Fluid Dynamics
Inclination-Driven Thin-Film Hydrodynamics: Universal Trajectory in the {Da, Pe, Bo} Space
Helena Cristina Vasconcelos, Maria Meirelles and Reşit Özmenteş
We develop a unified theoretical framework for thin-film hydrodynamics on inclined solid substrates, integrating capillarity, intermolecular forces, gravitational
symmetry breaking, confined transport and
stochastic wetting into a single formulation. Starting from lubrication theory with capillary
curvature and disjoining-pressure interactions, we derive a general thin-film equation that incorporates inclination-driven advection, nanoscale stabilization and humidity-controlled source-sink fluxes. A dimensionless analysis shows that, within the long-wave lubrication approximation, inclination induces a leading-order coupling of the Bond, Peclet and Damkohler numbers. This coupling defines a characteristic trajectory in the parameter space (Da, Pe, Bo), determined by the structure of the lubrication flux. Coupling this deterministic framework to a minimal
stochastic formulation captures the intermittent wet-dry dynamics characteristic of ultrathin films under environmental forcing. The framework provides a general surface-physics description of confined films under
geometric asymmetry, applicable across wetting, interfacial drainage, reactive confinement and soft-matter systems.
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Optics
Hybrid electrostatic-piezo MEMS photonic integrated modulators
Thuy-Linh Le, Hardit Singh, Julia M. Boyle et al. (+5 more)
Programmable photonic integrated
circuits (PICs) have recently emerged as an important technology for
quantum information science and artificial neural networks. In particular, PICs with MEMS-based modulators have the advantages of voltage-based control, ultra-low-energy consumption, cryogenic compatibility, and CMOS-foundry support. Here we report a cantilever optical modulator that utilizes hybrid piezoelectric and electrostatic tuning forces together on a monolithic silicon nitride (SiN) PIC platform. The device achieves actuation of visible-wavelength light with quasi-static tuning up to 10 kHz at 1.5 V{\pi}-cm as well as high-speed (>20 MHz) AC modulation with dynamically adjustable (25 - 40 MHz) mechanical resonances. We report the physics of how
geometric nonlinearities such as capacitive pull-in give rise to suspended and contacted cantilever modes. These reversible operating regimes generate different strain profiles and boundary conditions which are responsible for the active tuning of the mechanical resonances. Our proof-of-concept electrostatic-piezo modulator shows promising potential in large-scale programmable PICs applied to high-speed optical switching and optomechanical sensing.
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All authors Thuy-Linh Le, Hardit Singh, Julia M. Boyle, Matthew Zimmermann, Andrew J. Leenheer, Daniel Dominguez, Matt Eichenfield, Mark Dong
Optics
JPEG-Inspired Cloud-Edge Holography
Shuyang Xie, Jie Zhou, Jun Wang et al. (+1 more)
Computer-generated holography (CGH) presents a transformative solution for near-eye displays in augmented and virtual reality. Recent advances in deep learning have greatly improved CGH in reconstructed quality and computational efficiency. However, deploying neural CGH pipelines directly on compact, eyeglass-style devices is hindered by stringent constraints on computation and energy consumption, while cloud offloading followed by transmission with natural image codecs often distorts phase information and requires high bandwidth to maintain reconstruction quality. Neural compression methods can reduce bandwidth but impose heavy neural decoders at the edge, increasing inference latency and hardware demand. In this work, we introduce JPEG-Inspired Cloud-Edge Holography, an efficient pipeline designed around a learnable transform codec that retains the block-structured and hardware-friendly nature of JPEG. Our system shifts all heavy neural processing to the cloud, while the edge device performs only lightweight decoding without any neural inference. To further improve throughput, we implement custom CUDA kernels for
entropy coding on both cloud and edge. This design achieves a peak signal-to-noise ratio of 32.15 dB at $<$ 2 bits per pixel with decode latency as low as 4.2 ms. Both numerical simulations and optical experiments confirm the high reconstruction quality of the holograms. By aligning CGH with a codec that preserves JPEG's structural efficiency while extending it with learnable components, our framework enables low-latency, bandwidth-efficient hologram streaming on resource-constrained wearable devices-using only simple block-based decoding readily supported by modern system-on-chips, without requiring neural decoders or specialized hardware.
