Fluid Dynamics · Cross-listed from cs.LG
GLOBE: Accurate Surrogates for Boundary-Driven PDEs via Domain-Inspired Architectures and Equivariance
Peter Sharpe
We introduce GLOBE, a nonlinear neural surrogate for boundary-driven, homogeneous, weakly nonlinear PDEs that draws inductive bias from boundary-element methods and equivariant ML, representing solutions as superpositions of learnable Green's-function-like kernels evaluated from boundary faces to targets. The architecture is translation-, rotation-, and parity-equivariant; discretization-invariant; and units-invariant via rigorous nondimensionalization. On AirFRANS (steady incompressible RANS over NACA airfoils), a compact ~117k-parameter GLOBE model has lower mean-squared error than published values for the strongest reported ML surrogates on both the interpolation and low-data splits, in single-run comparisons under the released AirFRANS normalization. The model evaluates fields at arbitrary points, handles non-watertight meshes, and generalizes under Reynolds number and angle-of-attack distribution shifts. We further introduce a hierarchical Barnes-Hut acceleration that exploits the kernel's far-field decay to reduce evaluation cost from quadratic to near-linear in the number of boundary elements, with a tunable opening angle that recovers exact dense evaluation in the zero-angle limit. Quantitative validation focuses on 2D AirFRANS; on 3D DrivAerML cars, GLOBE predicts surface and volume fields on the full meshes, and we compare it with a matched Transolver baseline. Together, these results show that boundary-element-inspired inductive biases can substantially improve the accuracy of ML surrogates for boundary-driven PDEs while scaling to large 3D meshes.
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Data Analysis and Statistics
The Ensemble Kalman Inversion Race
Rebecca Gjini, Matthias Morzfeld, Oliver R.A. Dunbar et al. (+1 more)
Ensemble Kalman methods were initially developed to solve nonlinear data assimilation problems in oceanography but are now popular in applications far beyond their original use cases. Of particular interest is
climate model calibration. As hybrid physics and machine-learning models advance, the number of parameters and complexity of parameterizations in
climate models will continue to grow. To fully realize these advances, we must move from laborious hand-tuning to calibration-driven model development in rapid iteration cycles. Thus, robust calibration of these parameters plays an increasingly important role. We focus on learning
climate model parameters by minimizing the misfit between modeled and observed
climate statistics in an idealized setting. Ensemble Kalman methods are a natural choice for this problem because they are derivative-free, scalable to high dimensions, and robust to noise caused by statistical observations. Given the many variants of ensemble methods proposed, an important question is: Which ensemble Kalman method should be used for
climate model calibration? To answer this question, we perform systematic numerical experiments to explore the relative computational efficiencies of several ensemble Kalman methods. The numerical experiments involve statistical observations of Lorenz-type models of increasing complexity, frequently used to represent simplified atmospheric systems, with some featuring neural network parameterizations. For each test problem, several ensemble Kalman methods and a derivative-based method "race" to reach a specified accuracy, and we measure the computational cost required to achieve the desired accuracy. We investigate how prior information and the parameter or data dimensions define the computational costs of the various methods.
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All authors Rebecca Gjini, Matthias Morzfeld, Oliver R.A. Dunbar, Tapio Schneider
Instrumentation and Detectors
Geant4 based library SCoRe4 for Surface Contamination and Roughness Effects simulations in rare event search experiments
Christoph Grüner
Surface simulations are important for accurately modeling particle interactions in experiments where background contributions from surface contaminants can significantly affect detector performance. In rare event searches, such as dark matter or neutrinoless double beta decay experiments, standard Geant4 simulations typically assume perfectly smooth surfaces, neglecting the microscopic roughness that exists in real materials. This simplification can lead to inaccurate predictions of energy deposition. To address this limitation, I developed SCoRe4, a Geant4-based library designed to simulate more realistic surface roughness based on experimentally measurable parameters. The code allows users to generate patches of simplified rough surface geometries across a wide range of scales - from square millimeters to square meters - while maintaining computational efficiency. SCoRe4 is open source and can be easily integrated into existing Geant4 setups. This work presents the structure, implementation, and example application of SCoRe4,as well as its potential use in improving the accuracy of background modeling in rare event physics.
