Optics
Physics-informed Neural Operators for Predicting 3D Electromagnetic Fields Transformed by Metasurfaces
Orkun Furat, Vinay Chakravarthi Gogineni, Henrik Bindslev et al. (+1 more)
Metasurfaces, typically realized as arrays of nanopillars, transform electromagnetic (EM) fields depending on their geometry and spatial arrangement. For solving the inverse problem of designing new metasurfaces that transform EM fields in a desirable manner, it is often necessary to explore large design spaces through full-wave simulations that can be computationally demanding. In this work, we demonstrate that neural operators, which are artificial neural network architectures designed to learn operators between function spaces, can effectively approximate the differential operators underlying Maxwell's equations, enabling their use as fast and accurate 3D surrogate models that can predict 3D EM fields transformed by metasurfaces. To calibrate neural operators, we generate synthetic training data consisting of 3D metasurface geometries together with their associated 3D EM fields obtained by numerically solving Maxwell's equations. Using the generated synthetic data, we train physics-informed neural operators to minimize physical inconsistencies of predicted EM fields by incorporating residuals that capture deviations from Maxwell's equations. We observe that a training
dataset consisting of fewer than 5000 examples already suffices to achieve reasonable results. In particular, our experiments show that the resulting 3D surrogate model achieves high predictive performance across a wide range of metasurface geometries, including types of structures not encountered during training. Notably, it predicts diffraction efficiencies with relative errors of 3.9 % and provides a 67-fold speedup compared to conventional 3D simulations. Overall, once trained, our 3D surrogate model can rapidly predict EM fields for previously unseen metasurface geometries, which can facilitate efficient gradient-based design of nanostructured materials for EM wave control.
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All authors Orkun Furat, Vinay Chakravarthi Gogineni, Henrik Bindslev, Esmaeil S. Nadimi
Chemical Physics
Learning continuous state of charge dependent thermal decomposition kinetics for Li-ion cathodes using Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs)
Benjamin C. Koenig and Sili Deng
Thermal runaway in lithium-ion batteries is strongly influenced by the state of charge (SOC). Existing predictive models typically infer scalar kinetic parameters at a full SOC or a few discrete SOC levels, preventing them from capturing the continuous SOC dependence that governs exothermic behavior during abuse conditions. To address this, we apply the Kolmogorov-Arnold Chemical Reaction Neural Network (KA-CRNN) framework to learn continuous and realistic SOC-dependent exothermic cathode-electrolyte interactions. We apply a physics-encoded KA-CRNN to learn SOC-dependent kinetic parameters for cathode-electrolyte decomposition directly from differential scanning calorimetry (DSC) data. A mechanistically informed reaction pathway is embedded into the network architecture, enabling the activation energies, pre-exponential factors, enthalpies, and related parameters to be represented as continuous and fully interpretable functions of the SOC. The framework is demonstrated for NCA, NM, and NMA cathodes, yielding models that reproduce DSC heat-release features across all SOCs and provide interpretable insight into SOC-dependent oxygen-release and phase-transformation mechanisms. This approach establishes a foundation for extending kinetic parameter dependencies to additional environmental and electrochemical variables, supporting more accurate and interpretable thermal runaway prediction and monitoring.
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Fluid Dynamics
Wake instability past a sphere settling in a strongly stratified flow
Chang-Fan Mo, Matthieu J. Mercier, Jacques Magnaudet et al. (+1 more)
The wake of a body moving across the isopycnals of a strongly stratified fluid is characterized by the presence of an intense jet which, under certain circumstances, may become unstable. To get insight into the phenomenology of this instability and the underlying mechanisms, we conduct fully-resolved three-dimensional time-dependent simulations of the flow past a rigid sphere settling steadily through a linearly stratified fluid over a wide range of flow parameters which include the case of salt-stratified water. Results reveal a rich dynamics characterized by distinct wake
symmetries, vortical structures and transverse force signatures. Simulations evidence the existence of varicose and sinuous instability modes that arise from distinct physical mechanisms. Thanks to several metrics, we build a phase map delineating the bounds of each instability regime as a function of the three control parameters of the problem, namely the Froude, Reynolds and Prandtl numbers. We show that the instability mechanism results from a subtle interplay between baroclinic vorticity generation,
vortex tilting and radial transport of the mean density gradient within the jet.
