Plasma Physics
Topology of Plasma Wakefields Driven by Two Color Laguerre Gaussian Laser Pulses
Saumya Singh, Dinkar Mishra, Shivani Aggarwal et al. (+2 more)
Plasma wakefield excitation driven by two color Laguerre Gaussian
laser pulses carrying orbital angular momentum is investigated analytically and through quasi-cylindrical particle in
cell simulations. Using a perturbative framework together with the quasistatic approximation, the influence of the transverse
laser mode structure on the longitudinal and transverse wakefields in an underdense
plasma is examined in the weakly relativistic regime. The results show that drivers with finite azimuthal index produce reduced and less regular on-axis longitudinal wakefields compared to conventional Gaussian drivers. However, radial longitudinal field distributions reveal that this reduction originates from a redistribution of the wakefield energy toward finite radii rather than a simple loss of wake excitation. Orbital angular momentum carrying modes generate hollow and ring shaped wake structures accompanied by strongly modified transverse electric fields and broader
plasma density perturbations. Mixed Gaussian Laguerre Gaussian configurations exhibit intermediate behavior, combining weak on-axis acceleration with pronounced off axis wake excitation. The study demonstrates that structured two-color
laser drivers fundamentally modify the
topology of
plasma wakefields and provide an additional mechanism for controlling transverse
plasma dynamics, off-axis acceleration, and angular momentum mediated wakefield structures in
plasma based accelerator schemes.
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All authors Saumya Singh, Dinkar Mishra, Shivani Aggarwal, Bhupesh Kumar, Pallavi Jha
Instrumentation and Detectors
Compact Dual-Polarization Schottky Barrier Diode Receivers for Submillimeter Wave Remote Sensing
Olivier Auriacombe, Peter J. Sobis, Vladimir Drakinskiy et al. (+2 more)
Dual-polarization heterodyne receivers operating at 325 GHz, 424 GHz, and 650 GHz at room temperature are presented. Polarimetric measurements are enabled by two orthogonal open-ended E-field probes, co-optimized and integrated with two subharmonic GaAs Schottky-barrier diode mixers. The down-converted signals (IF) are amplified using low-noise InP HEMT amplifiers integrated into the receiver module, along with IF matching networks, dc-bias boards, a shared local oscillator (LO) distribution network, and a single smooth-walled, conical, spline-horn
antenna. Maximum cross-polarization isolation of 25 dB, 34 dB, and 25 dB was achieved at 315 GHz, 421 GHz, and 650 GHz, respectively. The measured double-sideband (DSB) receiver noise temperatures are 833 K, 835 K, and 1623 K at 315 GHz, 421 GHz, and 630 GHz, respectively. Stability measurements, with an integration Allan time of more than 10 s, were obtained for all receivers. Overall, the integrated dualpolarization receiver
topology achieves excellent sensitivity in a highly compact package, offering an efficient and scalable solution for polarimetric applications in submillimeter-wave remote sensing
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All authors Olivier Auriacombe, Peter J. Sobis, Vladimir Drakinskiy, Anders Emrich, Jan Stake
Optics
Self-healing of the Montgomery pattern
Athena Xu, Oscar de Vries, Alfonso Palmieri et al. (+3 more)
Self-healing – the ability of a structured beam to reconstruct its transverse profile after partial obstruction – has been demonstrated for diffraction-free beams, where the recovery distance varies continuously with obstruction size. Here, we investigate self-healing in the Montgomery pattern, a self-imaging of tightly localized optical fields. Using Babinet's principle, we show theoretically that the recovery distance is quantized in integer multiples of the self-imaging period – a qualitative distinction from all previously studied self-healing beams. We confirm these predictions experimentally using a programmable
holographic setup with circular disk obstructions of size up to $20\times$ of the spot size of the Montgomery pattern at the self-imaging plane, establishing the robustness of the Montgomery pattern against scatterers and obstructions in the beam path.
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All authors Athena Xu, Oscar de Vries, Alfonso Palmieri, Murat Yessenov, Ayman F. Abouraddy, Federico Capasso
Classical Physics
Violin ”Playing-In”: Disentangling Physical Change from Player Adaptation via Physical Measurements
Hugo Pauget Ballesteros, Philippe Lalitte, Vincent Lostanlen et al. (+1 more)
It is a widespread belief among musicians that a violin's sound “opens up” or improves through regular playing. However, physical evidence for this “playing-in” effect remains elusive. This study revisited the phenomenon by testing two hypotheses: (1) the instrument undergoes physical
evolution, or (2) the player undergoes behavioral adaptation. We conducted a longitudinal study centered on a seldom-played test violin played daily by a professional soloist for six months, alongside two stored control violins and a control group of ten violinists (N=10). All three violins had been rarely played prior to the experiment. Assessments performed at the beginning (“Before” phase) and end (“After” phase) of the period included input admittance measurements via
laser vibrometry, standardized sound recordings, and acquisition of the soloist's bowing gestures via motion capture. Results revealed no acoustical
evolution in the played violin exceeding the environmental drift observed in the control instruments. Furthermore, kinematic analysis showed no significant drift in the soloist's bowing strategy (bow position, velocity, contact point). These findings suggest that the “playing-in” phenomenon is driven neither by macroscopic physical changes in the instrument nor by a fundamental reorganization of the player's technique.
