Computational Physics
Physics-informed Bayesian Optimization for Quantitative High-Resolution Transmission Electron Microscopy
Xiankang Tang, Yixuan Zhang, Juri Barthel et al. (+4 more)
Quantitative high-resolution transmission electron microscopy (HRTEM) provides an indispensable means to understand the structure-property relationships of a material in atomic dimensions. Successful quantification requires reliable
retrieval of essential atomic structural information despite artifacts arising from unwanted but practically unavoidable imaging imperfections. Experimental observation carried out in tandem with model-based iterative image simulation shows vast applications in quantitative structural and chemical determination of objects spanning zero to three dimensions [Prog. Mater. Sci. 133, 101037, 2023]. However, the large number of parameters involved in the simulations make the current multi-step, user-guided iterative approach highly time consuming, thereby restricting its application primarily to small sample areas and to experienced users. In this work, we implement and apply a physics-informed
Bayesian optimization (BO) framework to advance HRTEM quantification towards full automation and large-field-of-view analysis. Unlike conventional optimization approaches, our method adopts a stepwise strategy that fully leverages the strength of BO in handling high-dimensional parameters, while its probabilistic engine rigorously and efficiently refines the parameter space to enable rapid quantification. Using a BaTiO3 single
crystal that contains heavy, medium and light elements as a model system, we demonstrate that the three-dimensional
crystal structure can be determined from a single HRTEM image with a three to four order-of-magnitude improvement in time efficiency. This approach thus opens new avenues for fast and automated image quantification over larger sample volumes and, potentially, in the time domain.
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All authors Xiankang Tang, Yixuan Zhang, Juri Barthel, Chun-Lin Jia, Rafal E. Dunin-Borkowski, Hongbin Zhang, Lei Jin
Optics
Structure orientation determined in transmission and reflection: q-plate
Hsin-Hui Huang, Meguya Ryu, Shuji Kamegaki et al. (+9 more)
Determination of orientation in the imaged sample/scene has a large application potential when the anisotropy of properties is analysed, usually, under a linearly polarised illumination. This study combined several improvements of microscopy imaging: use of an incoherent white illumination source (a lamp) with a spectral filter to define a spectral window, a plastic circular polariser to image with circularly polarised light (instead of linear), and a 4-pol. camera with integrated polarisers for simultaneous acquisition of images at four pi/4-azimuth shifts. In the transmission mode, a high-fidelity readout of form birefringent optical elements, q-plates, was achieved using a fitting procedure based on the analytical expression of S0 (intensity) Stokes parameters at the pixel level. In the reflection mode, the S0 fit was used to determine the azimuth orientation of the q-plates, as well as a generic Amp x cos(2Q1 - 2Q0) + Offset fit (at pixel level) applied to images taken at four Q1 azimuths. The 4-pol. analysis in reflection under circularly polarised illumination is discussed.
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All authors Hsin-Hui Huang, Meguya Ryu, Shuji Kamegaki, Haoran Mu, Eulalia Puig Vilardell, Vijayakumar Anand, Jitraporn Vongsvivut, Nguyen Hoai An Le, Tomas Katkus, Gediminas Seniutinas, Junko Morikawa, Saulius Juodkazis
Geophysics
A decade of airborne electromagnetic surveying Lake Menindee (Australia) under varying water levels
Anandaroop Ray, Andrew McPherson, Ross C. Brodie et al. (+8 more)
Time domain airborne electromagnetic (AEM) surveying is a mature geophysical tool for imaging the Earth's shallow subsurface. It produces images of the electromagnetic conductivity structure of the earth, down to depths of a few hundred metres. The AEM method is fast, with aircraft acquiring data at speeds of 100-300 km/hr, making it an ideal near-surface reconnaissance tool. The physics of the AEM method are sensitive primarily to the subsurface conductivity, which is influenced by a range of geological factors such as mineral content, porosity, and water content and chemistry. In addition, the inferred subsurface conductivity depends on the accurate measurement and modelling of airborne transmitter and receiver geometries. In this work, we present inferences of the subsurface conductivity over Lake Menindee, New South Wales, Australia, using data from various AEM systems over the period 2014-2024. The lake storage has varied dramatically over this time and while this difference in storage volume undoubtedly influences the near surface conductivity, a remarkably consistent interpretation of the regional geology emerges. While the upper ten metres of the modelled depth sections exhibit the greatest time-variability in inferred electromagnetic conductivity, a correlation of lakebed near-surface conductivity with the lake water volume cannot robustly be established. We also provide information theoretic calculations for each inversion result to aid in their quantitative comparison. The implications of our study are that subtle, shallow, hydrogeological changes are difficult to image with repeat overflights. Conversely, we establish that different AEM systems robustly image the regional geo-electric structure of the near surface, validated by known stratigraphy and borehole conductivity logs.
