Instrumentation and Detectors
Recent test beam results of ATLAS ITk Pixel sensors and modules
A. Schmier, B.K. Chitroda, C. Gemme et al. (+11 more)
Test beam studies of ITk pixel sensors and full modules are an essential tool for assessing their performance and operational behavior. In the 2025 test beam campaigns, quad and triplet modules equipped with both pre-production and production readout chips were evaluated. For the first time, triplet modules were tested in beam conditions, both before and after irradiation. Measurements were performed at multiple incident angles to study charge-collection efficiency under conditions representative of actual detector operation. The 2025 test beam
dataset contains several important new elements, enabling further progress toward the full qualification of ITk pixel modules. This work presents an overview of the ITk pixel test beam program including recent triplet module results from the 2025 campaigns.
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All authors A. Schmier, B.K. Chitroda, C. Gemme, N. Kakoty, M.N. Mantinan, H. Pang, K. Rama, S. Ravera, M. Ressegotti, A. Rummler, M. Sachdeva, M.A.A. Samy, M. Ventura, J. Ye
Plasma Physics
Optimized design of a Penning ion source for sealed neutron tube
Shiwei Jing, Jia Song, Shengduo Liu et al. (+7 more)
Sealed
neutron tubes have a wide range of applications, and the ion source is their core component. Penning ion sources commonly suffer from issues such as uneven
magnetic field distribution and a low proportion of monoatomic ions. Improving the performance of the ion source can effectively address the problems of low
neutron flux and short operational lifespan. This study aims to optimise the
magnetic field configuration and discharge parameters of the ion source, thereby increasing the proportion of monoatomic and enhancing discharge stability, and to provide a design basis for high-performance sealed
neutron tubes. Develop a
magnetic field-
plasma coupling model to compare and analyze the
magnetic field distribution patterns of traditional
magnetic block structures and soft iron-reinforced structures, and investigate the mechanisms by which operating pressure and anode voltage affect
plasma density and ion composition using COMSOL multiphysics simulation methods. Simulation results indicate that the soft iron structure significantly enhances the axial
magnetic field strength and uniformity within the discharge region; under conditions of 0.06 Pa gas pressure and 1500 V anode voltage, the proportion of monoatomic ions increased from the conventional 9% to 30%.
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All authors Shiwei Jing, Jia Song, Shengduo Liu, Weiyang Zhang, Sijia Zhou, Hailong Xu, Zebin Li, Tin Zhang, Zhihu Gao, Guofeng Qu
Instrumentation and Detectors
3D Magnetic Field Reconstruction and Mapping with Physics-Informed Neural Networks
Haohan Yu, Zhanxu Hao, Bingzhi Li et al. (+3 more)
Accurate reconstruction of
magnetic fields in inaccessible regions is vital for many high-precision experiments in physics. Traditional methods, such as spherical harmonic expansion, often suffer from truncation errors that limit their precision. This study proposes an advanced Physics-Informed Neural Network (PINN) framework for high-precision 3D
magnetic field mapping. Unlike conventional data-driven models, the proposed PINN integrates Maxwell's equations directly into the loss function, enforcing divergence-free and curl-free conditions across the entire domain. A key innovation is the inclusion of explicit physics-residual losses at measurement locations, ensuring rigorous physical consistency beyond random collocation sampling. Validation using simulated data achieves a reconstruction accuracy of $10^{-4}$, a tenfold improvement over existing PINN
benchmarks. Furthermore, experimental validation using a custom coil assembly demonstrates robust reconstruction with sub-percent relative accuracy, reaching the $10^{-3}$ level under ambient conditions. This AI-driven methodology provides a robust, high-precision solution for field monitoring and measurement in complex experimental environments where direct sensor placement is restricted.