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Atmospheric and Oceanic Physics
Cloud droplet size distribution and optical properties only weakly linked to aerosol size
Kadja Flore Gali, Hamed Fahandezh Sadi, Jesse C. Anderson et al. (+7 more)
Changes in aerosol concentrations can modify cloud brightness, producing a strong but poorly constrained influence on Earth's energy balance. Because cloud reflectivity depends on the size distribution of cloud droplets, and aerosol size strongly governs activation into droplets, one might expect cloud properties to be sensitive to aerosol size distributions. Here we show, through a combination of cloud chamber experiments and high-resolution simulations, that cloud microphysical and optical properties are often insensitive to aerosol size. Detectable impacts on cloud optical properties occur only under weak convective forcing and high aerosol concentrations. These results indicate that, in most conditions, cloud reflectivity can be predicted from aerosol number alone without detailed knowledge of aerosol size distributions, providing new constraints on how aerosol perturbations affect
climate.
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All authors Kadja Flore Gali, Hamed Fahandezh Sadi, Jesse C. Anderson, Payton Beeler, Aaron Wang, David Richter, Raymond A Shaw, Fan Yang, Will Cantrell, Laura Fierce
Data Analysis and Statistics · Cross-listed from hep-ph
SURFing to the Fundamental Limit of Jet Tagging
Ian Pang, Darius A. Faroughy, David Shih et al. (+2 more)
Beyond the practical goal of improving search and measurement sensitivity through better jet tagging
algorithms, there is a deeper question: what are their upper performance limits? Generative surrogate models with learned likelihood functions offer a new approach to this problem, provided the surrogate correctly captures the underlying data distribution. In this work, we introduce the SUrrogate ReFerence (SURF) method, a new approach to validating generative models. This framework enables exact Neyman-Pearson tests by training the target model on samples from another tractable surrogate, which is itself trained on real data. We argue that the EPiC-FM generative model is a valid surrogate reference for JetClass jets and apply SURF to show that modern jet taggers may already be operating close to the true statistical limit. By contrast, we find that autoregressive GPT models unphysically exaggerate top vs. QCD separation power encoded in the surrogate reference, implying that they are giving a misleading picture of the fundamental limit.
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All authors Ian Pang, Darius A. Faroughy, David Shih, Ranit Das, Gregor Kasieczka
Fluid Dynamics
Objective detection of coherent vortices from instantaneous flow data
Tiemo Pedergnana and Florian Kogelbauer
Vortices are swirling regions of fluid that structure motion in gases and
liquids across a wide range of scales, from laboratory-scale experiments to vast atmospheric currents. They play a key role in mixing, transport, and energy transfer, yet their reliable identification in unsteady flows remained a major challenge. Most existing approaches rely on local, instantaneous properties of the velocity gradient, such as strain or rotation. Although effective in simple or steady flows, these criteria can fail in complex, time-dependent settings, falsely detecting
vortices or overlooking coherent structures altogether. Lagrangian methods instead identify
vortices as regions of material coherence by tracking fluid trajectories over time. While conceptually sound, these approaches are computationally intensive, require high-quality data, and are impractical for real-time applications. This motivates a central challenge: whether coherent
vortices, inherently defined over finite times, can be detected objectively from instantaneous flow measurements. Here we introduce the first Eulerian criterion to overcome these challenges. By examining the temporal
evolution of strain and removing from the velocity field the components attributable to rigid-body motion, we construct an objective velocity field that isolates genuine swirling dynamics. The resulting $Q_\text{s}$-criterion consistently identifies coherent
vortices in both analytical examples and complex flow data, including cases where traditional methods fail. Our framework provides an observer-independent, computationally efficient tool for
vortex detection from instantaneous data, enabling improved analysis and prediction of fluid flows across scales.
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Fluid Dynamics
Consistency requirement of data-driven subgrid-scale modeling in large-eddy simulation
Xinyi Huang, Sze Chai Leung and H. Jane Bae
Data-driven subgrid-scale (SGS) modeling in the large-eddy simulations (LES) suffers from the inconsistency between the a priori tests and the a posteriori tests, which make training accurate SGS models a difficult task. We study the difference in filtered high-fidelity data and LES to identify the numerical deviation between the two cases, which is a combined impact of commutation error, numerical errors, and error coupling. The impact of the numerical deviation is examined through two SGS model formulations: the eddy-viscosity and the complex nonlinear models. By incorporating numerical deviations into model training, we enhance consistency, stabilize simulations, and improve predictions of energy spectra in a posteriori tests. Our findings highlight that data-driven methods introduce significant nonlinearity and equation coupling, exacerbating inconsistencies compared to non-data-driven approaches. Finally, while the impact of the numerical deviation can be generalized, achieving accurate model predictions necessitates a physically grounded model form and an optimal filter width.