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All authors Anandaroop Ray, Andrew McPherson, Ross C. Brodie, Alan Yusen Ley-Cooper, Kok Piang Tan, Michael Hatch, Ravin N. Deo, Sebastian Wong, Francesco Dauti, Wayne Cook, Tim Scarr
Optics
Anisotropic non-Hermitian skin effect in a two-dimensional Lieb photonic crystal
Zhi-Kang Xiong, Tianlei Wen, Y. Liu et al. (+2 more)
In this contribution paper, we construct a two-dimensional non-Hermitian (NH) photonic
crystal (PhC) to prototype its anisotropic non-Hermitian skin effect (NHSE) for experimental proposal. Based on the tight-binding model for Lieb
lattice with NH coupling, a nontrivial spectral winding number is pinpointed for certain eigenstates, which translates to geometry-dependent skin modes with tilt boundaries. For ease of implementation, complex refractive indices are employed for the Lieb unit
cell of PhC to emulate the NH coupling. Validated by full wave simulation, our work underscores the boundary dependence of skin effect, and provides a concrete prototype design of NHSE implementable by state-of-the-art of topological metamaterial platforms.
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All authors Zhi-Kang Xiong, Tianlei Wen, Y. Liu, Hai Lin, Bin Zhou
Applied Physics
Cylindrical Metasurface for Efficient Traveling-wave MRI at 7 T
Kristina I. Popova, Georgiy A. Solomakha, Zicheng Wen et al. (+4 more)
This research focuses on the design and evaluation of an ultrathin cylindrical metasurface for improving the transmit efficiency of traveling-wave
magnetic resonance imaging (MRI) of the human
brain. To improve efficiency, we matched a travelling waveguide mode to an electrically large, lossy dielectric load using a thin cylindrical metasurface, which occurs to be a task closely related to impedance matching in waveguide
circuits in the microwave. This metasurface was designed as a compact and lightweight replacement for a high-permittivity dielectric waveguide previously proposed for the same purpose. The dispersion analysis showed that both structures (waveguide and metasurface) support a similar type of slow-wave propagation, characterized by a uniform
magnetic field profile close to the cylinder axis. At the Larmor frequency, the longitudinal wavenumbers showed close agreement. Based on the optimized unit
cell geometry of the periodic copper strip grid loaded with PCB capacitors, full numerical model of the cylindrical metasurface in the presence of a voxel human body model was constructed. We also compared the proposed metasurface with the dielectric waveguide in the traveling-wave setup experimentally, including in vivo measurements performed on a healthy volunteer. The proposed metasurface showed improved B1 + homogeneity (by 17.3%), transmit efficiency (by 27.4%), and SAR-efficiency (by 23%) compared to the dielectric waveguide. The proposed cylindrical metasurface, optimized for field enhancement in the human
brain at 7 T in the traveling-wave excitation regime, can further improve the transmit efficiency and homogeneity in the region of interest compared to state-of-the-art structures for traveling-wave MRI, at the same time, granting the advantages of light weight and compactness.
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All authors Kristina I. Popova, Georgiy A. Solomakha, Zicheng Wen, Mikhail M. Popov, Xiatong Zhang, Stanislav B. Glybovski, Yang Gao
Optics
7-Rod-Core Thulium-Doped Fiber for Enhanced Fiber Laser Cooling
Ivo Bartoň, Jan Aubrecht, Bara Švejkarová et al. (+7 more)
Structured-core thulium-doped fibers were developed to reduce heat load, enable shorter-wavelength operation, and achieve a pedestal-free design. In a proof-of-principle experiment,
laser slope efficiencies of 52% at 1907 nm and 54% at 1940 nm were achieved with respect to absorbed power.
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All authors Ivo Bartoň, Jan Aubrecht, Bara Švejkarová, Jan Pokorný, Michal Kamrádek, Ondřej Podrazký, Petr Vař'ak, Ivan Kaš'ik, Martin Grábner, Pavel Peterka
Chemical Physics
Data-Efficient Active Learning Discovery of Transition Metal Photosensitizers for Type I Photodynamic Therapy
Alessio Fallani, Pi A. B. Haase, Julianne F. F. Eckert et al. (+4 more)
Transition-metal complexes (TMCs) are promising photosensitizers for Type~I photodynamic therapy (PDT), where electron-transfer processes can generate reactive oxygen species under hypoxic conditions. Yet identifying candidates with the required ground- and excited-state redox energetics remains challenging across the vast chemical space of TMCs. Here, we develop a data-efficient active learning (AL) framework for the discovery of Type~I active TMC photosensitizers by combining a chemically structured design space of over 2.1 million Ru(II), Os(II), and Ir(III) complexes with targeted DFT calculations and pretrained atomistic representations. With only 300
quantum-chemical evaluations, the approach efficiently enriches candidates within a mechanistically defined optimal redox region. Analysis of the viable complexes reveals chemical design principles linking metal identity, ligand framework, substituent pattern, and physicochemical properties to Type~I photoreactivity, including a pronounced preference for Os(II)-based complexes and electronically asymmetric ligand environments along with combination of electronic donating and accepting substituents. More broadly, the strategy presented herein provides a scalable, mechanism-guided route for the rational design of transition-metal photocatalysts for applications spanning biomedicine,
solar energy conversion, and photoredox chemistry.