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All authors Hugo Pauget Ballesteros, Philippe Lalitte, Vincent Lostanlen, Claudia Fritz
Optics · Cross-listed from cond-mat.mes-hall
Strong nanomechanical Duffing nonlinearity and interactions induced through cavity optomechanics
Jesse J. Slim and Ewold Verhagen
Nonlinearity is a key resource in both classical and
quantum signal processing. Nonlinear nanomechanical elements have found applications ranging from sensing to computing, while networks of nonlinear resonators, as well as nonlinearly coupled networks of linear resonators, constitute promising platforms for simulating complex dynamics. Here, we experimentally demonstrate an approach to realizing strong mechanical nonlinearity in nanomechanical resonators, fully controlled through optical
laser drives. The mechanism exploits the nonlinearity of the radiation-pressure interaction in a cavity optomechanical system, which gives rise to a nonlinear optical spring effect. The resulting Duffing nonlinearity is conveniently tunable in strength via pump
laser power, while its sign is controlled by
laser detuning. Moreover, we demonstrate that the nonlinear optical spring mediates effective interactions between mechanical modes coupled to a common cavity, inducing tunable nonlinear interactions between them that impact spectral response and dynamics. These results establish cavity optomechanics as a versatile and in-situ reconfigurable platform for engineering nonlinear dynamics in resonators and networks.
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Fluid Dynamics
Faraday waves covered by a viscoelastic sheet
Hanna Pot, Bram Christiaens and Willem van de Water
The hydroelastic response of free floating viscoelastic covers is measured using Faraday waves on the surface of a vertically oscillated fluid layer. We systematically vary the thickness $d$ of the covers to investigate its effect on the hydroelastic dispersion relation, the damping and the isotropy of the waves. Compared to bare fluids, the wave patterns are disordered. Various methods are explored to define and analyze the wavelengths, the isotropy, and shape of the waves. We find a significant difference between the measurements and the theoretical dispersion relation. Over all thicknesses $d$, this is explained by an increase in the in-plane membrane tension, which scales with $d^{3/2}$. Covering waves also has a large efect on their damping. Only for thin covers ($d = 20\: \mu{\rm m}$) the onset amplitude (and thus the damping) can be explained by dissipation in the bulk and in the boundary layer of the water beneath the cover. The same was found for bare water due to the presence of an immobile surface layer. Lastly, we find a large effect of the membrane on the ampitude of the waves, which we attribute to nonlinear wave interaction.
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Optics
Energy-Resolved Eigenmode Spectroscopy of 1-D and 2-D Non-Hermitian Skin Effects
Rohith Srikanth, Sashank Kaushik Sridhar and Avik Dutt
Non-Hermitian
lattices can host the non-Hermitian skin effect, a boundary-induced collapse of all bulk eigenstates into exponentially localized edge modes. This effect underlies anomalous bulk-boundary correspondence and remarkable enhancements in non-Hermitian sensing, yet direct energy-resolved access to the eigenmodes of non-Hermitian
lattices has remained limited. Here we report band- and energy-resolved eigenmode spectroscopy of skin modes in a frequency synthetic dimension. By introducing strong frequency-domain boundaries in an electro-optically modulated ring resonator, we realize finite non-Hermitian
lattices and use
laser detuning as a spectroscopic axis for the eigenenergies of the effective
Hamiltonian. Site-resolved heterodyne measurements then reconstruct the spatial profile of each mode, revealing boundary-localized skin states throughout the spectrum and their eigenenergy-dependent displacement from the edge. Beyond 1D, the same frequency-boundary architecture, upon incorporating long-range couplings between finite
lattices, produces genuine 2D frequency
lattices rather than the hitherto-realized folded 1D systems on twisted tubes. In these
lattices we observe tunable directional transport and edge localization in two synthetic dimensions. Our results introduce eigenmode spectroscopy as a direct probe of non-Hermitian physics and establish strongly bounded frequency
lattices as a flexible platform for
Hamiltonian engineering.
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Plasma Physics
Time Series Extrinsic Regression of Ion Cyclotron Emission Spectra Trained on Particle-In-Cell Simulations
Ethan Attwood, J.W.S. Cook and Peter Hill
Ion Cyclotron Emission (ICE) is a ubiquitous magnetised
plasma phenomenon previously detected on virtually all large
magnetic fusion devices and whose diagnostic potential for future power plants rests upon an accurate mapping of
plasma parameters to spectra. This work demonstrates that the inverse problem is solved by training Time-Series Extrinsic Regression (TSER) models on synthetic ICE spectra from oblique propagation angle sweeps of nonlinear fully kinetic 1D3V particle-in-
cell simulations of the magnetoacoustic cyclotron instability. Using
datasets from a systematically constructed scan over reactor-relevant ranges of background
magnetic field strength, density, and alpha-particle velocity pitch ($v_\parallel/|v|$) and concentration, we show that these bulk and fast ion parameters may be recovered from a JET ICE spectrum via TSER models with near real-time capability.