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All authors Haohan Yu, Zhanxu Hao, Bingzhi Li, Zejia Lu, Xiang Chen, Liang Li
Physics and Society
Match classification in the last round of four-team round-robin tournaments
László Csató and András Gyimesi
Classification of matches played in the last rounds of sports competitions is a well-established tool for evaluating tournament designs. Both deterministic and probabilistic approaches are available for this purpose. Our paper offers the first comparison of them by analysing the most prominent example of four-team round-robin competitions, the group stage of the FIFA World Cup. We show that both methods are highly relevant in practice: all (four) deterministic and (six) probabilistic match types occurred in the 2014 and 2018 FIFA World Cups, respectively. The probabilistic model, which accounts for the relative benefits of attacking and defending, provides deeper insights; for instance, the competitive matches from the deterministic approach can be of any of the six probabilistic types. Finally, the probabilistic framework is used to quantify and decompose the impact of the main reforms introduced for the 2026 FIFA World Cup: the expansion to 48 teams, as well as the modified qualification and tie-breaking rules.
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Accelerator Physics
Quantitative Evaluation of a Hybrid Target for Ultraslow-Muon Production in $\mu$TRISTAN
Yasuhito Sakaki, Shusei Kamioka and Mitsuhiro Yoshida
We present the first quantitative evaluation of the hybrid target concept proposed in $\mu$TRISTAN. The $\mu$TRISTAN project is a positive-
muon collider concept based on the ultraslow-
muon production technique, in which thermal muonium emitted from a material surface is subsequently
laser-ionized. The hybrid target consists of a pion production target and a surrounding pion- and
muon-stopping target that also serves as a muonium-production target. In this paper, Monte Carlo simulations of the hybrid target are performed to evaluate the yield of positive
muons stopped near the tungsten surface, where they can contribute to muonium emission. The ultraslow-
muon yield at the hybrid target before extraction is estimated to reach the 10$^{-3}$ level per p–Li
nuclear collision, assuming unit
muon-to-muonium conversion efficiency. This study provides a quantitative
benchmark for hybrid-target design toward an intense ultraslow-
muon source.
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Fluid Dynamics
A semi-implicit two dimensional solver for a covariant formulation of the shallow water equations
Maurizio Tavelli and Olindo Zanotti
In this paper we combine a flexible covariant formulation of the shallow water equations with the semi-implicit numerical scheme developed over the years by Casulli and collaborators. After adopting an orthogonal, but non-orthonormal, coordinate basis on two dimensional
manifolds, and by writing the divergence of symmetric tensors in a way that avoids the introduction of Christoffel symbols, the shallow water equations preserve a very close resemblance to the usual one expressed in Cartesian coordinates. In this way, a stable semi-implicit scheme can be derived by using an implicit discretization for the gradient of surface elevation in the momentum equations and for the velocity in the continuity equation, with stability properties that are independent of the celerity. We have tested the new method over a variety of challenging
benchmarks, including, among the others, the smooth wave propagation over a water globe and the deformation of an artery branch. Two appealing additional features make the method particularly powerful with respect to oceanographic applications: firstly, thanks to the wetting and drying ability of our semi-implicit approach, no pathological behaviors occur at the poles; secondly, the scheme is naturally well-balanced, and it is able to preserve perfect stationarity, up to machined precision, of the entire ocean configuration of the earth.
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Fluid Dynamics
Finite-Time Relaxation of Inertial Particle Clustering in Non-Equilibrium Turbulence
Taketo Tominaga and Ryo Onishi
Inertial particles in
turbulence form clusters, which strongly affect particle collisions and transport properties. Clustering models based on statistically stationary
turbulence implicitly assume the instantaneous-
equilibrium approximation when applied to time-varying non-
equilibrium turbulence. However, the validity of this approximation remains unclear. In this study, the temporal response of inertial particle clustering in non-
equilibrium turbulence was investigated using direct numerical simulation of homogeneous isotropic
turbulence with unsteady forcing. Periodic responses of the flow and clustering intensity were evaluated by varying the forcing period. The flow showed non-
equilibrium scaling for all forcing periods. The relationship between instantaneous energy dissipation rate and clustering intensity showed hysteresis exceeding statistically stationary fluctuations when the forcing period exceeded several large-eddy turnover times. For the particles with the largest inertia, clustering intensity took values of 0.80 and 1.56 times the reference value at the same instantaneous energy dissipation rate. This shows that the instantaneous-
equilibrium approximation is not appropriate under such conditions. A linear relaxation model was constructed from transient responses, in which clustering intensity approaches the instantaneous-
equilibrium value with a finite relaxation time. The relaxation time scaling was identified as $\tau_g = 1.0 T_\mathrm{e}(t)\,\mathrm{St}(t)^{0.40}$, where $T_\mathrm{e}(t)$ and $\mathrm{St}(t)$ are the instantaneous large-eddy turnover time and Stokes number. The model reduced the maximum relative error from 49% to 10% for the particles with the largest inertia and from 76% to 22% in an independent validation case. These results demonstrate that finite-time relaxation improves prediction accuracy for clustering intensity in non-
equilibrium turbulence.