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All authors Alessio Fallani, Pi A. B. Haase, Julianne F. F. Eckert, Luukas Nikkanen, Sherri A. McFarland, Martina Stella, Fabijan Pavošević
Classical Physics · Cross-listed from cond-mat.stat-mech
Macroscopic Thermodynamic Framework for the Mpemba Effect
Yun-Qian Lin, Z. C. Tu and Yu-Han Ma
The counterintuitive Mpemba effect, wherein a hotter system cools faster, critically lacks a general macroscopic theory. Here, starting from linear irreversible thermodynamics, we formulate a generalized Newton's cooling law, $\mathrm{d}T/\mathrm{d}t = -[\gamma_0 + \mathcal{M}Q(t)](T-T_r)$, for a system at temperature $T$ relaxing in a thermal reservoir at $T_r$, where the bare relaxation rate $\gamma_0$ is modified by an initial-state
memory term, $Q(t) \propto T(0)-T_r$. Arising from the interplay between heat flux and structural
evolution, the coefficient $\mathcal{M}$ governs anomalous relaxation behaviors, where $\mathcal{M} > 0$ ($\mathcal{M} < 0$) induces the (inverse) Mpemba effect. This universal thermodynamic framework maps out phase diagram to provide general criteria for the Mpemba effect in complex systems, offering a macroscopic picture that bridges disparate microscopic approaches.
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Optics
Electromagnetic coupling between subradiant plasmons and dye molecular excitons analyzed by spectral changes in ultrafast surface-enhanced fluorescence
Tamitake Itoh and Yuko S. Yamamoto
Electromagnetic (EM) coupling between molecular
exciton and plasmon has been studied using in Rayleigh scattering or extinction spectroscopy. However, evaluating EM coupling involving subradiant plasmon is challenging because this resonance does not manifest clearly in far-field spectra. In this study, we developed a method to evaluate such coupling using EM enhancement factors (FR) derived from ultrafast surface-enhanced fluorescence (ultrafast SEF). This SEF, which appears as a broad background in surface-enhanced resonant Raman scattering (SERRS) spectra, were measured using silver nanoparticle dimers containing dye molecules within their nanogaps. Our results show that the spectral peaks of FR for subradiant resonances appear near the dips in Rayleigh scattering spectra. Furthermore, these FR peaks exhibit blue-shifts during the quenching processes of both ultrafast SEF and SERRS. We examined these static and temporal spectral properties using a coupled oscillator model composed of radiant plasmons, subradiant plasmons, and molecular
excitons. The static properties were reproduced by increasing the linewidths of the radiant plasmon resonance, while the temporal properties were captured by decreasing the EM coupling energies between the
exciton and both plasmon oscillators. These findings indicate that this methodology is a powerful tool for evaluating EM coupling between subradiant plasmons and molecular
excitons.
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Optics
Anisotropic propagation of GHz surface and bulk acoustic waves in gallium arsenide studied by random scattering
T.A. Steenbergen, M.M. Wohlfarth, P.E. Veefkind et al. (+2 more)
Understanding the complex anisotropic acoustic propagation in
crystals is crucial for optimizing the performance of surface and bulk acoustic wave devices. Here, we investigate the anisotropy and coupling of GHz acoustic modes in (001)-cut gallium arsenide through theory and experiment. We first numerically calculate the angle-dependent phase velocities for surface and bulk modes, and we provide a code which can easily be adapted to different material systems. We validate our theoretical model experimentally by exciting surface modes with an interdigital transducer, and achieve omnidirectional acoustic propagation through random scattering of the acoustic waves. We measure the complex acoustic field with a scanning optical interferometer, and extract the angle-dependent velocities of surface and bulk modes using
Fourier domain analysis. Our method could be used for the optimization of GHz-range classical and
quantum acoustic devices by identifying surface and bulk modes.
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All authors T.A. Steenbergen, M.M. Wohlfarth, P.E. Veefkind, M. Fisicaro, W. Löffler
Biological Physics · Cross-listed from cond-mat.stat-mech
Starvation suppression in dense scale-free metabolic networks: Dynamical mean-field analysis of catalytic reaction networks
Kota Mitsumoto and Shuji Ishihara
Cellular metabolic networks exhibit scale-free
topologies with power-law degree distributions across diverse organisms. Although such
topologies are often linked to mutational robustness and evolutionary advantage, their role in metabolic dynamics remains unclear. Using dynamical mean-field theory, we derive an exact solution for an intracellular catalytic reaction model on dense random networks with arbitrary degree distributions. We show that the metabolic-starvation transition observed under nutrient-poor conditions for homogeneous degree distributions disappears when the out-degree distribution is scale-free. We also show a power-law in-degree distribution of the underlying catalytic reaction network gives rise to a power-law distribution of biomolecular abundances with the same exponent. Large-scale numerical simulations validate these predictions. Our results provide a theoretical framework linking network
topology and metabolic dynamics.