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Chemical Physics
Low-energy electron attachment to $\text{NO}_2$: absolute cross sections
Ana I. Lozano, Francisco Blanco, Juan C. Oller et al. (+2 more)
Resonances from electron attachment to $\text{NO}_2$ have been identified in the total electron scattering cross sections (TCS) measured with a magnetically confined electron transmission apparatus. The corresponding absolute electron attachment cross sections have been derived from the TCS values through a self-consistent scattering channel deconvolution process. The positions of some of these resonances are consistent with previous elastic scattering calculations and dissociative electron attachment experiments. However, both the observed positions and the magnitude of the present resonance cross sections are in contradiction with the available recommended TCS values thus suggesting that electron scattering cross section databases need to be updated. Keywords: electron scattering resonances, negative ion formation, electron attachment cross sections
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All authors Ana I. Lozano, Francisco Blanco, Juan C. Oller, Paulo Limão-Vieira, Gustavo García
Data Analysis and Statistics
A Unified Framework for Structured Flow Modeling: From Representation to Verification and Model Discovery
Diego Casadei
Many dynamical systems can be described in terms of structured flows combining source/sink behavior, cyclic dynamics, and
topology-constrained transport. These features arise across a wide range of physical, engineered, and data-driven systems. The objective of this work is to establish a unified perspective on such systems, to identify modeling approaches that balance expressivity, interpretability, computational complexity, and data requirements, and to investigate how highly expressive models can be used to uncover the dominant mechanisms underlying observed dynamics. Starting from the Helmholtz-Hodge decomposition of continuous vector fields, we review the recently proposed Graph Vector Field (GVF) framework and its discrete representation on simplicial complexes. We then introduce a hierarchy of alternative approaches, including parametric conditional models, linear graph dynamical systems, and reduced Hodge representations. Finally, we propose a verification and validation methodology based on
benchmark datasets from well-understood physical systems and on systematic model-reduction and ablation studies. The resulting family of structured-flow models within a common framework, ranging from low-dimensional parametric representations to full GVF formulations, supports a diagnostic methodology in which gradient, curl, harmonic, and topological contributions are systematically assessed through ablation studies. This process enables the identification of dominant mechanisms underlying the observed dynamics and guides the construction of simplified models tailored to the available data and operational constraints. By separating structural verification, behavioral verification, and domain-specific validation, the proposed approach provides a foundation for scalable and interpretable analysis of complex dynamical systems across multiple application domains.
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Accelerator Physics
Undulator Radiation from a Single Electron: A Temporal Double-Slit Experiment
Shaukat Khan, Yuya Asai, Zohair Usfoor et al. (+9 more)
Double-slit diffraction studies with
photons or massive particles rank among the most beautiful experiments in physics. In particular, measurements at very low intensities demonstrate the particle-wave duality and the coherent superposition of states very clearly. In this paper, low-intensity double-slit experiments in the time domain are presented measuring the spectral distribution of synchrotron light from a single relativistic electron in a storage ring. In two consecutive radiation sources (so-called undulators) with a
magnetic detour between them, electrons emit two temporally separated light pulses leading to a spectrum with interference fringes, very much like the angular distribution of light behind two spatially separated slits. Independent experiments at two synchrotron light sources (DELTA in Germany and UVSOR-III in Japan) directly demonstrate that the spectral distribution of accumulated synchrotron light from a single electron is essentially the same as the spectrum from a beam of many electrons. While the latter is usually explained as interference between electromagnetic waves from the two undulators, the single-electron experiments demonstrate that coherent
photon emission is delocalized over several meters and the accumulated spectral distribution exhibits a deterministic interference pattern at small wavelengths. The experiments presented here were conducted with near-ultraviolet light to avoid an elaborate in-
vacuum setup, but the very wide spectral range of synchrotron radiation, from infrared light to X-rays, enables access to regimes not available in
laser-based
quantum optics experiments.
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All authors Shaukat Khan, Yuya Asai, Zohair Usfoor, Tatsuo Kaneyasu, Carsten Mai, Hiroshi Miyauchi, Yasuaki Okano, Arjun Radha Krishnan, Wael Salah, Miho Shimada, Vivek Vijayan, Masahiro Katoh
Fluid Dynamics
Dynamic Evolution of Pore-scale Heterogeneity and Transport Conditions Control Mineral Dissolution Regimes
Jinlei Wang, Yongfei Yang, Martin J. Blunt et al. (+1 more)
Mineral dissolution in porous media is classically partitioned into static regimes within the Pe-Da plane, but this framework fails to capture the dissolution behavior of structurally complex rocks. Using three-dimensional micro-continuum simulations on micro-CT images of three rock samples spanning a wide range of pore-space heterogeneity, we track the joint
evolution of dissolution morphology, velocity distribution, and reaction rate. Our results reveal that initial flow heterogeneity controls accessibility of reactants, thereby controlling the dissolution regime,reshaping them as dynamic trajectories. Channeled dissolution emerges as a simultaneous reorganization of structure and flow, and the resulting permeability-porosity relationship cannot be captured by a single power-law. The effective power-law exponent increases with heterogeneity and changes over time, reaching a maximum of 9.8, 18.0, and 40.9 for the three samples. Consequently, the effective reaction rate falls one to three orders of magnitude below the uniform dissolution prediction, with the suppression scaling with flow heterogeneity due to mass transfer limitations in channeled dissolution.