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Physics and Society · Cross-listed from cs.CY
Generative AI impacts on intra-urban inequality and skill premium in Beijing
Xiliu He, Haoxiang Zhao, Mingyi Ma et al. (+6 more)
Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018–2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large
language models. We examine the spatial, structural and causal mechanisms of this shock. We find that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers – a "high-skill trap." This wage penalty is driven by task de-skilling and intensified labor-
market crowding. A difference-in-differences design centered on ChatGPT's release supports a causal interpretation. These findings challenge the prevailing theory of skill-biased technological change and provide a basis for inclusive AI governance in global technology hubs.
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All authors Xiliu He, Haoxiang Zhao, Mingyi Ma, Edward Wen Chuan Lai, Koei Enomoto, Anni Hu, Jiatong Li, Lingyun Chu, Yuan Lai
Atomic Physics
From Vintage Mythology to Topological Physics: Unveiling a Universal Structural Attractor in Alcoholic Beverage Aging
Xinyue Jiang, Heng Yang, Zhiyin Jiu et al. (+3 more)
Alcoholic beverage properties are increasingly understood through ethanol-water structural states rather than empirical labels such as alcohol content and vintage. Yet whether chronological vintage similarly reflects an intrinsic structural state remains unclear. Here, we apply persistent homology to map the topological
evolution of self-assembled molecular aggregates in strong-aroma Baijiu aged 1-10 years. The resulting fingerprints reveal a three-stage maturation pathway: rapid scaffold consolidation (B0), population-level channel stabilization (B1), and non-monotonic cavity reorganization (B2). These coupled trajectories converge toward a mature topological state rather than passively tracking chronological age. We therefore propose a universal topological attractor, in which optimal aging is defined by a system's position in persistence space relative to a mature structural domain. This framework reframes beverage aging as
navigation through structural state space, providing a physical basis for quality evaluation and accelerated maturation.
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All authors Xinyue Jiang, Heng Yang, Zhiyin Jiu, Youxi Luo, Lin Chen, Yuqun Xie
Atomic Physics
FPGA-based disturbance-observer servo for broadband noise suppression in laser frequency stabilization
Meung Ho Seo, Jae Hoon Lee, Young-Ho Park et al. (+7 more)
We have demonstrated broadband frequency-noise suppression in a
laser stabilization system by augmenting a conventional proportional-integral-derivative (PID) controller with a digital disturbance observer (DOB) implemented on a field-programmable gate array (FPGA). The DOB employs a first-order exponential moving average filter as its Q-filter, replacing multi-parameter frequency-domain plant identification with a single one-dimensional gain sweep. Using modulation transfer spectroscopy on the 87Rb D2 line at 780.24 nm, we have measured the frequency-noise power spectral density and the Allan deviation of the beat note between two independently stabilized
lasers. The integrated rms frequency noise below 40 kHz decreased by 16.9 dB compared with PID alone, corresponding to a reduction from approximately 140 kHz to 20 kHz. The short-term fractional frequency instability improved from sigma_y(1 ms) = $7.9 \times 10^{-12}$ to $4.6 \times 10^{-12}$, while the long-term stability at tau > 1 s remained within statistical uncertainty. This DOB-augmented architecture offers a simple and effective route to enhanced noise rejection in FPGA-based servo systems for atomic physics experiments.