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Physics and Society
Understanding friendship formation with explainable machine learning
María Pereda
Understanding the formation of social ties requires disentangling the roles of individual traits and local network structure. We analyse signed social relationships among 3,395 students using an interpretable machine learning model – the Explainable Boosting Machine (EBM) – to predict link polarity from individual attributes (prosociality, cognitive reflection, and gender) and a structural metric, triadic influence. Our results show that triadic influence overwhelmingly dominates link prediction, confirming that local network structure is the primary driver of social relationships. Nevertheless, a small subset of links (0.24%) is primarily explained by individual-level traits. A detailed characterisation of this subset reveals that these links do not arise from distinct structural conditions, but rather correspond to weaker and less structurally embedded relationships. In particular, they are more likely to be negative ties and exhibit lower levels of structural balance, whereas triadic-dominant links are strongly associated with positive relationships and highly balanced configurations. Furthermore, we find that links without indirect structural paths are not explained by individual traits, but by the absence of structural reinforcement itself. These findings support a layered view of social tie formation, in which structural mechanisms dominate globally, while individual-level effects emerge in specific, less constrained contexts. More broadly, our work highlights the value of explainable machine learning for uncovering the mechanisms underlying social network formation.
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Fluid Dynamics
Modeling subgrid scale production rates on complex meshes using graph neural networks
Priyabrat Dash, Mathis Bode and Konduri Aditya
Large-eddy simulations (LES) require closures for filtered production rates because the resolved fields do not contain all correlations that govern chemical source terms. We develop a graph neural network (GNN) that predicts filtered species production rates on non-uniform meshes from inputs of filtered mass fractions and temperature. Direct numerical simulations of turbulent premixed hydrogen-methane jet flames with hydrogen fractions of 10%, 50%, and 80% provide the
dataset. All fields are Favre filtered with the filter width matched to the operating mesh, and learning is performed on subdomain graphs constructed from mesh-point connectivity. A compact set of reactants, intermediates, and products is used, and their filtered production rates form the targets. The model is trained on 10% and 80% blends and evaluated on the unseen 50% blend to test cross-composition generalization. The GNN is compared against an unclosed reference that evaluates rates at the filtered state, and a convolutional neural network baseline that requires remeshing. Across in-distribution and out-of-distribution cases, the GNN yields lower errors and closer statistical agreement with the reference data. Furthermore, the model demonstrates robust generalization across varying filter widths without retraining, maintaining bounded errors at coarser spatial resolutions. A backward facing step configuration further confirms prediction efficacy on a practically relevant geometry. These results highlight the capability of GNNs as robust data-driven closure models for LES on complex meshes.
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Applied Physics · Cross-listed from cs.CE
A computational framework to predict the spreading of Alzheimer's disease
A. Vazquez-Palomo, C. Betegón, J. Weickenmeier et al. (+1 more)
Alzheimer's disease is characterised by the spreading of misfolded
proteins and progressive structural changes in the
brain. Despite significant clinical research, understanding how microscopic
protein dynamics translate into macroscopic tissue degeneration remains a major challenge. In this work, we present a three-dimensional, finite element-based computational framework to model disease progression by combining multi-
protein transport and
brain tissue deformation within anatomically realistic geometries. The propagation of toxic tau and amyloid-beta
proteins is described using reaction-
diffusion equations of the Fisher-Kolmogorov type, incorporating prion-like growth mechanisms and anisotropic transport along white matter fibre tracts.
Brain atrophy is represented through a hyperelastic constitutive model driven by
protein-dependent volume loss. Subject-specific simulations are achieved through an automated preprocessing pipeline that generates finite element meshes and reconstructs axonal orientation fields from medical imaging data. The model reproduces key morphological patterns observed in Alzheimer's disease and shows good quantitative agreement with longitudinal imaging measurements. Overall, the proposed framework offers an extensible computational platform for studying Alzheimer's disease progression across subject-specific
brain geometries. The models developed, including the image processing framework (BrainImage2Mesh) and the coupled bio-chemo-mechanical COMSOL finite element implementation, are made freely available to download at https://mechmat.web.ox.ac.uk/codes.
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All authors A. Vazquez-Palomo, C. Betegón, J. Weickenmeier, E. Martínez-Pañeda
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
First-principle study of the influence of hydroxyapatite on magnesium surfaces
Anthony Veit Berg, Ablai Forster, Tim Hansson et al. (+3 more)
Hydroxyapatite (HA) on a magnesium (Mg) surface is studied using density functional theory, to help understand the effect of HA coating and alloying in the surfaces of Mg-based biodegradable implants. We determine the adsorption energies and structural changes of a single layer of HA on pure Mg(0001) and on sparsely calcium (Ca) or zinc (Zn) doped Mg(0001) and find that both Zn and Ca doping improves the adsorption, except in a few positions of HA relative to the dopant position. All adsorption configurations, whether with pure or doped Mg surfaces, show deformation of the surface and HA layer. For Ca doping, we found that for a certain adsorption configuration, the dopant Ca atom moves out of the Mg surface and into the HA layer, leaving behind a Mg vacancy in the top layer of the Mg surface. Plots of electron density changes show that electrons accumulate around the Ca dopant and the neighboring Mg atoms, while in Zn doping this is less pronounced. Overall, our results demonstrate that the dopant choice and relative position of HA influence the interaction between HA and Mg-surfaces, and affect both adsorption energies and atomic and electronic structures.
