum diagnostic combines filtered soft-x-ray (SXR), narrowband Lyman-$\alpha$, and unfiltered AXUV photodiode rows with partly overlapping, nearly coincident tangential views of the
plasma, including the neutral beam path. This geometry provides simultaneous sensitivity to beam-induced SXR emission; neutral-hydrogen line radiation associated with recycling, fast neutrals and fueling; and broadband emission that can include direct neutral impacts from fast-ion charge-exchange losses. Initial measurements from 12-20 keV hydrogen beam operation show beam-synchronous detector responses in all three modalities. The unfiltered AXUV signals exhibit millisecond-scale rise and fall times that are much slower than the detector response, that vary across sightlines, and depend on lithium-conditioning history. Comparison with classical slowing-down time estimates indicates that charge exchange with background neutrals contributes appreciably to the measured decay. The diagnostic can potentially be used to constrain a forward model to estimate the time-resolved balance of beam heating and fueling for small tokamaks.
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All authors A. Maan, Tosh Le, D.P. Boyle, R. Majeski, S. Banerjee, G.J. Wilkie, M. Lampert, C. Lopez Perez, R. Shousha, W. Capecchi, H. Gajani
Physics and Society
Equilibrium and dynamics of a three-state opinion model on a network of networks
Irene Ferri, Albert Díaz-Guilera and Hiroki Sayama
Opinion formation models typically represent each individual as a single variable. However, in practice each individual holds interconnected beliefs whose internal organization may influence collective outcomes. To explore this dependence, we study a three-state opinion model on a network of networks in which each
agent has an internal belief graph and interacts with other
agents through an external social graph. Each belief can take two opposite polarized states or a neutral one and a neutrality parameter tunes the relative conviction of the neutral stance. We incorporate temperature into the model to account for external social agitation and for the tolerance of internal cognitive dissonance. We explore the stationary state and dynamics of the model using analytical approaches and Monte Carlo simulations on a fully connected external social graph, with internal belief
topologies given by one-dimensional chains, cliques, and
star-like structures, where there is a central core belief to which all other beliefs are connected. We find that the critical temperature at which the polarized consensus destabilizes increases with the addition of more beliefs to
star-like
agents but saturates in the case of ring- and clique-like internal
topologies. We also consider binary mixtures of
agents with different internal
topologies in equal proportions, showing that the interplay between
agents is regime-dependent, with the dominant
topology depending on the value of the neutrality parameter.
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Biological Physics · Cross-listed from cond-mat.soft
Origin of Persistent Boundary Motion in Confined Active Matter
Elsa Baby, Manoj Gopalakrishnan and Vishwas V. Vasisht
Active matter systems under confinement display persistent surface motion and a strong boundary affinity. However, despite extensive studies of their positional dynamics, much less
attention has been given to the corresponding orientational behavior. Here, using molecular simulations of an active Brownian particle confined within a hard circular boundary and the Fokker-Planck equation, we show that the positional distribution of the particle is directly coupled to orientational fluctuations, as characterized by the conditional orientational distribution. Confinement generates two preferred tangential orientational states connected by
stochastic flipping pathways: rapid boundary-localized switching and slower bulk-mediated excursions. Further, the positional distribution exhibits a nontrivial power-law decay with distance from the boundary that is closely linked to
curvature-induced bistable orientational states and the variance of the associated conditional distribution. The mean waiting time between flips exhibits power-law dependence on the confinement strength. Our results establish that the interplay between orientational fluctuations, bistability, positional accumulation, and
stochastic switching governs the observed dynamics of active particles under confinement, providing a framework for understanding transport, exploration, and escape processes in confined active systems.