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All authors Chang-Fan Mo, Matthieu J. Mercier, Jacques Magnaudet, Jie Zhang
Computational Physics · Cross-listed from cs.CE
Nonparametric Stochastic Subspaces via the Bootstrap Method for Characterizing Model Error
Akash Yadav and Ruda Zhang
Reliable forward uncertainty quantification in engineering requires methods that account for aleatory and epistemic uncertainties. In many applications, epistemic effects arising from uncertain parameters and model form dominate prediction error and strongly influence engineering decisions. Because distinguishing and representing each source separately is often infeasible, their combined effect is typically analyzed using a unified model-error framework. Model error directly affects model credibility and predictive reliability; yet its characterization remains challenging. To address this need, we introduce a bootstrap-based
stochastic subspace model for characterizing model error in the
stochastic reduced-order modeling framework. Given a snapshot
matrix of state vectors, the method leverages the empirical data distribution to induce a sampling distribution over principal subspaces for reduced order modeling. The resulting
stochastic model enables improved characterization of model error in computational mechanics compared with existing approaches. The method offers several advantages: (1) it is assumption-free and leverages the empirical data distribution; (2) it enforces linear constraints (such as boundary conditions) by construction; (3) it requires only one hyperparameter, significantly simplifying the training process; and (4) its
algorithm is straightforward to implement. We evaluate the method's performance against existing approaches using numerical examples in computational mechanics and structural dynamics.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Polaritonic Bloch's Theorem beyond the Long-Wavelength Approximation
Giovanna Bruno, Rosario Roberto Riso, Henrik Koch et al. (+1 more)
Cavity
quantum electrodynamics provides a powerful tool to manipulate material properties, yet it remains a matter of debate whether and how quantized fields affect the periodicity of
crystals. Here, we extend Bloch's
theorem to
crystals under strong light-matter coupling, revealing that polariton quasiparticles preserve
lattice periodicity. We introduce a general framework to incorporate multimode cavity fields in a simple and tractable way, showing that additional modes contribute small energy corrections noticeable only at low frequencies. Within the single-
photon approximation, these contributions reduce to a spatially uniform effective field in the
crystal plane, providing a formal justification for the single-mode and long-wavelength approximations commonly used in molecular polaritonics. Together, these results establish a rigorous framework for describing polaritonic states in crystalline solids.
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All authors Giovanna Bruno, Rosario Roberto Riso, Henrik Koch, Enrico Ronca
Fluid Dynamics
A reduced model for droplet dynamics with interfacial viscosity
Fabio Guglietta, Diego Taglienti and Mauro Sbragaglia
We propose an extension of the phenomenological Maffettone-Minale (MM) model (P.L. Maffettone and M. Minale, J. Non-Newton. Fluid Mech. 78, 227-241 (1998)) to describe the time-dependent deformation of a droplet with interfacial viscosity in a shear flow. The droplet, characterised by surface tension $\sigma$, is spherical at rest with radius $R$ and deforms into an ellipsoidal shape under a shear flow of rate $G$, described by a symmetric second-order morphological tensor $\boldsymbol{S}$. In addition to surface tension, the extended MM (EMM) model incorporates interfacial shear and dilatational viscosities, $\mu_s$ and $\mu_d$, through the corresponding Boussinesq numbers $\mbox{Bq}_s=\mu_s/\mu R$ and $\mbox{Bq}_d=\mu_d/\mu R$, where $\mu$ is the bulk viscosity. A central goal of this work is to quantify the parameter range over which the EMM model provides a realistic description of droplet deformation, as a function of the capillary number Ca$=\mu R G/\sigma$ and the Boussinesq numbers. To this end, model predictions are systematically compared with fully resolved numerical simulations.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
From Complex Magnetic Ground States to Magnetocaloric Effects: A Review of Rare Earth R$_2$In Intermetallic Compounds
Anis Biswas, Ajay Kumar, Prashant Singh et al. (+1 more)
R2In (R = rare earth) intermetallics exhibit unusual
magnetic and magnetocaloric properties, driven by subtle electronic effects,
lattice distortions, and
spin-
lattice coupling. Most of these binary compounds adopt the hexagonal Ni2In-type structure at room temperature, with Eu2In and Yb2In stabilizing in the orthorhombic Co2Si-type
lattice. Lighter lanthanide compounds Eu2In, Nd2In, and Pr2In undergo first-order
magnetic transitions with negligible hysteresis and minimal
lattice volume change and exhibit
giant cryogenic magnetocaloric effects, while heavy lanthanide R2In compounds including Gd2In show second-order transitions with moderate magnetocaloric effect. No lanthanide-based R2In compound exhibits
symmetry-breaking structural transition, while Y2In transforms from hexagonal to orthorhombic structure near 250 K. Secondary low-temperature transitions, including
spin reorientation or
antiferromagnetic ordering, further enrich the
magnetic phase landscape in these compounds. Integrating theoretical descriptors such as charge-induced strain and electronic structure provides predictive insight into phase stability and magnetocaloric performance, guiding the design of rare-earth intermetallics with tunable
magnetic properties for cryogenic applications
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All authors Anis Biswas, Ajay Kumar, Prashant Singh, Yaroslav Mudryk
Plasma Physics
T. Estrada, E. Maragkoudakis, D. Carralero et al. (+13 more)
The effect of
magnetic islands on
plasma flow and
turbulence has been experimentally investigated in the stellarator W7-X.
Magnetic configurations with the 5/5
magnetic island positioned at the
plasma edge, inside the last closed flux surface, are studied. The main diagnostic used in the present work is a V-band Doppler reflectometer that allows the measurement of the perpendicular
plasma flow and density fluctuations with good spatial resolution. A characteristic signature of the 5/5
magnetic island is clearly detected in the perpendicular flow profile. The comparison of the experimental flow and the neoclassically driven $E\times B$ flow indicates that the island contribution to the flow is maximum at the island boundaries and close to zero at the island O-point. Besides, a reduction in the density fluctuation level is found nearby the island O-point. The similarities between these observations and those found in other devices and in gyrokinetic simulations are discussed.