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All authors Meung Ho Seo, Jae Hoon Lee, Young-Ho Park, Hyun-Gue Hong, Seji Kang, Myoung-Sun Heo, Sang-Bum Lee, Taeg Yong Kwon, Sangwon Seo, Sang Eon Park
Data Analysis and Statistics · Cross-listed from cond-mat.mes-hall
AI-Driven SERS for Non-invasive and Label-Free Extracellular Vesicle Detection Across Cellular Origins in Tears and Sweat
Yang Li, Xiaoming Lyu, Ling Xia et al. (+5 more)
Wearable sensing technology capable of point-of-care, continuous and non-invasive analysis of exosomes in biofluid such as tears and sweat is an essential part for future personalized medicine. Major detection and identification methods of
cell secreted Extracellular Vesicles (EVs) often require labeling and are time-consuming, resulting in low efficiency in EV mechanism research and disease diagnosis. While the label-free Surface-enhanced Raman spectroscopy (SERS) has been combined with deep learning model for EV identification in blood, their application to non-invasive detection of EVs in tears and sweat are missing. Here, we filled this gap by developing an artificial intelligence (AI)-assisted Surface-enhanced Raman spectroscopy (SERS) method based on salt-induced nanoparticle aggregation for fast EV identification in tears and sweat with high accuracy. Significantly, our label-free detection and AI differentiation of EVs from 6
cell lines (HepG2, Hela, 143B, LO-2, BMSC, H8) achieved the identification of EVs in tear fluids from 7 different disease sources with accuracies >92%. Our results showed that this platform can not only distinguish EVs from multiple
cell sources but also generate highly reproducible and selective EV signals in tear fluids without a need for chemical labeling or separation steps. Molecular dynamics simulations revealed that silver atoms (Ag) form electrostatic interactions with oxygen atoms of multiple amino acid residues in
proteins, suggesting a high affinity. This strategy realizes ultra-sensitive and anti-interference detection of EVs, providing a new idea for the rapid diagnosis of clinical diseases.
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All authors Yang Li, Xiaoming Lyu, Ling Xia, Kuo Zhan, Haoyu Ji, Lei Qin, Seppo J. Vainio, Jian-An Huang
Atomic Physics · Cross-listed from cond-mat.soft
Non-equilibrium pathway to mesoscale ordering in ethanol-water binary liquid
Xinyue Jiang, Yating Shang, Jianhui Li et al. (+3 more)
Ethanol-water mixtures are a classic example of thermodynamic non-ideality, yet the structural origin of their pronounced anomalies, such as volume contraction and a large negative excess
entropy, has remained a long-standing puzzle. Here, we demonstrate these anomalies are not
equilibrium properties but calorimetric fingerprint of an arrested phase transition. By imposing periodic thermal oscillations, we drive a 50% (v/v) ethanol-water system along a complete hierarchical self-assembly pathway that progressed from ethanol clusters to water-containing droplets, then to acicular flakes, and finally to micron-scale ordered ethanol aggregates. Fluorescence spectroscopy, two-dimensional correlation analysis and
nuclear magnetic resonance revealed the underlying non-
equilibrium molecular mechanism: a periodic perturbation of the water-dominated hydrogen-bond network initiates a ethanol-water coexistence intermediate, ultimately leading to the stable ordered assembly of an ethanol-rich phase. Our finding demonstrated that periodic physical perturbations capable drive spontaneous ordering across multiple length scales in a simple binary mixture, providing a kinetic perspective on the structural origin of solution non-ideality, and carry general implications for self-assembly strategies in soft matter.