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All authors Jinlei Wang, Yongfei Yang, Martin J. Blunt, Branko Bijeljic
Medical Physics · Cross-listed from cs.SD
SIREM: Speech-Informed MRI Reconstruction with Learned Sampling
Md Hasan, Nyvenn Castro, Daiqi Liu et al. (+6 more)
Real-time
magnetic resonance imaging (rtMRI) of
speech production enables non-invasive visualization of dynamic vocal-tract motion and is valuable for
speech science and clinical assessment. However, rtMRI is fundamentally constrained by trade-offs among spatial resolution, temporal resolution, and acquisition speed, often leading to undersampled k-space measurements and degraded reconstructions. We propose SIREM, a
speech-informed MRI reconstruction framework that uses synchronized
speech as a cross-modal prior. The central idea is that vocal-tract configurations during
speech are correlated with the produced acoustics, making part of the image content predictable from audio. SIREM models each frame as a fusion of an audio-driven component and an MRI-driven component through a spatial weighting map. The audio branch predicts articulator-related structure from
speech, while the MRI branch reconstructs complementary content from measured k-space data. We further introduce a learnable soft weighting profile over spiral arms, enabling a differentiable study of how k-space arm usage interacts with
speech-informed fusion. This yields a unified
multimodal formulation that combines audio-driven prediction, MRI reconstruction, and sampling adaptation. We evaluate SIREM on the USC
speech rtMRI
benchmark against standard baselines, including gridding, wavelet-based compressed sensing, and total variation. SIREM introduces a
speech-informed reconstruction paradigm that operates in a substantially higher-throughput regime than iterative methods while preserving anatomically plausible vocal-tract structure. These results establish an initial
benchmark for
multimodal speech-informed rtMRI reconstruction and highlight the potential of synchronized
speech as an auxiliary prior for fast reconstruction. The source code is available at https://github.com/mdhasanai/SIREM
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All authors Md Hasan, Nyvenn Castro, Daiqi Liu, Lukas Mulzer, Jana Hutter, Jonghye Woo, Moritz Zaiss, Andreas Maier, Paula A. Perez-Toro
Plasma Physics
3D CO-TALIF distribution above a micro cavity discharge: A systematic approach for plasma catalysis
Henrik van Impel, Oliver Krettek, David Steuer et al. (+3 more)
We investigate a micro cavity
plasma array (MCPA) reactor operated at atmospheric pressure, offering excellent diagnostic accessibility and flexible opportunities for catalyst integration. Dissociation processes, such as $\mathrm{CO}$ production from $\mathrm{CO}_{2}$ diluted in helium, are studied. The diagnostic setup, combining TALIF with an ICCD camera, enables three-dimensional spatially resolved measurements of $\mathrm{CO}$ number densities above the $\mathrm{CO}$-generating discharges. The measured distributions are compared to a basic three-dimensional
diffusion model, showing good agreement and revealing the dominant transport mechanisms. Flow variation studies indicate that the gas flow inside the reactor follows a laminar, Poiseuille-like profile, while the transport behavior is consistent with literature values for the
diffusion coefficient of $\mathrm{CO}$, further validating the model. The high estimated local dissociation within the MCPA discharges (about $40\%$) results in saturation of $\mathrm{CO}$ production under increasing voltage. Combined with complementary diagnostics developed for this discharge, including measurements of surface charges, electric fields, and atomic oxygen, this approach provides a suitable platform for systematic studies of
plasma catalyst interactions.
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All authors Henrik van Impel, Oliver Krettek, David Steuer, Volker Schulz-von der Gathen, Marc Böke, Judith Golda
Fluid Dynamics
A discrete Boltzmann model with state-dependent power-law relaxation time for nonequilibrium transport in compressible flows
Demei Li, Zhongyi He, Huilin Lai et al. (+3 more)
Thermodynamic nonequilibrium effects play a central role in momentum and energy transport in compressible flows. In conventional BGK kinetic models, the relaxation time $\tau$ is taken as a constant, which neglects the dependence of the relaxation process on local macroscopic states. To overcome this limitation, we develop a discrete Boltzmann model with a density- and temperature-dependent power-law relaxation time, termed DTRT-DBM, in which $\tau=\tau_0(\rho/\rho_0)^a(T/T_0)^b$. This formulation extends the discrete Boltzmann framework to flows with spatially varying nonequilibrium intensity. The model is validated by the Sod shock tube and by analytical solutions for viscous stress and heat flux, demonstrating accurate recovery of both macroscopic wave structures and nonequilibrium quantities across shock waves, rarefaction waves, and contact discontinuities. On this basis, phase diagrams of viscous stress and heat flux are constructed to examine how these quantities depend on the power-law exponents $a$ and $b$. The extrema of these quantities depend exponentially on the model parameters and exhibit regime-dependent behaviour. The roles of $a$ and $b$ are not symmetric: the nonequilibrium response is more sensitive to $a$ when density gradients dominate, but more sensitive to $b$ when temperature gradients dominate. Within the parameter range and flow configurations examined here, higher-order viscous stress increases the growth rate of the total viscous-stress extremum, whereas higher-order heat flux reduces the growth rate of the total heat-flux extremum. These results show that the proposed model can capture different higher-order nonequilibrium responses in compressible flows and provides a framework for the modelling and analysis of multiscale nonequilibrium processes.