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All authors T. Estrada, E. Maragkoudakis, D. Carralero, T. Windisch, J.L Velasco, C. Killer, T. Andreeva, J. Geiger, A. Dinklage, A. Krämer-Flecken, G. A. Wurden, M. Beurskens, S. Bozhenkov, H. Damm, G. Fuchert, E. Pasch
Optics
Methods, trade-offs and opportunities in high-speed optical microscopy for neural voltage imaging
Zhaoqiang Wang, Ruth R. Sims, Sheng Xiao et al. (+6 more)
High-speed optical imaging of dynamic neuronal activity is essential yet challenging in neuroscience. While calcium imaging has been firmly established as a workhorse technique for monitoring neuronal activity, its limited temporal resolution and indirect measurement restrict its ability to capture rapid inhibitory and excitatory events and subthreshold voltage oscillations. In contrast, voltage imaging directly measures membrane potential fluctuations, providing a comprehensive and precise representation of neuronal
circuit dynamics. Recent advancements in voltage-sensitive dyes and, particularly, genetically encoded voltage indicators have significantly enhanced the feasibility of voltage imaging, prompting the development of advanced fluorescence microscopy methods optimized for high-speed acquisition. However, achieving millisecond-scale temporal resolution remains challenging due to inherent trade-offs among imaging speed, spatial resolution, and signal-to-noise ratio. Conventional raster-scanning approaches, including confocal microscopy, are fundamentally limited by their slow frame rates, precluding the capture of rapid neuronal events from multiple
neurons simultaneously. Alternative techniques such as random-access scanning, spatiotemporal multiplexing, and computational optical imaging have successfully addressed these constraints, enabling kilohertz-level imaging of neuronal activity in both two-dimensional and three-dimensional contexts. This review summarizes recent progress in high-speed optical microscopy for voltage imaging and discusses its transformative potential for neuroscience research.
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All authors Zhaoqiang Wang, Ruth R. Sims, Sheng Xiao, Ruixuan Zhao, Ohr Benshlomo, Zihan Zang, Jiamin Wu, Valentina Emiliani, Liang Gao
Instrumentation and Detectors
Performance Characterization of a Plastic-Scintillator Sensor for Fast-Neutron, Thermal-Neutron, and Gamma-Ray Discrimination
Yuhang Liu, Fengpeng An, Guang Luo et al. (+4 more)
Discrimination of fast
neutrons, thermal
neutrons, and $\gamma$ rays in mixed radiation fields is important for radiation monitoring, reactor-related measurements, and background suppression in
nuclear experiments. In this work, we investigate a compact plastic-scintillator sensor composed of EJ276 or EJ200 optically coupled to an EJ426 thermal
neutron screen and read out by a single photomultiplier tube (PMT). The $\gamma$ equivalent energy response of the detector assemblies was calibrated using $^{137}$Cs, $^{22}$Na, and $^{60}$Co sources through Compton edge analysis, and pulse shape discrimination was evaluated with an AmBe
neutron source under different moderator thicknesses. The EJ200+EJ426 assembly provides a well separated discrimination between thermal-
neutron capture events and $\gamma$ dominated events over the measured range, with a figure of merit greater than 5. In contrast, the EJ276+EJ426 assembly produced three identifiable signal populations associated with $\gamma$ rays, fast
neutrons, and thermal
neutrons. These results show that the proposed sensor architecture is a promising compact approach for mixed field radiation applications
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All authors Yuhang Liu, Fengpeng An, Guang Luo, Wei Wang, Xuesong Zhang, Dixiao Lu, Xiaohao Yin
Optics
Guided vortex bullets
Carlos F. Sánchez, Ángel Paredes, Humberto Michinel et al. (+2 more)
By means of the variational method and numerical simulations, we demonstrate the existence of stable 3D nonlinear modes, viz.
vortex “bullets”, in the form of pulsed beams carrying orbital angular momentum, that can self-trap in a 2D waveguiding structure. Despite the attractive self-interaction, which is necessary for producing the bullets (bright
solitons), and which readily leads to the collapse in the 3D setting as well as to spontaneous splitting of
vortex modes, we find a critical value of the trapping depth securing the stabilization of the
vortex bullets. We identify experimental conditions for the creation of these topological modes in the context of coherent optical and matter waves. Collisions between the bullets moving in the unconfined direction are found to be elastic. These findings contribute to the understanding of self-trapping in nonlinear multidimensional systems and suggest new possibilities for the stabilization and control of 3D topological
solitons.
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All authors Carlos F. Sánchez, Ángel Paredes, Humberto Michinel, Boris A. Malomed, José R. Salgueiro
Plasma Physics
Radial electric field and density fluctuations measured by Doppler reflectometry during the post-pellet enhanced confinement phase in W7-X
T. Estrada, D. Carralero, T. Windisch et al. (+13 more)
Radial profiles of density fluctuations and radial electric field, $E_r$, have been measured using Doppler reflectometry during the post-pellet enhanced confinement phase achieved, under different heating power levels and
magnetic configurations, along the 2018 W7-X experimental campaign. A pronounced $E_r$-well is measured with local values as high as -40 kV/m in the radial range $\rho \sim 0.7-0.8$ during the post-pellet enhanced confinement phase. The maximum $E_r$ intensity scales with both
plasma density and Electron Cyclotron Heating (ECH) power level following a similar trend as the
plasma energy content. A good agreement is found when the experimental $E_r$ profiles are compared to simulations carried out using the neoclassical codes DKES and KNOSOS. The density fluctuation level decreases from the
plasma edge toward the
plasma core and the drop is more pronounced in the post-pellet enhanced confinement phase than in reference gas fuelled
plasmas. Besides, in the post-pellet phase, the density fluctuation level is lower in the high iota
magnetic configuration than in the standard one. In order to discriminate whether this difference is related to the differences in the
plasma profiles or in the stability properties of the two configurations, gyrokinetic simulations have been carried out using the codes stella and EUTERPE. The simulation results point to the
plasma profile
evolution after the pellet injection and the stabilization effect of the radial electric field profile as the dominant players in the stabilization of the
plasma turbulence.