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All authors Xinyue Jiang, Yating Shang, Jianhui Li, Zhaoyong Zou, Yanxia Zuo, Yuqun Xie
Atmospheric and Oceanic Physics
Lagged sea-surface-temperature precursors of the leading PM2.5 mode in China
Yuan Chen, Dan Zhao and Xu Li
Fine particulate matter(PM2.5) pollution in China is strongly modulated bymeteorological variability, yet its seasonal predictability from oceanic signals remains unclear. Here we identify the leading PM2.5 variability mode over China and show that it is preceded by coherent sea-surface-temperature anomaly clusters by more than one season. These oceanic precursors influence summer PM2.5 mainly by altering precipitation and lowlevel ventilation, and winter PM2.5 by modulating boundary-layer height and near-surface stagnation. Using the four largest precursor regions, a simple regression model achieves significant independent prediction skill for both summer and winter PM2.5 variability. Our results reveal a physical pathway linking sea-surface-temperature
memory to regional aerosol pollution and provide a basis for seasonal air-quality risk assessment.
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Computational Physics · Cross-listed from gr-qc
Relativistic elastic shells: material support and cavity geometry
An T. Le
We derive the source moments that determine the leading
tidal field and rotational clock shift inside a finite-thickness relativistic elastic shell. In weak
gravity and slow rotation, these moments depend on the elastic response of the material and its two free
vacuum faces. Two choices of elastic
moduli, each causal near relaxation, give opposite signs for both observables. Both shells have oblate faces and gain rotational mass. The tide depends on differential flattening, bulk deformation, and rotational stress and energy; the central clock shift measures a distinct inverse-radius moment. A single constitutive action determines the material support and response. Nonnegative isotropic pressure cannot support a static spherical shell of positive-density matter with two free
vacuum faces, whereas tangential elastic stress admits
equilibria near relaxation. For sufficiently weak
gravity these
equilibria obey the dominant energy condition and have subluminal sound speeds. Their static cavities are locally flat, with clocks redshifted relative to infinity. At leading weak-
gravity order, static
redshift and linear frame dragging probe the same inverse-radius mass moment. Energy estimates establish radial linear stability and exclude exponentially growing odd-parity modes for weakly gravitating spherical
equilibria. Nonlinear compression can nevertheless produce unbounded gradients while the material remains causal. Nonspherical even-parity and rotating stability, together with quantitative bounds at finite rotation, remain open. The cavity observables distinguish material responses that surface shape and total mass alone do not determine.
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Fluid Dynamics
A High-Performance, Cross-Platform Open-Source Solver for the Incompressible Navier-Stokes Equations in FEALPy
Wang Pengxiang, Huang Xianbo, Peng Li et al. (+1 more)
To address the dual challenges of performance portability across heterogeneous hardware and the high usability barriers of conventional computational fluid dynamics (CFD) software, this paper introduces FEALPy.CFD, a high performance, open-source solver for the incompressible Navier-Stokes equations developed within the FEALPy framework. The solver's core innovation is its backend-agnostic design, which supports multiple computational backends like NumPy, PyTorch, and JAX to enable seamless switching between CPU and GPU computations with minimal code modification, thereby maximizing hardware utilization and code portability. Its highly modular architecture provides a library of composable components for various spatial discretization schemes, greatly simplifying the development and integration of new
algorithms.Validation on
benchmark cases confirms that the implemented numerical schemes achieve their theoretical orders of convergence. Furthermore, the capability to select a suitable backend architecture for different computational tasks fully leverages the hardware's potential, delivering substantial efficiency gains. By lowering the technical barrier to high-performance, cross-platform fluid dynamics simulation, FEALPy.CFD offers a powerful and accessible tool for academic research, engineering applications, and reproducible computational science.
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All authors Wang Pengxiang, Huang Xianbo, Peng Li, Wei Huayi
Atomic Physics
The Pseudospectral Method for the Dirac Equation with Confining Potential
Dengshan Liu, Huihui Xie, Pengxiang Du et al. (+2 more)
We observe that solving the Dirac equation for confined potentials using the generalized pseudospectral (GPS) method leads to deteriorating convergence of energy
eigenvalues and highly oscillatory in wave functions as the confinement radius decreases. It is found that this issue stems from the first-order differentiation formulation employed in GPS method. Motivated by this insight, we adopt the kinetically balanced generalized pseudospectral method, which incorporates the kinetically-balanced condition into the GPS method. Numerical results demonstrate that the mono-kinetically-balanced generalized pseu dospectral (MKB-GPS) method yields converged energy
eigenvalues and generates smooth, continuous wave functions. This is the first application of the MKB-GPS method to confined potentials, and its effectiveness is validated for small confinement radii.