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All authors Shuyang Xie, Jie Zhou, Jun Wang, Renjing Xu
Atomic Physics · Cross-listed from cond-mat.quant-gas
Interaction-assisted topological pumping in few- and many-atom Rydberg arrays
Chenxi Huang, Tao Chen, Qian Liang et al. (+7 more)
Topology can imbue
lattice systems with special properties, notably the presence of robust eigenstates living at their boundary. Through dimensional reduction, the robust bulk band
topology of, e.g., the integer
quantum Hall system can be mapped onto similarly robust charge-pumping dynamics of a topological pump living in one lower dimension. Recent studies have uncovered a rich influence of interactions on the dynamics of topological pumps in nonlinear systems, including the robust pumping of self-bound
solitons. These striking observations in classical nonlinear photonics have raised a number of questions, chiefly if and how this phenomenology persists in strongly correlated
quantum systems and in the few-body limit. Here, using few- and many-atom arrays, we explore how dipolar interactions impact the dynamics of topological population pumping along a Rydberg synthetic dimension. In the few-body limit, we find that dipolar interactions lead to self-bound states that are efficiently pumped along the synthetic dimension, described by an emergent pair-state topological pump. We find that this interaction-assisted pumping persists in many-atom arrays, with a sharpened dependence on the dipolar interaction strength that stems from the enhanced spatial connectivity. These Rydberg-based studies on interaction-assisted topological pumping help connect observations from classical nonlinear photonics to the few-body
quantum limit and pave the way for studies of new strongly correlated
quantum pumping phenomena.
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All authors Chenxi Huang, Tao Chen, Qian Liang, Matthew A. Krebs, Ethan Springhorn, Ruiyu Li, Mingsheng Tian, Kaden R. A. Hazzard, Jacob P. Covey, Bryce Gadway
Applied Physics
Single-Antenna Non-Line-of-Sight Matrix Imaging via Reconfigurable Intelligent Surfaces
Antton Goïcoechea, François Sarrazin, Theodosios Karamanos et al. (+3 more)
Modern imaging and sensing in complex environments, ranging from biomedical diagnostics to wireless communication, relies on accurately measuring and then controlling the wave propagation. Conventional approaches demand large arrays of
antennas or transducers to reconstruct the full reflection or transmission
matrix, enabling advanced protocols such as selective focusing or adaptive wave control. Yet, these arrays are expensive, bulky, and difficult to implement at microwave frequencies. Here, we show that a single transmitting-receiving
antenna, when combined with a reconfigurable intelligent surface (RIS), can fully reconstruct the reflection
matrix from far-field measurements, effectively transforming the RIS into a programmable synthetic
antenna array. This approach allows high-fidelity imaging of complex scenes, selective focusing through clutter, and real-time tracking of moving targets. Our results establish RIS as a versatile, low-cost platform for
matrix-based imaging, with broad implications for adaptive wave control, real-time sensing, and imaging in environments previously considered inaccessible.
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All authors Antton Goïcoechea, François Sarrazin, Theodosios Karamanos, Mathias Fink, Fabrice Lemoult, Matthieu Davy
Physics and Society
Understanding Main Path Analysis
H.C.W. Price and T.S. Evans
Main path analysis has long been used to trace knowledge trajectories in citation networks, yet it lacks solid theoretical foundations. To understand when and why this approach succeeds, we analyse directed acyclic graphs created from two types of artificial models and by looking at over twenty networks derived from real data. We show that
entropy-based variants of main path analysis optimise
geometric distance measures, providing its first information-theoretic and
geometric basis. Numerical results demonstrate that existing
algorithms converge on near-geodesic solutions. We also show that an approach based on longest paths produces similar results, is equally well motivated yet is much simpler to implement. However, the traditional single-path focus is unnecessarily restrictive, as many near-optimal paths highlight different key nodes. We introduce an approach using “baskets” of nodes where we select a fraction of nodes with the smallest values of a measure we call “generalised criticality”. Analysis of large vaccine citation networks shows that these baskets achieve comprehensive algorithmic coverage, offering a robust, simple, and computationally efficient way to identify core knowledge structures. In practice, we find that those nodes with zero unit criticality capture the information in main paths in almost all cases and capture a wider range of key nodes without unnecessarily increasing the number of nodes considered. We find no advantage in using the traditional main path methods.
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Biological Physics · Cross-listed from quant-ph
Quantum Coherence Dispersion
Fernando Parisio
We investigate how
quantum coherence can be distributed among the several off-diagonal elements of an arbitrary density
matrix. An easily computable quantity that captures this variability notion is proposed and it is argued that it presents marked features of complexity quantifiers. It turns out that this coherence dispersion ($\Delta_{\rm c}$) is maximized for intermediate values of an appropriate
entropy (the relative
entropy of coherence), a prevalent signature of complexity quantifiers across different fields, from evolutionary biology to linguistics and information science. The final part of the manuscript is dedicated to the connection between the proposed framework and non-
equilibrium systems in the
quantum regime.
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Optics
Absorption and scattering of light by metal-dielectric nanoeggs
R. Yu. Korolkov, R. O. Malysh, A. V. Korotun et al. (+1 more)
The optical and plasmonic properties of metal-dielectric nanoeggs were investigated in this study. Frequency dependencies of polarizability, absorption and scattering cross-sections, and radiation efficiency were determined. Expressions describing the size-dependent behavior of surface plasmon resonance frequencies were derived. The causes of blue and red shifts in the maxima of polarizability, absorption, and scattering cross-sections as well as variations in their number and amplitude were identified. Recommendations were proposed regarding the use of materials with maximum radiation efficiency in different spectral ranges.