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All authors Demei Li, Zhongyi He, Huilin Lai, Yanbiao Gan, Hailong Liu, Pengfei Lin
Plasma Physics
Localization of a quantum particle in a classical one-component plasma: fluctuation-induced random potential, localization length and mutual decoherence
Yury A. Budkov
We develop a microscopic theory of disorder-induced attenuation and mutual coherence degradation for a
quantum particle in a classical one-component
plasma. The random potential originates from
equilibrium thermal fluctuations of the ionic charge density within the random phase approximation. Its correlator retains an unscreened $1/r$ tail, leading to a Coulomb logarithm in the eikonal localization scale $\ell(k)$. In the weak-disorder regime $\ell(k) \propto k^2 / \ln(\kappa L)$, while in the strong-disorder limit $\ell \propto (\ln(\kappa L))^{-1/3}$. Building on the same disorder model, we evaluate the mutual coherence function (Cooperon) of an electron beam and derive a closed analytical expression for the phase structure function $D_\phi(\rho)$. At large transverse separations the coherence decays as a power law $\gamma(\rho)\sim \rho^{-\eta}$, with an exponent determined by the disorder strength. The transverse coherence length $\rho_c$ satisfies a scaling relation $\rho_c \sim \lambda_D \sqrt{\ell/L}$, linking the eikonal attenuation scale with the loss of
quantum coherence. Numerical estimates for aqueous electrolytes under transmission electron microscopy conditions are given. A relativistic extension confirms that the same scaling holds for relativistic beams, with the eikonal coupling given by $A_{ m rel}=1/(\hbar v)$ and approaching the finite high-energy limit $1/(\hbar c)$.
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Optics
The thin line for optical neural networks towards broad practical relevance
Anas Skalli and Daniel Brunner
Optical neural networks promise unmatched efficiency, bandwidth, and latency, critical benefits as demand for neural network hardware surges. However, their practical value for general-purpose acceleration or specialized applications must be proven under application-realistic conditions. We discuss recent insights and outline key research priorities.
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Physics and Society
Hypergraphx-data: a repository for higher-order network data
Quintino Francesco Lotito, Lorenzo Betti, Berné Nortier et al. (+2 more)
The availability of network
datasets advances research in network science, machine learning and related fields by enabling empirical analyses and their reproducibility,
algorithm development, model validation and benchmarking. Existing repositories, such as SNAP and Netzschleuder, have made traditional network
datasets widely accessible with metadata, metrics, and basic visualizations. However, they primarily focus on pairwise interactions, limiting data access to systems with many-body interactions. To address this gap, we created hypergraphx-data, a repository of real-world hypergraph
datasets for higher-order network analysis, spanning different domains from social networks to biology and finance, and supporting configurations such as weighted, directed, temporal, and multiplex hypergraphs. Each
dataset includes relational information and metadata, provided in an open JSON format and a binarized format for Hypergraphx. We provide a user-friendly interface to facilitate browsing, filtering, and accessing the
datasets, while also ensuring integrity and reproducibility through hash-based verification and data versioning. The repository is available at https://hgx-team.github.io/hypergraphx-data
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All authors Quintino Francesco Lotito, Lorenzo Betti, Berné Nortier, Alberto Montresor, Federico Battiston
Biological Physics · Cross-listed from cond-mat.soft
Amoeboid cell migration and shape dynamics driven by actin polymerization
Winfried Schmidt, Chaouqi Misbah and Alexander Farutin
Cell migration is fundamental to development, tissue organization, immune response, and disease progression. Amoeboid motility is distinguished by rapid motion and strongly fluctuating
cell shapes, reflecting the intrinsically nonlinear nature of active living matter far from
equilibrium. Here we introduce a minimal active-shell model of an amoeboid
cell that couples actin polymerization, cortical flows, and membrane deformation through nonlocal mechanical interactions. The model gives rise to a rich spectrum of emergent behaviors. A symmetric non-motile state can spontaneously break
symmetry and transition toward persistent directed migration driven solely by polymerization-induced retrograde flow, even in the absence of shape deformation. Increasing activity further triggers a cascade of dynamical states, including circular trajectories, oscillatory zigzag motion, and irregular chaotic-like migration with fluctuating protrusions and multi-lobed morphologies. Although these migratory modes are observed experimentally in distinct cellular contexts, our results show that they can emerge from the same underlying physical mechanism, providing a unified framework for amoeboid dynamics. Notably, contractile stresses induced by molecular motors are not required to generate spontaneous motility, polarity, or complex migration patterns. Our findings highlight how collective active processes at the cellular scale can self-organize into complex dynamical states, revealing generic principles of nonlinear behavior in living systems.
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Physics Education
Faculty Orientations Shape Adoption of AI in Research and Teaching
Timothy J. Atherton, Ian Descamps, Tova R. Holmes et al. (+4 more)
Despite the widespread availability of large
language models (LLMs) in higher education, instructors vary substantially in their adoption and use of these tools, and the reasons for this variation remain poorly understood. A mixed-methods survey of 90 STEM faculty in the Research Corporation for Science Advancement (RCSA) Cottrell community examined relationships between AI use, attitudes, institutional context, and instructional practice. Exploratory factor analysis identified a coherent construct, AI pedagogical orientation, that strongly predicted self-reported AI use across research, teaching, and other professional activities. Qualitative analysis indicated that this construct reflected differing views about the role AI should play in disciplinary thinking, learning, and expertise development, rather than simply positive or negative attitudes toward AI. Institutional initiatives, demographic variables, and information sources showed comparatively weak associations with AI use. The results suggest that existing technology-adoption models may not fully explain adoption in contexts where technologies interact directly with disciplinary
reasoning and knowledge production.