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All authors Anthony Veit Berg, Ablai Forster, Tim Hansson, Alexandra J. Jernstedt, Emmy Salminen, Elsebeth Schröder
Computational Physics
A distribution-free lattice Boltzmann method for compartmental reaction-diffusion systems with application to epidemic modelling
Alessandro De Rosis
We introduce a distribution-free
lattice Boltzmann formulation for general compartmental reaction–
diffusion systems arising in mathematical epidemiology. The proposed scheme, termed a single-step simplified
lattice Boltzmann method (SSLBM), evolves directly macroscopic compartment densities, eliminating the need for particle distribution functions and explicit streaming operations. This yields a compact and computationally efficient framework while retaining the kinetic consistency of
lattice Boltzmann methodologies. The approach is applied to a SEIRD (Susceptible-Exposed-Infected-Recovered-Deceased) reaction-
diffusion model as a representative case. The resulting discrete
evolution equations are derived and shown to recover the target macroscopic dynamics. The method is systematically validated against a fourth-order finite difference reference solution and compared with a standard BGK
lattice Boltzmann formulation. Numerical results demonstrate that the SSLBM consistently improves accuracy across all compartments and norms. The error reduction is robust with respect to both the basic reproduction number and
diffusion strength, typically ranging between factors of approximately two and five depending on the regime. In particular, the method shows enhanced control of localised errors in regimes characterised by strong nonlinear coupling and steep spatial gradients. Our findings indicate that the proposed formulation provides an accurate and efficient alternative to classical
lattice Boltzmann approaches for reaction-
diffusion systems, with particular advantages in stiff and nonlinear epidemic dynamics.
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Optics
Programmable spectral phase transfer to the ultraviolet by gas-filled-fibre four-wave mixing
Linshan Sun, Hao Zhang, Cameron Leary et al. (+2 more)
Programmable shaping of femtosecond ultraviolet (UV) pulses is still much less flexible than at visible and near-infrared wavelengths, mainly because direct UV modulators remain limited in bandwidth, throughput and damage threshold. Here we show that dispersive four-wave mixing (DFWM) in a gas-filled hollow-cappillary fibre (HCF) can transfer programmed spectral phase from the near infrared (NIR) to the UV without relying a narrowband pump. A shaped NIR signal at 1032 nm and a chirped 516-nm pump generate a 344-nm idler, which is characterized with transient-grating frequency-resolved optical gating (TG FROG). As a
benchmark, second-order dispersion (SOD) applied to the signal is quantitatively reproduced in the idler. We then demonstrate the transfer of two nontrivial phase patterns: a localized nominal {\pi}-step and a moderate sinusoidal modulation. In the {\pi}-step case, a step imposed on the long-wavelength side of the signal appears on the short-wavelength side of the idler, consistent with the 2*pump - 1*signal mixing relation. In the sinusoidal case, the periodic phase produces a split temporal waveform in both signal and idler. These results show that gas-filled HCF DFWM can act as a practical spectral-phase transducer from the NIR to the UV, while also revealing a trade-off between conversion efficiency and phase-transfer fidelity.
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All authors Linshan Sun, Hao Zhang, Cameron Leary, Alex Amador, Sergio Carbajo
Medical Physics
Characterizing the Radiation Dose to Measurement Accuracy Relationship across Multiple Metrics in Opportunistic Chest CT
Boyuan Li, Carolyn C. Chang, Jake J. Kim et al. (+5 more)
Objectives: This study aims to characterize the dose-performance relationship for opportunistic CT and disentangle the contributions of
segmentation failure and dose-dependent HU bias to performance degradation. Methods: Simulated low-dose CT images at 1-75% of full dose were generated from 50 paired full- and low-dose chest CT scans. An independent
dataset of 22 paired PCCT acquisitions at lung cancer screening (LCS) and chest x-ray-equivalent (CXR) dose levels provided parallel real-world evaluation. Multiple quantitative disease metrics were obtained using deep learning-based
segmentation followed by quantitative metric extraction. Classification performance was evaluated against full-dose reference standards, with additional analyses isolating the contributions of
segmentation error and HU bias. Agreement between dose levels was assessed using Bland-Altman and correlation analyses. Results: Mean HU metrics maintained classification accuracy to CXR-equivalent dose (3%); bias correction improved accuracy from 88% to 96% for hepatic steatosis and from 84% to 90% for sarcopenia. Trabecular bone attenuation maintained 98% accuracy at LCS dose. Volume metrics (cardiomegaly) achieved 94% accuracy at CXR-equivalent dose. Threshold-based metrics required LCS dose for reliable classification; bias correction improved accuracy from 58% to 92%. Coronary artery calcification scoring reached 96% accuracy at LCS dose. In both Mayo and PCCT
datasets, agreement analyses demonstrated strong correlation for all metrics except coronary artery calcification. Conclusions: Opportunistic CT is feasible at reduced dose levels though it becomes less robust at ultra-low doses. Distinct failure modes are caused by HU bias or
segmentation failure and depend on the clinical task. Providers should be aware of these task-specific limitations when designing opportunistic screening programs.