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All authors R. Yu. Korolkov, R. O. Malysh, A. V. Korotun, R. A. Kulykovskyi
Fluid Dynamics
Experimental and Numerical Analysis of the Intermittentency in a Nozzle Overexpanded-Flow
Emanuele Martelli, Vincent Jaunet, Giacomo Della Posta et al. (+1 more)
The present work reports an investigation into the statistical properties of wall-pressure fluctuations in a highly over-expanded nozzle flow, characterized by significant shock-induced flow separation. This regime is extremely hazardous to rocket nozzles, as it leads to very high off-axis loads. The database under investigation has been obtained both experimentally and numerically by means of a hybrid RANS/LES simulation of the flow issuing from a sub-scale Truncated Ideal Contour (TIC) nozzle, fed with cold air and operating at a Reynolds number on the order of 10^6. The experimental campaign was conducted in the S150 supersonic wind tunnel at the Institut PPRIME in Poitiers. The degree of over-expansion is quantified by the nozzle pressure ratio (NPR). Pressure fluctuations are extracted from several probes positioned along the nozzle wall, considering different NPR values. The intermittent behavior is investigated using conditional statistics based on the wavelet transform, which demonstrates that the aerodynamic loads of the over-expanded jet consist of intermittent bursts rather than continuous variations. The wavelet analysis reveals scale-by-scale intermittency and, in particular, shows that the wall-pressure signals exhibit a significant degree of intermittency around the frequency associated with aerodynamic side-loads. The statistics of these intermittent events, in terms of the time delay between occurrences and in terms of their amplitude, are found to be weakly sensitive to NPRs and to locations along the nozzle wall and appear to follow a universal behaviour that can be modelled by a log-normal distribution. This finding may support the development of a
stochastic model of the aerodynamic side-loads.
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All authors Emanuele Martelli, Vincent Jaunet, Giacomo Della Posta, Matteo Bernardini
Physics Education
Exploring Fourier methods with beer bottles
David Kordahl and Emma Foster
As anyone who has blown across the mouth of a beer bottle knows, beer bottles have a well-defined fundamental frequency. This paper shows how a beer bottle's acoustical resonance can be modeled as a one-dimensional driven-damped oscillator and includes enough detail to be useful in undergraduate laboratory experiments. While the frequency-domain Green's function of the bottle can be extracted through sequential pure-tone measurements, sufficient data to fit the model's parameters can be collected in just a few seconds when
Fourier methods are used.
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Applied Physics
Bio-integrated ${\mu}$Bots with Overtone Ultra-Wideband Magnetoelectric Antennas for Wireless Telemetry
Mahdieh Shojaei Baghini, Adam Armada-Moreira, Alessio Di Clemente et al. (+20 more)
Implantable and wearable devices require
antennas that are both miniaturized and efficient, yet conventional designs are constrained by narrow bandwidth and orientation sensitivity. We report overtone ultra-wideband magnetoelectric (OUWB-ME)
antennas that exploit higher order acoustic modes in polished silicon substrates to achieve a 22.6 GHz bandwidth in the 3-4 GHz range. Packaged into "${\mu}$Bots," these magnetoelectric
heterostructures bonded with silver nanoparticle inks maintain stable operation under biological loading. In vitro assays confirm the biocompatibility of AlN and the protective role of parylene encapsulation for FeGa. Ex vivo rat and human tissues reshape transmission spectra, identifying reproducible frequency windows near 3.3 and 3.9 GHz. ${\mu}$Bots enable real-time audiovisual telemetry using software-defined
radios and exhibit compatibility with 7T MRI. By combining wideband response, robustness to misalignment, and biocompatible packaging, OUWB-ME ${\mu}$Bots provide a scalable platform for wireless bio-integrated communication and telemetry.
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All authors Mahdieh Shojaei Baghini, Adam Armada-Moreira, Alessio Di Clemente, Dibyajyoti Mukherjee, Afesomeh Ofiare, Jonathon Harwell, Mary Dysko, Luana Benetti, Declan Bolster, Laura Mazon Maldonado, Dayhim Nekoeian, Moreno Maini, Mostafa Elsayed, Rossana Cecchi, Ricardo Ferreira, Jeff Kettle, Sandy Cochran, William Holmes, Carlos Garcia Nunez, Luca Selmi, Nicola Toschi, Michele Giugliano, Hadi Heidari
Physics and Society
Optimising for the long game: methodological challenges in energy system optimisation pathways
Ivan Ruiz Manuel, Meijun Chen, Francesco Lombardi et al. (+1 more)
Pathways that describe the optimal
evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar
climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little
attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.