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All authors Timothy J. Atherton, Ian Descamps, Tova R. Holmes, Christina L. Vizcarra, Ning Sui, Max Webel, Jay J. Foley IV
Fluid Dynamics
Electrolyte flows under magnetic fields: Manning-like counterion condensation in one dimension
Yoav Tsori and Hannes Uecker
We present a theoretical framework for unidirectional electromagnetohydrodynamic flow of dilute electrolytes under perpendicular
magnetic fields. Starting from the Navier–Stokes equation coupled with the Poisson–Nernst–Planck formulation, we show that the problem admits a sequential decoupling: the Stokes equation is solved first to obtain the velocity profile, which defines a hydrodynamic potential entering the Nernst–Planck description of ions. This Lorentz-force-induced potential competes with electrostatic attraction and significantly alters ionic distributions. We analyze this mechanism in two canonical geometries. In planar Couette shear, it produces a Manning–Oosawa-like condensation transition in one dimension, a phenomenon absent in classical electrostatics. We derive an
eigenvalue equation predicting a sharp threshold between counterion enrichment and depletion at the charged wall. In cylindrical Taylor–Couette flow, the same effect shifts the classical Manning criterion by a
magnetic parameter, enabling tunable control of condensation. These findings extend Manning–Oosawa phenomenology to driven, non-
equilibrium systems and provide a basis for
magnetic manipulation of screening in electrolytes, with implications for microfluidics, electrochemical systems, and nonlinear boundary-value theory.
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General Physics
Synergetic capacity planning of private and public EV charging piles via city-scale multiobjective optimization
Yiwu Hao, Hong Yuan, Nan Zhou et al. (+1 more)
Rapid electric vehicle (EV) expansion necessitates optimized charging infrastructure to bridge the persistent gaps between vehicle growth and charger availability. This study develops a demand-driven framework for city-scale EV charging demand assessment and charging pile capacity planning. It employs a bottom-up estimation approach to quantify electricity demand and a Harris Hawks Optimization
algorithm to solve capacity planning challenges, capturing spatiotemporal demand variations across powertrain types and guiding allocation over 2022-2030 in Chongqing, China. The results show that (1) compared with June 2022, monthly EV electricity consumption tripled to 57.5 gigawatt-hours by the end of 2024, characterized by significant seasonal
volatility and a structural shift in which the combined share of plug-in hybrid electric vehicles and extended-range electric vehicles reached 57.6%, necessitating a transition toward technology-specific infrastructure planning; (2) historical evaluations reveal a marked spatial mismatch, with actual deployment heavily concentrated in the urban core while public charging capacity consistently lagging behind demand, whereas the proposed optimized configuration achieved a superior comprehensive performance score of 0.28, compared to 0.65 for actual deployment, in balancing service adequacy across the "Core-Suburban-Exurban" hierarchy; and (3) by 2030, Chongqing is projected to require approximately 1.8 million charging units to sustain a stable 9:1 private-to-public ratio, a synergetic strategy expects to significantly mitigate urban-rural service disparities and enhance overall system resilience and grid compatibility. Ultimately, this study provides a versatile, spatially explicit tool for policymakers to support sustainable and cost-effective EV infrastructure deployment aligned with long-term electrification targets.
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All authors Yiwu Hao, Hong Yuan, Nan Zhou, Minda Ma
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials
Chih-Yu Lee, Haotong Liang, Ryan Kim et al. (+4 more)
Autonomous labs enable the integration of automated experiment execution, data analysis and decision making. The main challenge remains the integration of diverse data streams from multiple instruments, where the data is often heterogeneous and unsynchronized. The standard learning process of undetermined synthesis-process-structure-property relationships (SPSPR) usually relies on post-experiment analysis after data is fully collected, not during live experiments, and decision making is carried out independently across characterization equipment. Here, we demonstrate the Multi-instrument Autonomous Discovery (MAD) framework – combining structural property mapping and functional property optimization simultaneously in a closed-loop manner. As an example, we applied MAD to phase change
memory (PCM) materials, and, in particular on the Mn-Sb-Te ternary, a previously unexplored materials system for PCM. A multi-output model is employed to merge data from x-ray diffraction (XRD) and electrical resistance measurements simultaneously through a co-regionalization kernel that models the relationship between them. The output probabilistic posterior and uncertainty quantification facilitate decision making with shared knowledge, while the goals are different across tasks. We aimed to maximize the knowledge of
crystal structure distribution using non-negative
matrix factorization (NMF), while in parallel, we find the composition with the maximum resistance value, an important figure of merit for PCM. Leveraging MAD, we found promising electrical PCMs and identified the SPSPR within 25 closed-loop iterations, corresponding to a seven-fold speed-up. The framework opens a new path of study in large-scale autonomous facilities, where future experiments can be run in parallel together, not independently.