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All authors T. Estrada, D. Carralero, T. Windisch, E. Sánchez, J. M. García-Regaña, J. Martínez-Fernández, A. de la Peña, J. L. Velasco, J. A. Alonso, M. Beurskens, S. Bozhenkov, H. Damm, G. Fuchert, R. Kleiber, N. Pablant, E. Pasch
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
First-principles simulation of spin diffusion in static solids using dynamic mean-field theory
Timo Gräßer, Götz S. Uhrig and Matthias Ernst
The dynamics of disordered
nuclear spin ensembles are the subject of
nuclear magnetic resonance studies. Due to the through-space long-range dipolar interaction generically many
spins are involved in the time
evolution, so that exact brute force calculations are impossible. The recently established
spin dynamic mean-field theory (spinDMFT) represents an efficient and unbiased alternative to overcome this challenge. The approach only requires the dipolar couplings as input and the only prerequisite for its applicability is that each
spin interacts with a large number of other
spins. In this article, we show that spinDMFT can be used to describe spectral
spin diffusion in static samples and to simulate zero-
quantum line shapes which eluded an efficient quantitative simulation so far to the best of our knowledge. We perform
benchmarks for two test substances that establish an excellent match with published experimental data. As spinDMFT combines low computational effort with high accuracy, we strongly suggest to use it for large-scale simulations of
spin diffusion, which are important in various areas of
magnetic resonance.
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Chemical Physics · Cross-listed from cs.AI
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du et al. (+53 more)
Large
language models (LLMs) are increasingly applied to scientific research, yet prevailing science
benchmarks probe decontextualized knowledge and overlook the iterative
reasoning, hypothesis generation, and observation interpretation that drive scientific discovery. We introduce a scenario-grounded
benchmark that evaluates LLMs across biology, chemistry, materials, and physics, where domain experts define research projects of genuine interest and decompose them into modular research scenarios from which vetted questions are sampled. The framework assesses models at two levels: (i) question-level accuracy on scenario-tied items and (ii) project-level performance, where models must propose testable hypotheses, design simulations or experiments, and interpret results. Applying this two-phase scientific discovery evaluation (SDE) framework to state-of-the-art LLMs reveals a consistent performance gap relative to general science
benchmarks, diminishing return of scaling up model sizes and
reasoning, and systematic weaknesses shared across top-tier models from different providers. Large performance variation in research scenarios leads to changing choices of the best performing model on scientific discovery projects evaluated, suggesting all current LLMs are distant to general scientific "superintelligence". Nevertheless, LLMs already demonstrate promise in a great variety of scientific discovery projects, including cases where constituent scenario scores are low, highlighting the role of guided exploration and serendipity in discovery. This SDE framework offers a reproducible
benchmark for discovery-relevant evaluation of LLMs and charts practical paths to advance their development toward scientific discovery.
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All authors Zhangde Song, Jieyu Lu, Yuanqi Du, Botao Yu, Thomas M. Pruyn, Yue Huang, Kehan Guo, Xiuzhe Luo, Yuanhao Qu, Yi Qu, Yinkai Wang, Haorui Wang, Jeff Guo, Jingru Gan, Parshin Shojaee, Di Luo, Andres M Bran, Gen Li, Qiyuan Zhao, Shao-Xiong Lennon Luo, Yuxuan Zhang, Xiang Zou, Wanru Zhao, Yifan F. Zhang, Wucheng Zhang, Shunan Zheng, Saiyang Zhang, Sartaaj Takrim Khan, Mahyar Rajabi-Kochi, Samantha Paradi-Maropakis, Tony Baltoiu, Fengyu Xie, Tianyang Chen, Kexin Huang, Weiliang Luo, Meijing Fang, Xin Yang, Lixue Cheng, Jiajun He, Soha Hassoun, Xiangliang Zhang, Wei Wang, Chandan K. Reddy, Chao Zhang, Zhiling Zheng, Mengdi Wang, Le Cong, Carla P. Gomes, Chang-Yu Hsieh, Aditya Nandy, Philippe Schwaller, Heather J. Kulik, Haojun Jia, Huan Sun, Seyed Mohamad Moosavi, Chenru Duan
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Fabrication of (In,Ga)N pseudo-substrates by a three-step growth protocol without ex-situ processing
Huaide Zhang, Aidan F. Campbell, Jingxuan Kang et al. (+3 more)
We fabricate (In,Ga)N pseudo-substrates with a total thickness of ~1 um grown on GaN templates using
plasma-assisted molecular beam epitaxy. In a three-step process, we change growth conditions from N-rich to metal-rich in order to sequentially form a roughened GaN layer, relaxed (In,Ga)N nanostructures, and a coalesced, smooth (In,Ga)N layer. Samples are analyzed by scanning electron and atomic force microscopy, X-ray diffraction, as well as photo- and cathodoluminescence spectroscopy. Compared to a reference layer grown directly on GaN, the pseudo-substrate exhibits a higher In content (~0.3), strain relaxation degree (~80%), narrower photoluminescence linewidth, and larger area fraction of bright regions in cathodoluminescence maps, showing the benefits of the three-step growth protocol. This straightforward approach does not necessitate any ex-situ processing and could enable the scalable fabrication of (In,Ga)N pseudo-substrates for high-efficiency red-emitting (In,Ga)N devices.