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All authors Ivan Ruiz Manuel, Meijun Chen, Francesco Lombardi, Stefan Pfenninger-Lee
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Balancing Accuracy and Speed: A Multi-Fidelity Ensemble Kalman Filter with a Machine Learning Surrogate Model
Jeffrey van der Voort, Martin Verlaan and Hanne Kekkonen
Currently, more and more machine learning (ML) surrogates are being developed for computationally expensive physical models. In this work we investigate the use of a Multi-Fidelity Ensemble Kalman Filter (MF-EnKF) in which the low-fidelity model is such a machine learning surrogate model, instead of a traditional low-resolution or reduced-order model. The idea behind this is to use an ensemble of a few expensive full model runs, together with an ensemble of many cheap but less accurate ML model runs. In this way we hope to reach increased accuracy within the same computational budget. We investigate the performance by testing the approach on two common test problems, namely the Lorenz-2005 model and the Quasi-Geostrophic model. By keeping the original physical model in place, we obtain a higher accuracy than when we completely replace it by the ML model. Furthermore, the MF-EnKF reaches improved accuracy within the same computational budget. The ML surrogate has similar or improved accuracy compared to the low-resolution one, but it can provide a larger speed-up. Our method contributes to increasing the effective ensemble size in the EnKF, which improves the estimation of the initial condition and hence accuracy of the predictions in fields such as meteorology and oceanography.
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Atomic Physics
Coulomb crystallization of xenon highly charged ions in a laser-cooled Ca+ matrix
Leonid Prokhorov, Aaron A. Smith, Mingyao Xu et al. (+17 more)
We report on the sympathetic cooling and Coulomb crystallization of xenon highly charged ions (HCIs) with
laser-cooled Ca$^+$ ions. The HCIs are produced in a compact electron beam ion trap, then charge selected, decelerated, and finally injected into a cryogenic linear Paul trap. There, they are captured into $^{40}$Ca$^+$ Coulomb
crystals, and co-crystallized within them, causing dark voids in their fluorescence images. Fine control over the number of trapped ions and HCIs allows us to realize mixed-species
crystals with arbitrary ordering patterns. By investigating Xe$^{q+}$–Ca$^+$ strings, we confirm the HCI charge states, measure their lifetime and characterize the mixed-species motional modes. Our system effectively combines the established
quantum control toolbox for Ca$^+$ with the rich set of atomic properties of Xe highly charged ions, providing a resourceful platform for optical frequency metrology, searches for signatures of new physics, and
quantum information science.
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All authors Leonid Prokhorov, Aaron A. Smith, Mingyao Xu, Kostas Georgiou, Vera Guarrera, Lakshmi P. Kozhiparambil Sajith, Elwin A. Dijck, Christian Warnecke, Malte Wehrheim, Alexander Wilzewski, Laura Blackburn, Matthias Keller, Vincent Boyer, Thomas Pfeifer, Ullrich Schwanke, Cigdem Issever, Steven Worm, Piet O. Schmidt, José R. Crespo Lopez-Urrutia, Giovanni Barontini
Optics
Cavity Ring-down Spectroscopy with behavior of Hybrid Cavity Structures
Muhammad Junaid Khan and Rida Batool Sheraliyat
Cavity ring-down (CRD) spectroscopy represents a direct absorption technique of sample absorption measurement. Instead of measuring the amount of the absorbed light, this technique determines the rate at which light intensity decays inside an optical cavity. When using a pulsed or continuous-wave light source, CRD spectroscopy offers considerably higher sensitivity as compared with conventional spectroscopy of absorption, making even very weak absorptions easy to detect.
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Fluid Dynamics
A Computational Approach for Multi-Body Potential-Flow Interaction Effects Using Matrix-Free FEM and Body-Conforming Grids
Anil Lal S and Mannu Yadav
This paper presents a unified and computationally efficient framework for predicting incompressible, irrotational (potential) flow around multiple immersed bodies in two-dimensional domains, with particular emphasis on quantifying irrotational interaction effects in multi-body configurations. The methodology integrates three components: a fast body-conforming mesh-generation strategy, a
matrix-free finite-element solution of the Laplace equation, and a systematic procedure for determining the stream-function values associated with each immersed solid. Body-fitted grids are generated by imposing boundary displacements on a Cartesian background mesh followed by
Laplacian smoothing, yielding simple, robust, and accurate meshes for domains containing multiple immersed bodies. The potential-flow field is obtained by solving the Laplace equation using a
matrix-free Conjugate Gradient method, wherein element-level operators are evaluated without assembling global stiffness
matrices. Immersed bodies are treated as constant-\,$\psi$ streamlines, and their unknown stream-function values are determined through multi-point constraints that naturally capture inter-body flow connectivity. The results highlight the ability of the proposed approach to resolve subtle multi-body interaction phenomena with minimal case-setup effort and very low
memory requirements, while providing a quantitative measure of potential-flow interference in complex immersed-body systems.