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Optics
Spin-polarized lasing in a photonic lattice
A. Herrero Otermin, N. Carlon Zambon, A. Bieganowska et al. (+3 more)
We characterize
spin-polarized lasing in a two-dimensional photonic
lattice fabricated from a GaAs/InGaAs
semiconductor microcavity sample. The
lattice is defined by a staggered arrangement of rounded rectangular micrometric mesas that laterally confine and couple the optical modes. Polarization-, angle-, and energy-resolved micro-photoluminescence measurements reveal the transition from the strong-coupling regime to
photon lasing, accompanied by extended spatial coherence across several
lattice unit
cells. Under circularly polarized nonresonant excitation, the emitted light acquires a controllable circular polarization whose handedness follows that of the pump. These results establish photonic-
lattice VCSELs as a platform for
spin-controlled coherent emission in extended optical systems.
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All authors A. Herrero Otermin, N. Carlon Zambon, A. Bieganowska, F. Jabeen, L. Viña, C. Antón-Solanas
Atmospheric and Oceanic Physics
Blending machine learning and physics-based approaches for weather and climate: a typology
Benjamin J Shipway, Caroline Bain, David Walters et al. (+6 more)
The integration of machine learning (ML) with traditional physics-based models is reshaping the landscape of weather and
climate prediction. On their own, ML-based and physics-based approaches each have significant benefits - but also challenges. Deploying both these approaches side by side has the potential to accelerate the pull through of emerging science in a trusted and practical way. But there are many choices that can be made to how we "blend" ML and established physics-based modelling systems to get the optimal benefits. This paper aims to provide a typology of blended modelling approaches and discusses some of the strategic benefits that come with them. It can be used not just to classify modelling systems, but also identify routes to gradual, incremental or wholesale development and implementation of new and emerging capabilities. These approaches provide a practical path to innovation by combining the speed and adaptability of machine learning with the robustness, trust, and interpretability of physics-based systems. By adopting a structured vocabulary and outlining the benefits and limitations of each approach, this framework supports informed decision-making and strategic planning, and can be used by the wider community to navigate the transition to next-generation prediction systems.
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All authors Benjamin J Shipway, Caroline Bain, David Walters, Ben B. B. Booth, Ian Boutle, Robin T. Clark, Katherine L. Hill, Elizabeth Kendon, Simon B. Vosper
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Topological phononics
Zeguo Chen, Tiantian Zhang, Xulong Wang et al. (+12 more)
Topological phononics extends the foundational concepts of topological condensed matter physics to the realm of
lattice vibrations and classical mechanical waves, unlocking robust, defect-immune states and phenomena beyond the reach of conventional phononic engineering. This review provides a unified, systematic framework for understanding topological
phonons across natural and artificial systems, spanning solid-state materials, acoustic/mechanical metamaterials, and non-Hermitian platforms. We cover the core theoretical principles – from Berry
curvature and
symmetry-protected topological invariants to bulk-boundary correspondence – alongside experimental advances in probing topological
phonon states via inelastic scattering and momentum-resolved techniques for solid-state
phonons as well as pump-probe measurements in acoustic/mechanical metamaterials. Key topics include Weyl/Dirac/nodal-line
phonons in crystalline solids,
symmetry-engineered topological phases in metamaterials, non-Hermitian effects (exceptional points, skin effect), and emergent directions such as Floquet engineering, synthetic dimensions, and real-space topological textures (skyrmions, merons). We also highlight technological applications in robust waveguides, on-chip surface-acoustic-wave devices, and acoustofluidics, while outlining future challenges and opportunities in
quantum phononics, nonlinear topological phenomena, and interdisciplinary integration with photonics and electronics. This review serves as a comprehensive guide across physics, materials science, and engineering, bridging fundamental theory with cutting-edge experiments and innovations in topological phononics.
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All authors Zeguo Chen, Tiantian Zhang, Xulong Wang, Jiangxu Li, Zhi-Kang Lin, Feng Gao, Li-Wei Wang, Yizhou Liu, Qi Wang, Xiujuan Zhang, Guancong Ma, Xingqiu Chen, Minghui Lu, Yanfeng Chen, Jian-Hua Jiang