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Fluid Dynamics
Periodic modulation of the space-filling nature of the turbulent flame leads to spiky heat release oscillations
Sivakumar Sudarsanan, Manikandan Raghunathan, Shwetha Viswesh et al. (+1 more)
Spiky oscillations are characterized by slow-fast dynamics and are observed in excitable media such as neuronal membranes and cardiac
cells. In a turbulent reactive flow system, we observe that the heat release rate exhibits self-sustained periodic, spiky oscillations in synchrony with the sinusoidal periodic acoustic pressure oscillations. These self-sustained oscillations are a consequence of thermoacoustic instability, which arises due to a positive feedback between the acoustic and the heat release rate fields. One of the primary mechanisms for fluctuations in the heat release rate is the modulation in the
topology of the flame, a thin interface separating the reactants and products. In this work, we explore the dynamics of the space-filling nature of the flame, quantified by its fractal dimension in relation to the spiky heat release rate oscillations in a turbulent reactive flow system. We discover that the periodic oscillatory dynamics in the space-filling nature of the flame lead to periodic, spiky heat release rate oscillations. Based on this result, we show that the spiky oscillations in the heat release rate can be approximated as $e^{\mathrm{sin}(\omega t)}$ during the dynamical state of generalized synchronization between the heat release rate and the acoustic pressure oscillations in a turbulent reactive flow system. In synchronization theory, generalized synchronization is characterized by an emergent functional relationship, $\Phi$, between the interacting subsystems. We unravel this emergent functional relation between the heat release rate and the acoustic pressure oscillations in a turbulent reactive flow system. It is intriguing that the dynamics of a far-from-
equilibrium complex system can be represented by such simple mathematical relations.
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All authors Sivakumar Sudarsanan, Manikandan Raghunathan, Shwetha Viswesh, R. I. Sujith
Medical Physics
The Emory Optic Nerve Head Atlas - Using 3D Anatomical Mapping to Study Optic Neuropathies with an Initial Focus on Glaucoma
Thanadet Chuangsuwanich, Patipol Tiyajamorn, Yibo Chen et al. (+5 more)
Purpose: To develop the first 3D optic nerve head (ONH) atlas using AI-based registration and evaluate its use in: (1) atlas-adjusted retinal nerve fiber layer (RNFL) analysis for glaucoma diagnosis, and (2) strain-based assessment of glaucoma severity. Methods: Large-scale OCT
datasets were registered using REFLECTIVITY-generated tissue
segmentations. A healthy atlas (n=460) and glaucoma atlases for mild (n=852), moderate (n=640), and severe (n=546) disease were built using an AI-driven
algorithm incorporating structural and biomechanical constraints. Atlas quality was evaluated using inter-layer contrast and agreement between warped scans and the template. Atlas-adjusted RNFL thickness was extracted by mapping fixed 1.5 mm BMO-based reference points from the atlas to each subject and compared with standard subject-specific RNFL measurements. Effective strain relative to each atlas was computed by morphing subjects to the atlas template. Strain and thickness features were used to classify glaucoma severity. Results: The healthy atlas captured normative ONH anatomy, while glaucoma atlases showed stage-dependent changes. Registration accuracy was high (NCC=0.86+-0.05; Dice=0.90+-0.02), with strong inter-layer contrast (0.40+-0.03). Atlas-adjusted RNFL thickness improved separation between healthy and mild glaucoma (Cohen's d=0.77 vs. 0.72). Classification using atlas-adjusted profiles outperformed native measurements (AUC=0.771 vs. 0.757). Mean effective strain differed significantly across severity groups (ANOVA p<0.05), and CNNs trained on strain maps achieved strong classification (AUC=0.79). Conclusion: The ONH atlas provides a population-derived structural reference enabling anatomically consistent thickness mapping and strain-based characterization. It enhances diagnostic discrimination, supports severity classification, and offers a framework to study glaucomatous biomechanics.