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Physics and Society · Cross-listed from astro-ph.IM
Bibliometric benchmarking across astronomy journals: Knowledge-use cycle and PASJ in the global landscape
Hideaki Fujiwara
We present a comparative bibliometric analysis of eight astronomy journals over 1996–2024, including Publications of the Astronomical Society of Japan (PASJ). Using data from Scopus and SciVal, we extract annual indicators of publication activity and scholarly impact, analyze time series, citation distributions, and citation age profiles, and
benchmark PASJ within this landscape. The age profiles reveal a characteristic knowledge-use cycle: citations rise over $\sim$2–4 years, approach saturation by $\sim$10–12 years, underscoring limits of short-window impact metrics. Journals published by European and North American astronomical organizations sustain higher impact, whereas PASJ generally lies below the world baseline. In parallel, PASJ shows episodic above-baseline impact through facility- or mission-driven special issues and features that, given the journal's modest annual volume, can materially shift year-level metrics. These patterns point to two potential avenues for PASJ: well-timed, thoughtfully organized special issues and features that highlight high-impact results, and continued strengthening of international collaboration.
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Fluid Dynamics
Shear Stress and Fluid-Wall Interaction Force in LBM Simulations of Hydrodynamic and MHD Flows
Jun Li
Chapman-Enskog analysis of the
lattice Boltzmann method (LBM) is adopted to recover the Navier-Stokes (N-S) equation for the magnetohydrodynamic (MHD) flows driven by external body forces other than the induced Lorentz force. Various numerical schemes are discussed for the implementation of external body forces, leading to different artefact terms. An order-of-magnitude analysis is provided to demonstrate that the artefact terms are negligible in calculating the shear stress and strain rate tensors, which keeps the study of fluid-wall interaction and the implementation of large eddy simulation (LES) simple. This clarifies the confusion in the literature, where both considering and not considering the artefact terms have been adopted without discussion. Additionally, the fluid-wall interaction force can be computed as the intuitive momentum exchange rate using a special distribution function after a half-step propagation, and consistency is obtained with the formula derived using the Chapman-Enskog analysis. The momentum flux tensor for moving boundaries is uncovered as the cause of violating Galilean invariance and the Maxwell stress tensor for Lorentz force is also contained in this intuitively calculated interaction force for MHD flows due to the modification of
equilibrium distribution function. Both extra terms should be removed for an interaction force associated with the hydrodynamic pressure and viscous shear stress. The implication of extra terms for the global balance of different physical forces is discussed.
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Atomic Physics
Barium Autoionization for Efficient Ion Trap Loading
Zachary J. Wall, Justin D. Piel, Samuel R. Vizvary et al. (+7 more)
We report a theoretical and experimental investigation of autoionizing resonances from the $5d6p\,{}^3\mathrm{D}_1^o$
manifold in neutral barium for efficient loading of ion traps. Our calculations predict large resonant cross sections for many narrow autoionizing resonances, but we find experimentally that for most of these, Doppler broadening during trap loading depresses the effective cross sections that can be achieved in practice. We identify and demonstrate a strong, broad transition at $531\,\mathrm{nm}$, and show that it furnishes an order-of-magnitude increase in trap loading efficiency compared to other demonstrated resonances.
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All authors Zachary J. Wall, Justin D. Piel, Samuel R. Vizvary, Michael Bareian, Steven Diaz, Elijah Mossman, Anthony Ransford, Chris H. Greene, Eric R. Hudson, Wesley C. Campbell
Physics and Society · Cross-listed from cs.CY
The Ideological Turing Test for Moderation of Outgroup Affective Animosity
David Gamba, Daniel M. Romero and Grant Schoenebeck
Rising animosity toward ideological opponents poses critical societal challenges. We introduce and test the Ideological Turing Test, a gamified framework requiring participants to adopt and defend opposing viewpoints, to reduce affective animosity and affective polarization. We conducted a mixed-design experiment ($N = 203$) with four conditions: modality (debate/writing) x perspective-taking (Own/Opposite side). Participants engaged in structured interactions defending assigned positions, with outcomes judged by peers. We measured changes in affective animosity and ideological position immediately post-intervention and at 2-6 week follow-up. Perspective-taking reduced out-group animosity and ideological polarization. However, effects differed by modality (writing vs. debate) and over time. For affective animosity, writing from the opposite perspective yielded the largest immediate reduction ($\Delta=+0.45$ SD), but the effect was not detectable at the 4-6 week follow-up. In contrast, the debate modality maintained a statistically significant reduction in animosity immediately after and at follow-up ($\Delta=+0.37$ SD). For ideological position, adopting the opposite perspective led to significant immediate movement across modalities (writing: $\Delta=+0.91$ SD; debate: $\Delta=+0.51$ SD), and these changes persisted at follow-up. Judged performance (winning) did not moderate these effects, and willingness to re-participate was similar across conditions (~20-36%). These findings challenge assumptions about
adversarial methods, revealing distinct temporal patterns: non-
adversarial engagement fosters short-term empathy gains, while cognitive engagement through debate sustains affective benefits. The Ideological Turing Test demonstrates potential as a scalable tool for reducing polarization, particularly when combining perspective-taking with reflective
adversarial interactions.