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All authors Boyuan Li, Carolyn C. Chang, Jake J. Kim, Jia Wang, Justin R Tse, Natalie S. Lui, Haiwei Henry Guo, Adam S. Wang
Optics
Mid-infrared reconfiguration of population flow in lanthanide nanocrystals
Xinyang Yu, Yin Huang, Karin Yamamura et al. (+15 more)
Converting mid-infrared (MIR) radiation to visible or near-infrared wavelengths is essential for imaging and sensing, yet achieving sensitive, low-power, and scalable detection remains challenging. Lanthanide nanocrystals provide an alternative through ratiometric luminescence but are typically constrained by Boltzmann statistics, which tie population distributions to
lattice temperature and limit signal contrast. Here we show that MIR irradiation rebalances dissipative relaxation pathways, driving lanthanide emitters into a non-Boltzmann steady state that enables non-thermal control of population distributions. This allows emission behaviors inaccessible under thermal
equilibrium. We exploit this regime to achieve linear MIR detection with respect to MIR power across 6.8 to 8.6 micrometers. The ratiometric response is intrinsically independent of the pump power, enabling operation at an ultralow excitation power of 10 uW, several orders of magnitude lower than conventional approaches. Using standard silicon photodetectors, we then demonstrate room-temperature MIR imaging with detection limits approaching 4 nW um-2. Our results establish lanthanide nanoparticles as an efficient platform for MIR conversion and sensing in nanophotonic systems.
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All authors Xinyang Yu, Yin Huang, Karin Yamamura, Chenyi Wang, Lei Ding, Mehran Kianinia, Yang Yu, Jiyun Kim, Baolei Liu, Xiaoxue Xu, Otto Cranwell Schaeper, Yue Bian, Lan Fu, Guochen Bao, Qian Peter Su, Fan Wang, Igor Aharonovich, Chaohao Chen
Physics and Society
On the existence of fair zero-determinant strategies in the periodic prisoner's dilemma game
Ken Nakamura and Masahiko Ueda
Stochastic games are a framework for investigating long-term interdependence of multi-
agent systems with environmental feedback. When the number of environmental states is one, they are reduced to repeated games. In repeated games, zero-determinant (ZD) strategies attract much
attention in evolutionary game theory, since they can unilaterally control payoffs. Especially, fair ZD strategies unilaterally equalize the payoff of the focal player and the average payoff of the opponents, and they were found in several games including the social dilemma games. Although the existence condition of ZD strategies in repeated games was specified, its extension to
stochastic games remains largely unclear. Here, we investigate the existence condition of fair ZD strategies in the periodic prisoner's dilemma game, which is one of the simplest
stochastic games. The periodic prisoner's dilemma game consists of two environmental states and the two states alternate deterministically. Whereas each stage game is not necessarily the prisoner's dilemma game, the whole game can be regarded as the prisoner's dilemma game on average. We show that fair ZD strategies do not necessarily exist in the periodic prisoner's dilemma game, in contrast to the repeated prisoner's dilemma game. Furthermore, we also prove that the Tit-for-Tat strategy, which imitates the opponent's action, is not necessarily a fair ZD strategy in the periodic prisoner's dilemma game, whereas the Tit-for-Tat strategy is always a fair ZD strategy in the repeated prisoner's dilemma game. Our results highlight difference between ZD strategies in the periodic prisoner's dilemma game and those in the standard repeated prisoner's dilemma game.
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Geophysics · Cross-listed from stat.ML
On the role of memorization in learned priors for geophysical inverse problems
Ali Siahkoohi and Davide Sabeddu
Learned priors based on deep generative models offer data-driven regularization for seismic inversion, but training them requires a
dataset of representative subsurface models – a resource that is inherently scarce in geoscience applications. Since the training objective of most generative models can be cast as maximum likelihood on a finite
dataset, any such model risks converging to the empirical distribution – effectively memorizing the training examples rather than learning the underlying geological distribution. We show that the posterior under such a memorized prior reduces to a reweighted empirical distribution – i.e., a likelihood-weighted lookup among the stored training examples. For
diffusion models specifically, memorization yields a Gaussian mixture prior in closed form, and linearizing the forward operator around each training example gives a Gaussian mixture posterior whose components have widths and shifts governed by the local Jacobian. We validate these predictions on a stylized inverse problem and demonstrate the consequences of memorization through
diffusion posterior sampling for full waveform inversion.