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All authors Chih-Yu Lee, Haotong Liang, Ryan Kim, Austin McDannald, Carlos A Rios Ocampo, A. Gilad Kusne, Ichiro Takeuchi
Optics
Optoelectronic Chromatic Dispersion in a Single Photodiode for Machine-Learning-Based Computational Spectroscopy
Endalamaw Ewnu Kassa, Ziv Glasser, Uttama K. Saint et al. (+2 more)
Spectroscopy requires high-precision wavelength discrimination but typically requires bulky,
alignment-sensitive instrumentation. To address this, we present a compact computational spectrometer built from a single germanium PN photodiode. The system exploits optoelectronic chromatic dispersion (OED), a phenomenon whereby wavelength-dependent absorption depth produces carrier
diffusion delays that encode spectral information as measurable RF amplitude and phase signatures in the photodiode output. We extract DC voltage, RF amplitude, and RF phase across 15 modulation frequencies (0.1-1.5 MHz), forming a 31-dimensional feature vector per optical input. Spectral reconstruction was formulated as a high-dimensional inverse problem and solved using five machine learning models, utilizing group-wavelength splitting and k-fold cross-validation to prevent spectral leakage and ensure unbiased evaluation. Across the C- and L-bands, single-wavelength reconstruction using Gaussian Process Regression (GPR) achieves an accuracy of 0.178 nm on a wavelength-grouped, held-out test set spanning seven optical power levels. Five-fold cross-validation yields a robust Root Mean Square Error (RMSE) of (0.342 +/- 0.117) nm, confirming excellent generalization under wavelength and power variations. For dual-wavelength inputs, GPR yields accuracies of 0.362 nm for the swept wavelength and 0.434 nm for the fixed wavelength. This is the first spectral reconstruction method exploiting a multi-frequency OED feature space from a single photodiode. By merging the physics of OED with data-driven learning, this work enables
alignment-free, on-chip-compatible spectrometers suitable for portable optical sensing, smartphone-integrated diagnostics, and field-deployable environmental monitoring.
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All authors Endalamaw Ewnu Kassa, Ziv Glasser, Uttama K. Saint, Roi Yozevitch, Shmuel Sternklar
Optics · Cross-listed from eess.SP
A Computationally Efficient Reciprocal Effective Roughness Model for Diffuse Scattering
Giacomo Melloni, Enrico M. Vitucci, Vittorio Degli Esposti et al. (+4 more)
Ray-tracing (RT) has become central to site-specific electromagnetic propagation modeling in dynamic complex environments. Yet its computational burden grows sharply as high-fidelity digital twins of these environments scale to millions of facets whose material parameters must be continuously updated as the environment changes. The challenge is amplified at mmWave and sub-THz frequencies, where surface roughness becomes comparable to the wavelength and so diffuse scattering can account for up to 40% of the received power, making accurate yet tractable models essential. The popular Effective Roughness (ER) approach offers physical consistency but become increasingly costly when highly directive lobes are required or when parameters must be iteratively tuned. This communication introduces a directive, reciprocal diffuse scattering model that preserves the structure of the ER while enabling an order-of-magnitude reduction in computational cost. Validation across eight materials shows no loss in accuracy - and a slight improvement - demonstrating a scalable and physically meaningful solution for RT in scenarios where diffuse scattering is non-negligible.
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All authors Giacomo Melloni, Enrico M. Vitucci, Vittorio Degli Esposti, Samuel Berweger, Jack Chuang, Camillo Gentile, Nada Golmie
Fluid Dynamics · Cross-listed from cond-mat.soft
Lateral hydrodynamics in supported membranes: The Evans-Sackmann model and its extensions
Yuto Hosaka, David Andelman and Shigeyuki Komura
We review the theoretical development and modern applications of the Evans-Sackmann hydrodynamic model for lateral transport in supported fluid membranes. We first cover the original formulation, emphasizing the linear momentum decay term that captures membrane-substrate coupling mediated by a thin lubricating fluid layer. This coupling term enables quantitative interpretation of tracer
diffusion measurements in supported
bilayers. Building on this foundation, we survey theoretical extensions that relax standard boundary conditions at the inclusion perimeter, where inclusions refer to embedded objects such as
proteins, lipid domains, or tracer particles within the membrane. We discuss the drag of a disk and a
liquid domain, as well as the dynamics of membrane phase separation. We further highlight how the supported-membrane mobility tensor serves as a unifying tool for systematic treatments of correlated
diffusion,
polymer dynamics, phase separation kinetics, and many-body interactions in quasi-two-dimensional environments. Finally, we discuss recent extensions to active and
chiral membranes, where odd viscosity provides a transverse hydrodynamic response and offers a possible route for detecting chirality in two-dimensional fluids.
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Fluid Dynamics
Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis
Bo Chen, Yitong Fan, Jie Yao et al. (+1 more)
Long-horizon prediction of three-dimensional (3D) wall-bounded
turbulence with machine-learning methods remains a challenging task, due to the rapid accumulation of autoregressive errors and the substantially computational cost. To address these challenges, we present a hybrid machine-learning framework, in which a channel-time-
attention Swin-UNet (CTA-Swin-UNet) and a multi-time-scale fusion correction (MTFC) strategy are developed to predict the turbulent flow fields in a wall-parallel plane, with affordable computational cost. Then, 3D flow fields are reconstructed via a resolvent-based spectral linear
stochastic estimation (SLSE), rooting from the predicted planar flow. Results show that the CTA-Swin-UNet outperforms the baseline models (LSTM, FNO and traditional Swin-UNet) in both single-step prediction and autoregressive rollouts, indicating the effectiveness of introducing the CTA module into the Swin-UNet architecture. At the same temporal interval, the CTA-Swin-UNet remains stable for approximately 150 rollout steps, while the baseline models fail within 20 to 50 rollout steps. After introducing the MTFC strategy, a longer horizon upto 300 steps is achieved. Using the resolvent-based SLSE reconstruction further recovers the 3D flow structures and energy spectral distributions from the predicted planar inputs, which demonstrates that the proposed framework provides an effective and computationally efficient approach for long-horizon autoregressive prediction of 3D wall-bounded
turbulence.