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Geophysics · Cross-listed from cs.LG
HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone
Yihan Wang, Annan Yu, Lujun Zhang et al. (+2 more)
Recent advances have introduced
diffusion models for probabilistic streamflow
forecasting, demonstrating strong early flood-warning skill. However, current implementations rely on recurrent Long Short-Term
Memory (LSTM) backbones and single-step training objectives, which limit their ability to capture long-range dependencies and produce coherent forecast trajectories across lead times. To address these limitations, we developed HydroDiffusion, a
diffusion-based probabilistic
forecasting framework with a decoder-only state space model backbone. The proposed framework jointly denoises full multi-day trajectories in a single pass, ensuring temporal coherence and mitigating error accumulation common in autoregressive prediction. HydroDiffusion is evaluated across 531 watersheds in the contiguous United States (CONUS) in the CAMELS
dataset. We
benchmark HydroDiffusion against two
diffusion baselines with LSTM backbones, as well as the recently proposed
Diffusion-based Runoff Model (DRUM). Results show that HydroDiffusion achieves strong nowcast accuracy when driven by observed meteorological forcings, and maintains consistent performance across the full simulation horizon. Moreover, HydroDiffusion delivers stronger deterministic and probabilistic forecast skill than DRUM in operational
forecasting. These results establish HydroDiffusion as a robust generative modeling framework for medium-range streamflow
forecasting, providing both a new modeling
benchmark and a foundation for future research on probabilistic hydrologic prediction at continental scales.
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All authors Yihan Wang, Annan Yu, Lujun Zhang, Charuleka Varadharajan, N. Benjamin Erichson
Optics
Suspended waveguide-enhanced near-infrared photothermal spectroscopy for ppb-level molecular gas sensing on a chalcogenide chip
Kaiyuan Zheng, Hanyu Liao, Fengbo Han et al. (+11 more)
On-chip waveguide sensors have attracted significant
attention recently due to their potential for high level integration. However, so far on-chip gas sensing based on traditional
laser absorption spectroscopy has demonstrated low detection sensitivity, due to weak light-gas interaction over a limited interaction distance. On-chip photothermal spectroscopy (PTS) appears to be a powerful technique to achieve higher sensitivity, its performance is yet constrained to parts-per-million (ppm)-level due to small fraction of evanescent field in the light-gas interaction zone and fast thermal dissipation through the solid substrate. Herein, we demonstrated suspended chalcogenide glass waveguide (ChGW)-enhanced PTS that overcomes these limitations, enabling highly sensitive parts-per-billion (ppb)-level molecular gas sensing. We fabricated a nanoscale suspended ChGW with low loss of 2.6 dB/cm using CMOS-compatible two-step patterning process. By establishing an equivalent PTS model to guide the optimization of the ChGW geometry, we achieved a 4-fold increase in the absorption-induced heat source power and a 10.6-fold decrease in the equivalent heat conductivity, resulting in a 45-fold enhancement in photothermal phase modulation efficiency over the non-suspended waveguides. Combining with a high-contrast waveguide facet-formed Fabry-Perot interferometer, we achieved an unprecedented acetylene detection limit of 330 ppb, a large dynamic range close to 6 orders of magnitude, and a fast response of less than 1 s. The overall system exhibits a noise-equivalent absorption coefficient of 3.8x10-7 cm-1, setting a new
benchmark for photonic waveguide gas sensors to the best of our knowledge. This work provides a key advancement towards prototyping an integrated sensor-on-a-chip for highly sensitive and background-free photonic sensing applications.
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All authors Kaiyuan Zheng, Hanyu Liao, Fengbo Han, Xueying Wang, Yan Zhang, Jiaxin Gu, Pengcheng Zhao, Haihong Bao, Shaoliang Yu, Qingyang Du, Lei Liang, Chuantao Zheng, Wei Jin, Lijun Wang
Plasma Physics
Large Errors in Kinetic Temperature Measurements Using Particle Tracking Velocimetry
Anton Kananovich, Parth Mehrotra and Surabhi Jaiswal
We report on random errors in kinetic temperature measurements due to finite spatial resolution in particle tracking velocimetry. Using simulated data, we isolate the error caused by finite spatial resolution from other sources of uncertainty, such as particle acceleration and particle mismatch. A sample of particle velocities is generated from a Maxwellian distribution at a prescribed kinetic temperature. Particle positions are assigned randomly and discretized to match a prescribed spatial resolution. Velocities are reconstructed using the two-frame tracking method, and the resulting kinetic temperature is calculated and compared to the true kinetic temperature. Results show that under typical experimental conditions, the uncertainty in particle positions propagates into large errors in the velocity distribution and the measured kinetic temperature. We find that this might introduce errors ranging from tens of percent at high kinetic temperatures ($\sim 10$~eV) to thousands of percent at low temperatures ($\sim 0.1$~eV).