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All authors Huaide Zhang, Aidan F. Campbell, Jingxuan Kang, Jonas Laehnemann, Oliver Brandt, Lutz Geelhaar
Optics
Self-Quenching Effect of the Decay of Localized Surface Plasmons: Classical and Quantum Perspectives
Krystyna Kolwas
This study presents a self-consistent,
quantum-informed model for the decay dynamics of localized surface plasmons (LSPs) in spherical metal nanoparticles (NPs), described as plasmonic quasi-particles (PQPs). By bridging classical electrodynamics description for quasi-normal modes (retardation effects included) with a
quantum emitter perspective, this framework provides an analytically tractable description of the damping of the dissipative confined plasmonic systems. In addition to its significance for emission control, the model emphasizes the bosonic characteristics of plasmonic quasi-particles, which are coherent many-electron excitations of the states of quasi-normal modes. Unlike conventional cavity
quantum electrodynamics (CQED), where the emitter and cavity exist as separate systems, a plasmonic quasi-particle functions as a
quantum emitter embedded within a self-created resonant near-field nano-cavity of confined radial fields, sharing the spectral characteristics of the surface transverse-
magnetic (TM) modes, which include nonradiative damping effects resulting from, e.g., ohmic losses in a metal. This work extends
Fermi's Golden Rule to include the coupling between the emission process and the self-generated cavity impact. The derived self-consistent formulation offers analytical expressions for the total damping rates, which demonstrate a size-dependent suppression displayed in higher multipolarity modes attributed to the impact of the self-quenching effect resulting from the coaction of radiative and non-radiative channels.
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Optics
Photonics-Enhanced Graph Convolutional Networks
Yuan Wang and Oleksandr Kyriienko
Photonics can offer a hardware-native route for machine learning (ML). However, efficient deployment of photonics-enhanced ML requires hybrid workflows that integrate optical processing with conventional CPU/GPU based neural network architectures. Here, we propose such a workflow that combines photonic positional
embeddings (PEs) with advanced graph ML models. We introduce a photonics-based method that augments graph convolutional networks (GCNs) with PEs derived from light propagation on synthetic frequency
lattices whose couplings match the input graph. We simulate propagation and readout to obtain internode intensity correlation
matrices, which are used as PEs in GCNs to provide global structural information. Evaluated on Long Range Graph
Benchmark molecular
datasets, the method outperforms baseline GCNs with
Laplacian based PEs, achieving $6.3\%$ lower mean absolute error for regression and $2.3\%$ higher average precision for classification tasks using a two-layer GCN as a baseline. When implemented in high repetition rate photonic hardware, correlation measurements can enable fast feature generation by bypassing digital simulation of PEs. Our results show that photonic PEs improve GCN performance and support optical acceleration of graph ML.
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Optics
Unveiling nonlinearities of electromagnetically induced transparency in a THz metamaterial
Amit Haldar, Shriganesh Prabhu and Shovon Pal
Electromagnetically induced transparency (EIT) in terahertz (THz) metamaterials relies on the coherent coupling between a radiative (bright) mode and a subradiant (dark) mode. Understanding the dynamic interplay between the bright and dark modes holds the key to manipulate the mutual interference and hence the transparency. Here, we use nonlinear 2D-THz spectroscopy to scrutinize the dynamics through nonlinearities of the EIT-like phenomenon in a metamaterial platform that comprises of two coupled resonators. From the temporal profiles of the nonlinear pump-probe and
photon-echo signals, we found that the bright mode relaxation time is almost twice the time for the coherent exchange of energy between the two coupled resonators. The multi-peak nature of
photon-echo signal and the corresponding temporal signatures further provides a direct visualization of the interference between the dressed states that drives the transparency window in our THz metamaterial. A time-resolved density
matrix model accurately describes the observed features, including the cross-peak behavior and the temporal dynamics, establishing the coherent mode coupling as the origin of the transparency window.
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Optics
Assessing the Effect of PCA-Based Dimensionality Reduction on Machine Learning Performance in Hyperspectral Optical Imaging
Parisa Parand and Mahmoud Samadpour
Hyperspectral optical imaging provides rich spectral information for estimating continuous environmental and material parameters; however, its high dimensionality and strong feature correlation pose significant challenges for machine learning models, especially when ground-truth
datasets are limited. In this study, we investigate a hyperspectral
dataset composed of 150 spectral bands with soil moisture as the target variable. To address the curse of dimensionality, Principal Component Analysis (PCA) was employed as a baseline dimensionality reduction technique. The optimal number of principal components was determined to be two, retaining more than 99% of the total variance. This selection was supported by the analysis of the covariance
matrix,
eigenvalue distribution, and the scree plot. Projecting the data onto the first two principal components enabled improved visualization and interpretability compared to the original high-dimensional feature space. The reduced representation also revealed a clearer separation of target values, effectively decreasing data complexity. To evaluate the impact of dimensionality reduction on predictive performance, a Random Forest regression model was trained to estimate soil moisture from the PCA-transformed data. The model achieved a coefficient of determination (R2) of 94.7 %, demonstrating that PCA-based feature reduction can enhance computational efficiency while preserving strong predictive capability in hyperspectral machine learning workflows.