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All authors Dengshan Liu, Huihui Xie, Pengxiang Du, Jian Li, Tomoya Naito
Fluid Dynamics · Cross-listed from cond-mat.mes-hall
Microfluidic Actuation by Einstein-de Haas Spin Torque
Xin Hu and Mamoru Matsuo
We propose
spin-current microfluidic actuation of a sealed
liquid metal.
Spin angular momentum injected from Pt contacts enters the
liquid as an Einstein-de Haas torque and is converted through micropolar angular-momentum balance into viscous flow without pressure drive, moving walls,
magnetic fields, Lorentz forces, or charge flow through the
liquid. The dc velocity obeys universal
spin-
diffusion scaling, and the finite-frequency
spin-mechanical admittance resolves viscous momentum
diffusion,
spin transport, microrotation relaxation, and interface transparency of the
liquid-metal channel.
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Plasma Physics
Bayesian Estimation of Spectroscopic Parameters: Application to the Atomic Nitrogen Bound-Bound System
Tae Woong Jeong and Sung Min Jo
Atomic nitrogen bound-bound radiation is a major component of the radiative heat flux on hypersonic vehicles entering nitrogen-dominated atmospheres, yet its prediction is limited by substantial parametric uncertainty in the published Einstein coefficients and Stark broadening coefficients. In the present study, these spectroscopic parameters are inferred and their uncertainty is quantified through
Bayesian inversion of
equilibrium spectral radiance measured in the NASA Ames Electric-Arc Shock Tube for two shots of the Test 62 campaign at shock speeds of 10.32 and 10.72 km/s. The inference is restricted to the post-shock
equilibrium region, where the Boltzmann assumption closes the species population degree of freedom. The residual uncertainty in the post-shock temperature and species number densities is incorporated as a coupled nuisance parameter distribution. A hybrid principal component analysis and
polynomial chaos expansion surrogate model and a likelihood formulated jointly over the two shots enable tractable Markov chain Monte Carlo sampling across multiple wavelength regions. Eighteen parameters in total, ten Einstein coefficients and eight Stark broadening coefficients, are inferred across eight wavelength regions, with posterior uncertainties significantly reduced relative to the prior literature bands. Forward propagation of the joint posterior through the stagnation-line flow field around a 3 m radius sphere at entry velocities of 10, 12, and 14 km/s demonstrates a reduction in the standard deviation of the predicted radiative heat flux by approximately a factor of five compared with the prior, in particular at 14 km/s, it drops from 10.4 to 1.94 W/cm$^{2}$.
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Chemical Physics
An Efficient On-the-Fly Nonadiabatic Coupling Framework Integrated into CP2K
Xiaoke He, Hsiao-Yi Tsai, Ziwei Chai et al. (+3 more)
Nonadiabatic molecular dynamics (NAMD) is widely used to describe hot electron relaxation and nonradiative recombination processes, but high computational costs limit its application to large supercells. Here, we implement a nonadiabatic coupling (NAC) module directly into CP2K, enabling on-the-fly NAC calculation during ab-initio molecular dynamics. Unlike conventional approaches relying on interfaces between external NAMD programs and electronic structure codes, this integration streamlines the workflow. Combined with CP2K's inherent Gaussian and Plane Waves (GPW) method, it achieves efficient NAC calculations for large-scale systems. To ensure numerical stability, a phase correction scheme is introduced to remove inconsistencies of wavefunction phases.
Benchmark calculations of hot electron relaxation in crystalline pentacene agree with previous studies, while the results of simulations with and without phase correction show significant difference, confirming the necessity of the phase correction. Simulations on larger pentacene supercells further demonstrate the capability of this implementation for large-scale NAMD simulations.