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Plasma Physics
JAX-in-Cell: A Differentiable Particle-in-Cell Code for Plasma Physics Applications
Longyu Ma, Rogerio Jorge, Hongke Lu et al. (+2 more)
JAX-in-
Cell is a fully electromagnetic, multispecies, and relativistic 1D3V Particle-in-
Cell (PIC) framework implemented entirely in JAX. It provides a modern, Python-based alternative to traditional PIC frameworks. It leverages Just-In-Time compilation and automatic vectorization to achieve the performance of traditional compiled codes on CPUs, GPUs, and TPUs. The resulting framework bridges the gap between educational scripts and production codes, providing a testbed for differentiable physics and AI integration that enables end-to-end gradient-based optimization. The code solves the Vlasov-Maxwell system on a staggered Yee
lattice with either periodic, reflective, or absorbing boundary conditions, allowing both an explicit Boris solver and an implicit Crank-Nicolson method via Picard iteration to ensure energy conservation. Here, we detail the numerical methods employed, validate against standard
benchmarks, and showcase the use of its auto-differentiation capabilities.
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All authors Longyu Ma, Rogerio Jorge, Hongke Lu, Aaron Tran, Christopher Woolford
Optics
A Thermal Modeling Toolkit for Continuous-Wave Gaussian Second-Harmonic Generation in KTP Crystal
Mostafa M. Rezaee, Mohammad Sabaeian, Alireza Motazedian et al. (+2 more)
We release an open-source finite-difference toolkit for computing temperature fields in continuous-wave (CW) second-harmonic generation (SHG) using potassium titanyl phosphate (KTP)
crystals under Gaussian end-pumping. The toolkit includes modules for geometry and material definitions, boundary and cooling models, and transient and steady-state finite-difference solvers. Users provide beam and
crystal parameters along with cooling profiles, and the solver returns spatiotemporal temperature fields with radial and axial profiles as exportable
datasets. This implementation consolidates previous work into a single versioned repository with reproducible pipelines and parameterized scenario sweeps covering temperature-dependent versus constant conductivity, convection with or without radiation, and heat-transfer coefficients from $6.5$ to $2.0 \times 10^{4}$~W~m$^{-2}$~K$^{-1}$. The compiled Fortran kernels include built-in
benchmark reporting. Validation is performed by reproducing published temperature distributions and trends for KTP under Gaussian CW pumping. The code is available as an open-source GitHub repository and is released under the MIT license as version v1.0.0, with an archived release on Zenodo identified by DOI 10.5281/zenodo.17266421 for citation and long-term access.
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All authors Mostafa M. Rezaee, Mohammad Sabaeian, Alireza Motazedian, Fatemeh Sedaghat Jalil-Abadi, Mohammad Ghadri
Atmospheric and Oceanic Physics · Cross-listed from cs.CV
MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater
Björn Lütjens, Patrick Alexander, Raf Antwerpen et al. (+3 more)
The Greenland ice sheet is melting at an accelerated rate due to processes that are not fully understood and hard to measure. The distribution of surface meltwater can help understand these processes and is observable through remote sensing, but current maps of meltwater face a trade-off: They are either high-resolution in time or space, but not both. We develop a deep learning model that creates gridded surface meltwater maps at daily 100m resolution by fusing data streams from remote sensing observations and physics-based models. In particular, we spatiotemporally downscale regional
climate model (RCM) outputs using synthetic aperture radar (SAR), passive microwave (PMW), and a digital elevation model (DEM) over the Helheim Glacier in Eastern Greenland from 2017-2023. Using SAR-derived meltwater as "ground truth", we show that a deep learning-based method that fuses all data streams is over 10 percentage points more accurate over our study area than existing non deep learning-based approaches that only rely on a regional
climate model (83% vs. 95% Acc.) or passive microwave observations (72% vs. 95% Acc.). Alternatively, creating a gridded product through a running window calculation with SAR data underestimates extreme melt events, but also achieves notable accuracy (90%) and does not rely on deep learning. We evaluate standard deep learning methods (UNet and DeepLabv3+), and publish our spatiotemporally aligned
dataset as a
benchmark, MeltwaterBench, for intercomparisons with more complex data-driven downscaling methods. The code and data are available at github.com/blutjens/hrmelt.
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All authors Björn Lütjens, Patrick Alexander, Raf Antwerpen, Til Widmann, Guido Cervone, Marco Tedesco
Optics
Benchmark for Non-reciprocal Attack Detection on Synchronization
Zishuo Ren, Yiting Lin, Yufei Zhang et al. (+3 more)
A precise and secure time synchronization is the backbone of both fundamental physics and advanced technologies. Despite ultra-high precision, security, particularly the unresolved vulnerabilities on physical links beyond traditional cryptography, remains the bottleneck. Here, we develop an analysis framework, the
Benchmark Attack Noise Detection (BAND) model, to quantify the security of the high-precision time synchronization when its core assumption, reciprocity, is broken. The model characterizes non-reciprocal attacks through a unified metric, termed attack intensity. Based on the analysis of attack intensity, we accurately predict both instantaneous and accumulating attacks in a 100-km dual-comb two-way time-transfer system. Beyond security considerations, we find that the BAND model can explain intrinsic fiber noise and delay-unsuppressed noise naturally, offering an effective tool to establish an environment-sensitive link model. This work paves the way for a secure large-scale integrated time, sensing and communication networks.