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Applied Physics
Modular Multirotary Joints SPS Concept-Challenges and Design Considerations
Xinbin Hou, Zhengai Cheng, Xin Wang et al. (+1 more)
As a major space based energy infrastructure in response to the challenge of
climate change, space
solar power SSP has attracted extensive international
attention for more than 55 years. Recently, various SSP concepts have been proposed, aiming to efficiently harvest gigawatts of
solar power over Earth geostationary orbit and transmit it to the Earth wirelessly. Based on the multirotary joints
solar power satellite MR SPS concept proposed in 2014, this article presents an updated modular MR SPS concept, together with the challenges and design considerations. The primary objective is to address the challenges associated with long distance dc electric power transmission and exceptionally high precision microwave power transmission. The MMR SPS employs multiple modules, each further including a
solar subpanel and an
antenna subpanel. The
solar subpanel and
antenna subpanel in each module are connected with each other via conductive rotary joints with a much shorter distance compared with that in the MR SPS concept. As a result, lower voltage in electric power transmission is allowed, which significantly reduces the complexity of the system and increases its reliability. Retroreflective beamforming is employed to ensure the accurate steering of the microwave power beam to the ground receiving aperture. For the first time, this article analyzes and validates the performance of using retroreflective beamforming to compensate for
antenna position deviations through a numerical calculation model that includes near field effects. Additionally, this article outlines the transportation and assembly methods, along with the considerations of the full life cycle cost for an MMR SPS.
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All authors Xinbin Hou, Zhengai Cheng, Xin Wang, Changjun Liu
Fluid Dynamics
Mixing induced by Faraday surface waves
Andrés Castillo-Castellanos, Benoît-Joseph Gréa, Antoine Briard et al. (+1 more)
We investigate how surface waves enhance mixing across the interface between two miscible fluids with a small density contrast. Imposing a vertical, time-periodic acceleration, we excite Faraday waves both experimentally and numerically. In systems with a shallow density gradient, these standing waves advect the interface and can trigger secondary instabilities. When driven beyond the linear regime, large Faraday crests collapse to form cavities, injecting bubbles and lighter fluid deep into the heavier layer. Together, these mechanisms gradually homogenize the upper layer, diminish the interfacial density jump, and drive the interface downward until it decouples from surface forcing. We report a non-monotonic mixing rate – first increasing as the interfacial energy barrier lowers, then decreasing as less energy is injected into the weakened surface – revealing a balance between barrier reduction and energy input. Based on these observations, we introduce a one-dimensional model incorporating a turbulent diffusivity coefficient that depends on depth and the internal Richardson number, which captures the qualitative
evolution of the system.
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All authors Andrés Castillo-Castellanos, Benoît-Joseph Gréa, Antoine Briard, Louis Gostiaux
Accelerator Physics
Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models
Guillermo Rodriguez-Llorente, Galo Gallardo, Rodrigo Morant Navascués et al. (+3 more)
The development of
nuclear fusion requires materials that can withstand extreme conditions. The IFMIF-DONES facility, a high-power particle accelerator, is being designed to qualify these materials. A critical testbed for its development is the MuVacAS prototype, which replicates the final segment of the accelerator beamline. Precise regulation of argon gas pressure within its ultra-high
vacuum chamber is vital for this task. This work presents a fully data-driven approach for autonomous pressure control. A Deep Learning Surrogate Model, trained on real operational data, emulates the dynamics of the argon injection system. This high-fidelity digital twin then serves as a fast-simulation environment to train a Deep Reinforcement Learning
agent. The results demonstrate that the
agent successfully learns a control policy that maintains gas pressure within strict operational limits despite dynamic disturbances. This approach marks a significant step toward the intelligent, autonomous control systems required for the demanding next-generation particle accelerator facilities.
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All authors Guillermo Rodriguez-Llorente, Galo Gallardo, Rodrigo Morant Navascués, Nikita Khvatkin Petrovsky, Anderson Sabogal, Roberto Gómez-Espinosa Martín
Medical Physics
Multiscale modeling of blood circulation with cerebral autoregulation and network pathway analysis for hemodynamic redistribution in the vascular network with anatomical variations and stenosis conditions
Jiawei Liu, Atsushi Kanoke, Hidenori Endo et al. (+2 more)
Cerebral hemodynamics is fundamentally regulated by the Circle of Willis (CoW), which redistributes flow through communicating arteries to stabilize perfusion under anatomical variations and vascular stenosis. In this study, we develop a multiscale circulation model by coupling a systemic hemodynamic framework with a cerebral arterial network reconstructed from medical imaging. The model incorporates a cerebral autoregulation mechanism (CAM) and enables quantitative simulation of flow redistribution within the CoW under normal, anatomically varied, and stenotic conditions. Baseline simulations reproduce physiological flow distributions in which communicating arteries remain nearly inactive, showing negligible cross-flow and agreement with clinical measurements. In contrast, anatomical variations reveal distinct collateral activation patterns: the anterior communicating artery (ACoA) emerges as the earliest and most sensitive functional collateral, whereas the posterior communicating arteries (PCoAs) exhibit structure-dependent engagement. Progressive stenosis simulations further demonstrate a transition from a complete CoW to a fetal-type posterior cerebral artery (PCA) configuration, characterized by early ACoA flow reversal followed by ipsilateral PCoA activation, consistent with experimental and transcranial Doppler observations. Finally, a path-based quantitative analysis is introduced to illustrate how the cerebral vascular network dynamically reconfigures collateral pathways in response to structural changes. Overall, the proposed framework provides a physiologically interpretable, image-informed tool for investigating cerebral flow regulation through functional collaterals within the CoW, with potential applications in the diagnosis and treatment planning of cerebrovascular diseases.