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All authors Xiaoke He, Hsiao-Yi Tsai, Ziwei Chai, Junwen Yin, Zhongfei Xu, Li-Min Liu
Computational Physics · Cross-listed from cs.LG
PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
Divyam Goel, Nithin Chalapathi, Sanjeev Raja et al. (+1 more)
Inverse problems in partial differential equations (PDEs) involve estimating the physical parameters of a system from observed spatiotemporal solution fields. Neural networks are well-suited for PDE parameter estimation due to their capability to model function-to-function space transformations. While existing
benchmarks of machine learning methods for PDEs primarily focus on the forward problem, there are no similar comprehensive studies and
benchmark datasets on PDE inverse problems, i.e., mapping solution fields to underlying physical parameters. We fill this gap by introducing PDEInvBench, a comprehensive
benchmark dataset consisting of numerical simulations for both time-dependent and time-independent PDEs across a wide range of physical behaviors and parameters. Our
dataset includes evaluation splits that assess performance in both in-distribution and various out-of-distribution settings. Using our
benchmark dataset, we comprehensively explore the design space of neural networks for PDE inverse problems along three key dimensions: (1) optimization procedures, analyzing the role of supervised, self-supervised, and test-time training objectives on performance, (2) problem representations, where we study the value of architectural choices with different inductive biases and various conditioning strategies, and (3) scaling, which we perform with respect to both model and data size. Our experiments reveal several practical insights: 1) neural networks perform best with a two-stage training procedure: initial supervision with PDE parameters followed by test-time fine-tuning using the PDE residual, 2) incorporating PDE derivatives as input features consistently improves accuracy, and 3) increasing the diversity of initial conditions in the training data yields greater performance gains than expanding the range of PDE parameters. We make our
dataset and codebase publicly available.
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All authors Divyam Goel, Nithin Chalapathi, Sanjeev Raja, Aditi S. Krishnapriyan
Biological Physics
Hydrodynamics constrain choanoflagellate collar geometry
Tasawar Iqbal, Catherine Penington, Christian Thomas et al. (+1 more)
As the closest living relatives of animals, choanoflagellates exhibit remarkable diversity. Even their microvilli collar, used to filter and capture food, varies significantly among species. This diversity suggests either strong environmental adaptation or an insensitivity to the collar geometry. Previous hydrodynamic studies have suggested that the pressure change across the collar is similar across species. In this study, we show that hydrodynamics imposes additional
geometric constraints on the choanoflagellate collar. We create a simplified, reduced-order model that neglects finite collar length to investigate how the microvillus radius and the gap between microvilli influence the flow. Comparing with biological data reveals significant variation in the pressure drop between species. Additionally, a ridge emerges in the microvilli radius-gap phase space, along which both effective flux and power dissipation are maximised. Notably, several species cluster near the flux ridge but lie away from the power dissipation ridge. These observations suggest that choanoflagellate collars do not necessarily share a similar pressure drop. Instead, their geometry is influenced by the competing demands of maximising flux and minimising power costs. The broad variation observed among species is made possible by these ridge-like structures.
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All authors Tasawar Iqbal, Catherine Penington, Christian Thomas, Lyndon Koens
Optics
Mid-infrared single-photon computational temporal ghost imaging
Wen Zhang, Kun Huang, Xu Wang et al. (+4 more)
The capture of transient optical waveforms is critical to reveal dynamical phenomena in various fields. However, fast and sensitive mid-infrared (MIR) measurements are typically limited by processing bandwidth and detection sensitivity of conventional infrared detectors. Here, we propose and implement a computational temporal ghost imaging system, which favors high-speed and high-sensitivity characterization of MIR temporal objects. The core process relies on high-fidelity nonlinear optical transduction for facilitating both the programmable structured illumination and frequency upconversion detection based on the high-performance near-infrared light modulator and detector, respectively. Consequently, the correlation between the recorded integral upconversion intensity and the designated encoding patterns allows one to reconstruct the MIR profiles with a temporal resolution of 80 ps, well beyond the intrinsic bandwidth or timing jitter of the involved detectors. Moreover, a record-high detection sensitivity is manifested by recovering single-
photon MIR waveforms with an incident flux below 0.1
photon/bit. Additionally, faithful reconstructions at sub-Nyquist sampling rates are demonstrated using the compressive sensing
algorithm, which can reduce the data acquisition time by over 90%. The presented paradigm features high timing precision, single-
photon sensitivity, and efficient data sampling, which could be extended into far-infrared or terahertz regions to address pressing demands in fast and sensitive sensing.