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Applied Physics
Widefield Nanodiamond Quantum Sensing Based on Light-Sheet Microscopy
Shuo Wang, Ming-Zhong Ai, Jing-Wei Fan et al. (+4 more)
Nanodiamonds containing nitrogen-vacancy (NV) centers are promising
quantum sensors for biological applications thanks to their sub-micron spatial resolution, biocompatibility, and versatile multi-modal responses. However, the optically detected
magnetic resonance (ODMR) measurement requires
laser irradiation, creating a trade-off between high-throughput and low phototoxicity for applications in live
cells. Here to address this challenge we develop a widefield
quantum sensing method based on light-sheet microscopy (LSM), in which the sample is illuminated by a vertically movable
laser sheet and the fluorescence is collected along the vertical axis that is orthogonal to the light sheet. This LSM-ODMR system is demonstrated to feature high throughput sensing due to the wide-field configuration, fast three-dimensional imaging and sensing due to the vertical mobility of the light sheet, enhanced sensitivity due to suppression of out-of-focus background fluorescence, and low phototoxicity for bio-sensing due to elimination of out-of-focus illumination. This LSM-based widefield nanodiamond sensing provides an approach for biological studies with low phototoxicity, offering three-dimensional and multi-modal sensing capability.
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All authors Shuo Wang, Ming-Zhong Ai, Jing-Wei Fan, Junchen Ye, Chao Lin, Quan Li, Ren-Bao Liu
Atmospheric and Oceanic Physics
Data-driven ensemble prediction of the global ocean
Qiusheng Huang, Xiaohui Zhong, Anboyu Guo et al. (+3 more)
Data-driven models have advanced deterministic ocean
forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble
forecasting system for the global ocean, providing 5-day forecasts on a global 1{\deg} grid up to 365 days for sea-surface temperature, sea-surface height, subsurface temperature, salinity and ocean currents. Rather than relying on repeated integration of computationally expensive numerical models, FuXi-ONS learns physically structured perturbations and incorporates an atmospheric encoding module to stabilize long-range forecasts. Evaluated against GLORYS12 reanalysis, FuXi-ONS improves both ensemble-mean skill and probabilistic forecast quality relative to deterministic and noise-perturbed baselines, and shows competitive performance against established seasonal forecast references for SST and Niño3.4 variability, while running orders of magnitude faster than conventional ensemble systems. These results provide a strong example of machine learning advancing a core problem in ocean science, and establish a practical path toward efficient probabilistic ocean
forecasting and
climate risk assessment.
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All authors Qiusheng Huang, Xiaohui Zhong, Anboyu Guo, Ziyi Peng, Lei Chen, Hao Li
Computational Physics · Cross-listed from cs.LG
Neural Uncertainty Principle: A Unified View of Adversarial Fragility and LLM Hallucination
Dong-Xiao Zhang, Hu Lou, Jun-Jie Zhang et al. (+2 more)
Adversarial vulnerability in
vision and
hallucination in large
language models are conventionally viewed as separate problems, each addressed with modality-specific patches. This study first reveals that they share a common
geometric origin: the input and its loss gradient are conjugate observables subject to an irreducible uncertainty bound. Formalizing a Neural Uncertainty Principle (NUP) under a loss-induced state, we find that in near-bound regimes, further compression must be accompanied by increased sensitivity dispersion (
adversarial fragility), while weak
prompt-gradient coupling leaves generation under-constrained (
hallucination). Crucially, this bound is modulated by an input-gradient correlation channel, captured by a specifically designed single-backward probe. In
vision, masking highly coupled components improves robustness without costly
adversarial training; in
language, the same prefill-stage probe detects
hallucination risk before generating any answer
tokens. NUP thus turns two seemingly separate failure taxonomies into a shared uncertainty-budget view and provides a principled lens for reliability analysis. Guided by this NUP theory, we propose ConjMask (masking high-contribution input components) and LogitReg (logit-side regularization) to improve robustness without
adversarial training, and use the probe as a decoding-free risk signal for LLMs, enabling
hallucination detection and
prompt selection. NUP thus provides a unified, practical framework for diagnosing and mitigating boundary anomalies across perception and generation tasks.