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All authors Jiawei Liu, Atsushi Kanoke, Hidenori Endo, Kuniyasu Niizuma, Hiroshi Suito
Computational Physics · Cross-listed from quant-ph
QuantGraph: A Receding-Horizon Quantum Graph Solver
Pranav Vaidhyanathan, Aristotelis Papatheodorou, David R. M. Arvidsson-Shukur et al. (+3 more)
Dynamic programming is a cornerstone of graph-based optimization. While effective, it scales unfavorably with problem size. In this work, we present QuantGraph, a two-stage
quantum-enhanced framework that casts local and global graph-optimization problems as
quantum searches over discrete trajectory spaces. The solver is designed to operate efficiently by first finding a sequence of locally optimal transitions in the graph (local stage), without considering full trajectories. The accumulated cost of these transitions acts as a threshold that prunes the search space (up to 60% reduction for certain examples). The subsequent global stage, based on this threshold, refines the solution. Both stages utilize variants of the Grover-adaptive-search
algorithm. To achieve scalability and robustness, we draw on principles from control theory and embed QuantGraph's global stage within a receding-horizon model-predictive-control scheme. This classical layer stabilizes and guides the
quantum search, improving precision and reducing computational burden. In practice, the resulting closed-loop system exhibits robust behavior and lower overall complexity. Notably, for a fixed query budget, QuantGraph attains a 2x increase in control-discretization precision while still benefiting from Grover-search's inherent quadratic speedup compared to classical methods.
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All authors Pranav Vaidhyanathan, Aristotelis Papatheodorou, David R. M. Arvidsson-Shukur, Mark T. Mitchison, Natalia Ares, Ioannis Havoutis
Optics
Bidirectional Fourier-Enhanced Deep Operator Network for Spatio-Temporal Propagation in Multi-Mode Fibers
Dinesh Kumar Murugan and Nithyanandan Kanagaraj
Ultrashort-pulse propagation in graded-index multimode fibers is a highly nonlinear phenomenon driven by several physical processes. Although conventional numerical solvers can reproduce this behavior with high fidelity, their computational cost limits real-time prediction, rapid parameter exploration, experimental feedback, and especially inverse
retrieval of input fields from measured outputs. In this work, we introduce an operator learning framework that learns both the forward and inverse propagation operators within a single unified architecture. By combining spectral filters for spatio-temporal representations with
Fourier-embedded conditioning on physical parameters, the model functions as a fast surrogate capable of accurately predicting complex field
evolution on previously unseen cases. To our knowledge, this represents one of the first demonstrations of a bidirectional operator-learning framework applied to ultrashort-pulse multimode fiber propagation. The resulting architecture enables orders-of-magnitude speedup over numerical solvers, paving the way for real-time beam diagnostics, data-driven design of complex input fields, and closed-loop spatio-temporal control. Moreover, the same framework can potentially be applied to a wide variety of wave systems exhibiting analogous nonlinear and dispersive effects in optics and beyond.
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Computational Physics · Cross-listed from quant-ph
Energy Inference of Black-Box Quantum Computers Using Quantum Speed Limit
Nobumasa Ishida and Yoshihiko Hasegawa
Cloud-based
quantum computers do not provide users with access to hardware-level information such as the underlying
Hamiltonians, which obstructs the characterization of their physical properties. We propose a method to infer the energy scales of gate
Hamiltonians in such black-box
quantum processors using only user-accessible data, by exploiting
quantum speed limits. Specifically, we reinterpret the Margolus-Levitin and Mandelstam-Tamm bounds as
estimators of the energy expectation value and variance, respectively, and relate them to the shortest time for the processor to orthogonalize a
quantum state. This shortest gate time, expected to lie on the nanosecond scale, is inferred from job execution times measured in seconds by employing gate-time amplification. We apply the method to IBM's superconducting
quantum processor and estimate the energy scales associated with single-, two-, and three-
qubit gates. The order of estimated energy is consistent with typical drive energies in superconducting
qubit systems, suggesting that current gate operations approach the
quantum speed limit. Our results demonstrate that fundamental energetic properties of black-box
quantum computers can be quantitatively accessed through operational time measurements, reflecting the conjugate relationship between time and energy imposed by the uncertainty principle.