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All authors Zishuo Ren, Yiting Lin, Yufei Zhang, Yang Li, Ziyang Chen, Hong Guo
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Simultaneous power generation and cooling using semiconductor-sensitized thermal cells
Atsushi Hayashida, Hitoshi Saito, Yang Chunxiang et al. (+5 more)
This manuscript reports a
semiconductor-sensitized thermal
cell (STC) that converts ambient heat into electrical power while simultaneously reducing its own temperature under isothermal conditions. Using a printable
semiconductor–electrolyte architecture, we fabricate $4\,\mathrm{cm} \times 4\,\mathrm{cm}$ devices that generate up to approximately $0.2\,\mathrm{mW}$ at temperatures of $40$–$55\,^\circ\mathrm{C}$. During continuous discharge, the STC exhibits a transient temperature decrease followed by thermal equilibration with the environment. In contrast, periodic on–off discharge produces sustained cooling of approximately $1\,^\circ\mathrm{C}$ relative to a non-discharging reference. Notably, parallel integration of four STCs yields a nonlinear enhancement of cooling (approximately $5\,^\circ\mathrm{C}$) without a corresponding increase in electrical output. The observed behavior can be understood within a macroscopic energy-balance framework, in which time modulation of electrochemical heat consumption prevents the establishment of thermal steady state. These results demonstrate sustained isothermal cooling induced by heat-to-electricity conversion at practical device scales, and highlight
semiconductor-sensitized thermal
cells as a platform for coupled energy harvesting and thermal management.
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All authors Atsushi Hayashida, Hitoshi Saito, Yang Chunxiang, Taiga Nishii, Motokazu Ishihara, Yuta Nakamura, Kento Sunaga, Sachiko Matsushita
Applied Physics · Cross-listed from cond-mat.mes-hall
Perturbative Input-Output Theory of Floquet Cavity Magnonics and Magnon Energy Shifts
T. Aguiar and M. C. de Oliveira
We develop a perturbative input-output formalism to compute the reflectance and transmittance spectra of cavity magnonics systems subject to a Floquet modulation. The method exploits the strong hierarchy between the
magnetic-dipole couplings transverse (drive field) and parallel (modulation field) to the static bias field, which naturally introduces the small parameter $\epsilon = (2Ns)^{-1/2}$ associated with the total
spin $Ns$ of the ferromagnet. By organizing the cavity and
magnon fields in a systematic expansion in $\epsilon$, we obtain compact analytic expressions for the spectra up to second order. Using these results, we reproduce the characteristic sideband structure observed in recent Floquet cavity electromagnonics experiments. Furthermore, accounting for the Zeeman interaction between the modulation field and the fully polarized ground state - a contribution typically neglected in previous treatments - we predict an additional
magnon detuning of approximately $0.8\,\mathrm{GHz}$, independent of both modulation frequency and sample size and determined solely by the spatial volume occupied by the modulation field. This identifies a measurable and previously overlooked shift relevant for the interpretation and design of cavity magnonics experiments.
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Chemical Physics · Cross-listed from quant-ph
Symmetry Dilemmas in Quantum Computing for Chemistry: A Comprehensive Analysis
Ilias Magoulas, Muhan Zhang and Francesco A. Evangelista
Symmetry adaptation, universality, and gate efficiency are central but often competing requirements in
quantum algorithms for electronic structure and many-body physics. For example, fully
symmetry-adapted universal operator pools typically generate long and deep
quantum circuits, gate-efficient universal operator pools generally break
symmetries, and gate-efficient fully
symmetry-adapted operator pools may not be universal. In this work, we analyze such
symmetry dilemmas both theoretically and numerically. On the theory side, we prove that the popular, gate-efficient operator pool consisting of singlet
spin-adapted singles and perfect-pairing doubles is not universal when spatial
symmetry is enforced. To demonstrate the strengths and weaknesses of the three types of pools, we perform numerical simulations using an adaptive
algorithm paired with operator pools that are (i) fully
symmetry-adapted and universal, (ii) fully
symmetry-adapted and non-universal, and (iii) breaking a single
symmetry and are universal. Our numerical simulations encompass three physically relevant scenarios in which the target state is (i) the global ground state, (ii) the ground state crossed by a state differing in multiple
symmetry properties, and (iii) the ground state crossed by a state differing in a single
symmetry property. Our results show when
symmetry-breaking but universal pools can be used safely, when enforcing at least one distinguishing
symmetry suffices, and when a particular
symmetry must be rigorously preserved to avoid variational collapse. Together, the formal and numerical analysis provides a practical guide for designing and benchmarking
symmetry-adapted operator pools that balance universality, resource requirements, and robust state targeting in
quantum simulations for chemistry.
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