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All authors Dong-Xiao Zhang, Hu Lou, Jun-Jie Zhang, Jun Zhu, Deyu Meng
Fluid Dynamics · Cross-listed from math.NA
An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media
V. S. Maduri and K. B. Nakshatrala
Porous materials – natural or engineered – often exhibit dual pore-network structures that govern processes such as mineral exploration and hydrocarbon recovery from tight shales. Double porosity/permeability (DPP) mathematical models describe incompressible fluid flow through two interacting pore networks with inter-network mass exchange. Despite significant advances in numerical methods, there remains a need for computational frameworks that enable rapid
forecasting, data assimilation, and reliable inverse analysis. To address this, we present a physics-informed neural network (PINN) framework for forward and inverse modeling of DPP systems. The proposed approach encodes the governing equations in mixed form, along with boundary conditions, directly into the loss function, with adaptive weighting strategies to balance their contributions. Key features of the framework include adaptive weight tuning, dynamic collocation point selection, and the use of shared trunk neural architectures to efficiently capture the coupled behavior of the dual pore networks. It is inherently mesh-free, making it well-suited for complex geometries typical of porous media. It accurately captures discontinuities in solution fields across layered domains without introducing spurious oscillations commonly observed in classical finite element formulations. Importantly, the framework is well-suited for inverse analysis, enabling robust parameter identification in scenarios where key physical quantities – such as the mass transfer coefficient in DPP models – are difficult to measure directly. In addition, a systematic convergence analysis is provided to rigorously assess the stability, accuracy, and reliability of the method. The effectiveness and computational advantages of the approach are demonstrated through a series of representative numerical experiments.
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Fluid Dynamics
Direct Numerical Simulations of Ice-Ocean Boundary Turbulence
Ken X. Zhao, Tomas Chor, Eric Skyllingstad et al. (+3 more)
Turbulent heat and freshwater transport at ice-ocean interfaces controls glacier and iceberg melt rates, yet the underlying physics remains poorly constrained. Parameterizations that assume shear boundary layer scaling are commonly used, which neglects meltwater buoyancy-driven convective processes. Using Direct Numerical Simulations with realistic salt diffusivity, which is critical for representing the thin solutal boundary layer (deltaS ~ 0.4 mm) and resulting convective instabilities, we investigate ice-ocean boundary layer
turbulence across varying temperature, salinity, stratification, external velocity, and interfacial slope angles. Our simulations agree with laboratory measurements of melt rate and interfacial temperature. In the absence of external flows, we find no transition from buoyancy-controlled to shear-controlled regimes and convection is important even at near-horizontal slopes. External shear becomes significant only when it is strong enough to thin the thermal and solutal boundary layers, which starts influence melting substantially above background flow speeds of 5 cm/s. Understanding how shear and convection compete to determine the ice-ocean diffusive boundary layer enables accurate melt rate predictions across the parameter space relevant to ice shelves and marine-terminating glaciers.
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All authors Ken X. Zhao, Tomas Chor, Eric Skyllingstad, Jonathan Nash, Madelaine Rosevear, Craig McConnochie
Physics and Society
Discovering Governing Spatial Interaction Equations in Dynamic Urban Systems
Zhongfu Ma and Di Zhu
Governing equations are fundamental for describing and predicting dynamic urban geographic systems. Unlike physical systems guided by first principles, urban spatiotemporal phenomena emerge from coupled geographic processes that lack deterministic theoretical foundations, making the discovery of governing equations elusive and largely heuristic. Spatiotemporal dynamics in urban systems are often observed as sequential snapshots of spatial distribution, while the spatial interactions, or the flows, that cause such dynamics are often implied or unknown. In this study, we propose a general differential equation formalism that decomposes urban dynamics into a spatial interaction process and a self-dynamic component. Building on this formalism, we introduce the Urban Discovery Framework, which integrates hypothesis generation, neural fitting, and governing equation identification for discovering the governing equations of spatial interaction. U-Discovery leverages Large
Language Models and literature-based
reasoning to propose differential equation candidates. Each candidate was calibrated from the observed spatiotemporal dynamics using a neural fitting method. The candidates are evaluated and ranked based on the fitting error and mathematical complexity. Empirical experiments examine real-world active population dynamics in Hennepin County, Minnesota. Drawing on both literature-derived scientific priors and broader knowledge from LLMs, U-Discovery proposes original candidate equations and identifies those that best reproduce observed population dynamics. The discovered equations show an ability to recover the underlying mobility flows, while their varying performance across spatial scales suggests that different interaction mechanisms may dominate at different scales.
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Applied Physics
Spectral and Small-Signal Electroluminescence Analysis of Carrier Dynamics in Dual-Color InGaN/GaN Light-Emitting Diodes
Xuefeng Li, Rob Armitage and Daniel Feezell
We study carrier transport, distribution, and recombination in dual-color c-plane InGaN/GaN LEDs using spectral analysis and small-signal electroluminescence (SSEL). The emissions from green and blue
quantum wells (QWs) were experimentally separated and analyzed. Spectral analysis and SSEL independently demonstrate that emission from the green QW is dominant at low current densities due to its narrower bandgap, while emission from the blue QW is more significant at higher current densities due to its reduced
quantum confined stark effect (QCSE) and larger wavefunction overlap. In addition, we demonstrate that the carrier recombination in the QWs is non-uniform, with carrier transport dramatically affecting the carrier distribution between QWs and the recombination in a specific QW. Finally, we also show that the effective active region in these V-pit-engineered InGaN/GaN LEDs is roughly 2 to 3 QWs on the p-GaN side, with limited interwell carrier transport and recombination in additional QWs.
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