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All authors Bo Chen, Yitong Fan, Jie Yao, Weipeng Li
Optics
Amplification of Weak Forces via Parametric Interactions and Non-Markovian Effects in Cavity Optomechanics
Y. F. Li, Ze Wang, W. Y. Hu et al. (+3 more)
Weak force amplification describes the process of amplifying a faint low-frequency signal by means of an additional high-frequency modulation, which plays a vital role in
quantum sensing and high-precision measurement. However, the potential enhancement of weak-force amplification in non-Markovian environments has received little
attention. In this paper, we firstly study the amplification of weak forces within cavity-optomechanical systems incorporating a degenerate optical parametric amplifier (DOPA) under the Markovian assumption. The results show that the weak force can be effectively amplified by using two high-frequency signals via vibrational resonance through adjusting the strength and phase of the DOPA with different pumping frequencies. Moreover, we extend the study of the amplification of the weak force to the non-Markovian environment which consists of a collection of infinite oscillators. We illustrate that the amplification exhibits a conversion from the non-Markovian regime to Markovian regime by controlling environmental spectral width. This conversion leads to enhancements of amplification, which originates from the excitation backflow obtained through the interaction between the cavity and non-Markovian environment. By controlling DOPA to amplify weak forces, the study achieves amplification in the non-Markovian regime, offering new directions for
quantum optics research.
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All authors Y. F. Li, Ze Wang, W. Y. Hu, Yan-Hui Zhou, Cheng Shang, H. Z. Shen
Atomic Physics
Photon-Atom Granularity Noise Thermometry
Chen-Rong Liu, Yixuan Wang, Xiaowei Wang et al. (+5 more)
We propose granularity noise thermometry (GNT), a fluctuation-based optical thermometry scheme that exploits the intrinsic fluctuations of susceptibility arising from atomic discreteness. The power spectral density of transmitted light exhibits an excess noise above the shot-noise limit that scales linearly with the
photon-to-atom ratio $\mathcal{R}$. Consequently, varying the incident power (hence $\mathcal{R}$) yields the slope $\mathcal{K}$ of this linear scaling, which directly encodes the temperature. Closed-form expressions for the polarizability moments are derived via the
plasma dispersion function, which yield distinct temperature scalings: $\mathcal{K}\propto P_{\mathrm{v}}(T)/T^2$ for thermal vapors and $\mathcal{K}\propto T^{2}$ for cold atoms. While practical implementation requires careful control of technical noise and system parameters, the present framework provides a noise-based pathway for optical thermometry using atomic ensembles.
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All authors Chen-Rong Liu, Yixuan Wang, Xiaowei Wang, Chuang Li, Mingti Zhou, Runxia Tao, Hongwei Chen, Ying Dong
Geophysics · Cross-listed from cs.LG
HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL
Zhi Li, Songkun Yan, Jie Cao et al. (+4 more)
Calibrating distributed hydrologic models is a critical bottleneck across operational water resources management - streamflow prediction, reservoir operation, drought monitoring, infrastructure design, and flood
forecasting all depend on it. Each basin demands an expert to translate hydrograph signatures into adjustments of a high-dimensional parameter vector, and the resulting workflow does not transfer between watersheds. We ask: can frontier large
language model (LLM)
agents replace the human hydrologic modeler, and if not, what would it take? We
benchmark nine frontier LLM
agents - Claude Opus 4.6/4.7, Sonnet 4.6, GPT-5/5.4/5.4-pro, and Gemini 2.5-pro/3.1-pro/3-flash - on the operational CREST distributed hydrologic model used by the U.S. National Weather Service for flash-flood
forecasting. Best-of-twenty-rounds Nash-Sutcliffe Efficiency (NSE) across four held-out gauges spanning 329-40,792 km2 ranges from -0.16 (GPT-5.4) to 0.75 (Sonnet 4.6); the ceiling reproduces across all three vendors and capability tiers, with the strongest models concentrating in the 0.65-0.75 band, and no model reaches the human-expert reference except Opus-4.7 on one gauge. We argue this gap is not a parameter-count problem but a domain-grounding problem. We then propose HYDROAGENT, fine-tuning open-weight Qwen3-4B with supervised fine-tuning on 2,576 expert calibration trajectories and Group-Relative Policy Optimization using NSE as a verifiable reward from online CREST simulations - reinforcement learning with simulation feedback (RLSF). For Earth system science, a small domain-tuned policy with simulator-in-the-loop RL is a more compute-efficient and physically faithful path than scaling generic frontier models, and the multi-modal richness of Earth data - remote sensing, in-situ time series, and forecaster narrative - makes domain
agents a leveraged direction for AI in physical science.
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All authors Zhi Li, Songkun Yan, Jie Cao, Mofan Zhang, Anjiang Wei, Jinwoong Yoo, Yang Hong