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All authors Wen Zhang, Kun Huang, Xu Wang, Ben Sun, Jianan Fang, Yijing Li, Heping Zeng
Optics
Infrared Single-Pixel Hyperspectral Imaging via Spatial-Temporal Multiplexing
Ben Sun, Kun Huang, Zhibin Zhao et al. (+3 more)
Near-infrared (NIR) hyperspectral imaging is widely used to reveal morphological and chemical information. However, conventional spectral imagers usually rely on costly focal plane arrays and suffer from data redundancy and inefficiencies in spatial-spectral data acquisition. Here, we devise and implement a single-pixel NIR hyperspectral imaging system based on high-fidelity spectrum-to-time mapping and high-precision spatial-encoding compressive measurements. The system employs a single-mode telecommunication fiber for temporal dispersion and a programmable spatial light modulator to impose structured spatial patterns, with all signals detected by a single InGaAs photodetector. By correlating temporally stretched waveforms with spatial encodings, we reconstruct 64$\times$64 spatially resolved hyperspectral datacubes spanning 50 spectral bands over the 1550-1600 nm range. Furthermore, real-time monitoring of dynamic
liquid injection is demonstrated at a datacube refreshing rate of 12 Hz under sub-Nyquist sampling. The presented architecture features single-pixel simplicity, high optical throughput, and efficient data acquisition, which would pave a novel way for NIR spectral imaging in biomedical diagnostics and material characterization.
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All authors Ben Sun, Kun Huang, Zhibin Zhao, Beibei Dong, Jianan Fang, Heping Zeng
Fluid Dynamics
Scale invariant solutions in relativistic hydrodynamics
Esteban Calzetta
The goal of this work is to describe the scale invariant solutions of a typical relativistic hydrodynamic model. We shall take as representative model an Israel-Stewart framework, where the energy-momentum conservation laws for a conformal invariant fluid are supplemented by a Cattaneo-Maxwell equation for its viscous energy-momentum tensor. In these models the viscous energy-momentum tensor relaxes to its Landau-Lifshitz value on a finite time scale. We assume the parameters of the model depend on the speed of light $c$ in such a way that as $c\to\infty$ the fluid becomes an incompressible fluid obeying the Navier-Stokes equations. We seek the scale invariant solutions for this model and find that for finite $c$ there are two basic patterns, one which reproduces Kolmogorov
turbulence when $c\to\infty$, and another whose damping rate diverges in that limit. We point out the scaling relations that allow the latter flow pattern to sustain an
entropy cascade.
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Chemical Physics
A DMFT approach to evaluate electronic frictional effects near solid surfaces of strongly correlated systems
Yunhao Liu and Wenjie Dou
Electronic friction-Langevin dynamics (EF-LD) provides an efficient framework for capturing nonadiabatic effects at solid surfaces, with particular relevance to electrochemistry and molecular electronics. In this work, we investigate electronic friction in the two-dimensional Hubbard-Holstein model employing dynamical mean-field theory (DMFT), where the full density-
matrix numerical renormalization group (FDM-NRG) serves as the
impurity solver. Our results are benchmarked against mean-field theory (MFT). DMFT yields two distinct peaks in the electronic friction, arising from electron attachment/detachment resonances with the solid
Fermi level, whereas MFT is unable to capture this
Fermi resonance. We further examine the dynamics of electronic friction via EF-LD simulations. Our simulations uncover significant discrepancies mainly in the electronic population
evolution predicted by MFT versus DMFT, indicating that MFT is inadequate for describing nonadiabatic dynamics in strongly correlated systems. Thanks to its flexibility and computational efficiency, the proposed DMFT-based approach can be readily extended to a broad range of applications.
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