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Fluid Dynamics
Latent-space variational data assimilation in two-dimensional turbulence
Andrew Cleary, Qi Wang and Tamer A. Zaki
Starting from limited measurements of a turbulent flow, data assimilation (DA) attempts to estimate all the spatio-temporal scales of motion. Success is dependent on whether the system is observable from the measurements, or how much of the initial turbulent field is encoded in the available measurements. Adjoint-variational DA minimises the discrepancy between the true and estimated measurements by optimising the initial velocity or vorticity field (the `state space'). Here we propose to instead optimise in a lower-dimensional latent space which is learned by implicit rank minimising autoencoders. Assimilating in latent space, rather than state space, redefines the observability of the measurements and identifies the physically meaningful perturbation directions which matter most for accurate prediction of the flow
evolution. When observing coarse-grained measurements of two-dimensional Kolmogorov flow at moderate Reynolds numbers, the proposed latent-space DA approach estimates the full turbulent state with a relative error improvement of two orders of magnitude over the standard state-space DA approach. The small scales of the estimated turbulent field are predicted more faithfully with latent-space DA, greatly reducing erroneous small-scale velocities typically introduced by state-space DA. Furthermore, latent-space DA is demonstrated to be robust to noisy measurements at the range of Reynolds numbers considered. These findings demonstrate that the observability of the system from available data can be greatly improved when turbulent measurements are assimilated in the right space, or coordinates.
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Medical Physics
Female anatomies disguise ECG abnormalities following myocardial infarction: an AI-enabled modelling and simulation study
Hannah Smith, Abhirup Banerjee, Leto Luana Riebel et al. (+4 more)
The electrocardiogram (ECG) is modulated by torso-heart anatomy, and this challenges patients' diagnosis and risk stratification. This study aims to quantify how torso-heart anatomical factors affect sex-differences in ECG biomarkers in acute and chronic myocardial infarction (MI). We exploit the perfect control of AI-augmented multiscale modelling and simulation, based on clinical
magnetic resonance imaging (MRI) data and ECGs for model construction and validation. A cohort of 1720 torso-ventricular anatomies (50% female) was constructed from MRIs of healthy and post-MI participants in the UK Biobank study. 8600 ECG simulations were performed considering anatomical variability and 3 electrophysiological stages (healthy, acutely ischemic, and infarcted). The effect of cardiac size, position, and orientation on each ECG biomarker was quantified. Female anatomies had larger distances between the infarct and ECG electrodes (relative to cardiac size), and larger angles between the infarct normal and ECG lead axes, both primarily caused by their more superior cardiac position. This reduced ST-elevation and caused low-amplitude late depolarisation and repolarisation tails to be missed, shortening QRS duration (QRSd) and T-peak-to-end interval (TpTe). The position and orientation of the heart impacted TpTe more severely than QRSd. AI-enabled mechanistic modelling and simulation identify smaller ventricles, superior cardiac position, and different ventricular orientation as key anatomical contributors of shorter QRS and T waves, and lower ST-elevation, in female versus male anatomies. This provides a blueprint for quantifying the impact of anatomical sex differences on functional markers and enables future work in tailoring clinical guidelines considering unique patient anatomy to reduce biased outcomes.
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All authors Hannah Smith, Abhirup Banerjee, Leto Luana Riebel, Ruben Doste, Maxx Holmes, Vicente Grau, Blanca Rodriguez
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Thermal Stabilization of Defect Charge States and Finite-Temperature Charge Transition Levels
Tobias Hainer, Ethan Berger, Esmée Berger et al. (+3 more)
Point defects introduce localized electronic states that critically affect carrier trapping, recombination, and transport in functional materials. The associated charge transition levels (CTLs) can depend on temperature, requiring accurate treatment of vibrational and electronic free-energy contributions. In this work, we use machine-learned interatomic potentials to efficiently compute temperature-dependent CTLs for vacancies in MgO, LiF, and CsSnBr3. Using thermodynamic integration, we quantify free-energy differences between charge states and calculate the vibrational
entropy contributions at finite temperatures. We find that CTLs shift with temperature in MgO, LiF and CsSnBr3 from both
entropy and electronic contributions. Notably, in CsSnBr3 a neutral charge state becomes thermodynamically stable above 60 K, introducing a temperature-dependent
Fermi-level window absent at 0 K. We show that the widely used static, zero-kelvin defect formalism can miss both quantitative CTL shifts and the qualitative emergence of new stable charge states.
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All authors Tobias Hainer, Ethan Berger, Esmée Berger, Olof Hildeberg, Paul Erhart, Julia Wiktor
Optics
Non-linear X-ray Coherent Diffractive Imaging
Arnab Sarkar and Allan S. Johnson
The advent of nonlinear X-ray processes like sum-frequency generation and four-wave mixing raises the possibility of non-linear X-ray imaging, combining the high-resolution and elemental specificity of X-ray imaging with the state selectivity and sensitivity of non-linear optical imaging. While scanning imaging methods may be feasible, for linear X-ray processes coherent diffractive imaging has emerged as a key approach, enabling lensless reconstruction of nanoscale structure and dynamics with high spatial and temporal resolution. In this work, we propose a coherent diffractive approach to imaging using X-ray nonlinear processes, introducing an analysis method to isolate the nonlinear component from the overall diffraction pattern by leveraging the property of mutual incoherence between different wavelengths. For examples such as sum-frequency generation in ferroelectrics, this method reveals both domain structure and orientation through the retrieved amplitude and phase of the nonlinear signal. We discuss the feasibility of the proposed method in the presence of experimental noise, most relevantly shot noise. This analysis method is applicable in both static and dynamic imaging, offering a pathway beyond traditional spectroscopy toward XUV/X-ray coherent imaging of spatio-temporal dynamics in
quantum materials and biological systems.
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