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All authors Thanadet Chuangsuwanich, Patipol Tiyajamorn, Yibo Chen, Michael Dattilo, Deepta Ghate, Nancy Newman, Valérie Biousse, Michaël J.A. Girard
Fluid Dynamics
Towards Streaming Prediction of Oscillatory Flows: A Data Assimilation and Machine Learning Approach
Miguel M. Valero and Marcello Meldi
Data-driven methods have demonstrated strong predictive capabilities in fluid mechanics, yet most current applications still focus on simplified configurations, often characterised by statistical stationarity or limited temporal variability. This work proposes a methodology that combines Data Assimilation (DA) and Machine Learning (ML) to predict flow configurations that exhibit cyclic behaviour over time. Starting from limited, sparse high-fidelity measurements and a low-fidelity numerical model, the DA approach performs data fusion to obtain complete and accurate flow state estimations in time. This complete
dataset is used to train multiple ML tools, which are applied across different phases of the flow cycle to augment the model's predictions when high-fidelity data might not be available for the DA application. The methodology is applied to the analysis of an oscillating cylinder in a laminar regime using a sliding-window approach, in which separate models are trained for specific flow conditions to ensure each model specialises in flow dynamics representative of a phase of the oscillation period. This phase-resolved learning enables the efficient capture of transient features that would be challenging for a single global model. The results highlight the potential of this method to study complex flow configurations with oscillatory features in which neither the flow nor the cycle is known a priori, in particular by exploiting real-time training and updates, as is commonly done in digital twins, which require continuous model correction and adaptation.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Electrical Modulation and Probing of Antiferromagnetism in Hybrid Multiferroic Heterostructures
Yuhan Liang, Huiping Han, Hetian Chen et al. (+11 more)
The unique features of ultrafast
spin dynamics and the absence of macroscopic magnetization in
antiferromagnetic (AFM) materials provide a distinct route towards high-speed
magnetic storage devices with low energy consumption and high integration density. However, these advantages also introduce challenges in probing and controlling AFM order, thereby restricting their practical applications. In this study, we demonstrate an all-electric control and probing of the AFM order in heavy metal (HM)/AFM
insulator (AFMI)
heterostructures on a ferroelectric substrate at room temperature (RT). The AFM order was detected by the anomalous
Hall effect (AHE) and manipulated by the ferroelectric field effect as well as the piezoelectric effect in
heterostructures of Pt/NiO/0.7Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_{3}$–0.3PbTiO$_{3}$ (PMN–PT). The non-volatile control of AFM order gives rise to a 33% modulation of AHE, which is further evidenced by synchrotron-based X-ray
magnetic linear dichroism (XMLD). Combined with the $in$-$situ$ piezoelectric response of AHE, we demonstrate that ferroelectric polarization contributes mainly to the control of the AFM order. Our results are expected to have broader implications for efficient spintronic devices.
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All authors Yuhan Liang, Huiping Han, Hetian Chen, Yujun Zhang, Yi Zhang, Chao Li, Shun Lan, Fangyuan Zhu, Ji Ma, Di Yi, Jing Ma, Liang Wu, Tianxiang Nan, Yuan-Hua Lin
Fluid Dynamics
Modeling the influence of interactions on different variables in a turbulent thermoacoustic system
Aneesh Srivatsa, Shruti Tandon, Andrea Elizabeth Biju et al. (+3 more)
Turbulent reacting flows confined to ducts are plagued by thermoacoustic instability, a state in which a positive feedback between flow, flame and acoustic perturbations leads to the emergence of catastrophically high-amplitude oscillatory dynamics in the sound and global heat release rate fluctuations. Modeling the interdependence between local interactions and the global emergence of order in such spatially extended complex systems is exacting. Here, we present a novel reduced-order model to capture the influence of the local interactions on the variables exhibiting global emergence of order in a turbulent reacting flow system. We represent each variable that exhibits global oscillatory instability as an oscillator with a cubic nonlinearity. The oscillator is driven by a forcing term that represents the holistic influence of the inter-subsystem interactions on the global behavior. The forcing term essentially couples the local interactions and the globally emergent dynamics in the model. Further, the influence of the inter-subsystem interactions on the behavior of each subsystem is different. Therefore, we use different forcing terms for each variable inspired by the physical interactions in the system. The nonlinear oscillators representing the acoustic and the heat release rate oscillations are hence forced using Wiener and Markov-modulated Poisson processes, respectively. Using this approach, we are able to reproduce (i) the multifractal characteristics of acoustic pressure fluctuations during chaotic dynamics, (ii) the loss of multifractality through the experimentally observed scaling law behavior during the transition from chaos to order and (iii) the emergence of periodicity and bifurcation in heat release rate dynamics.
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All authors Aneesh Srivatsa, Shruti Tandon, Andrea Elizabeth Biju, Norbert Marwan, Juergen Kurths, R. I. Sujith
Fluid Dynamics
A new instability driven by the combined effect of wind stress and rotation in a sheared liquid layer
S. Preethi, Ankush Kamboj, Ramkarn Patne et al. (+2 more)
We examine the linear stability of a shear flow driven by wind stress at the free surface and rotation at the lower boundary, mimicking oceanic flows influenced by surface winds and rotation of Earth. The linearised
eigenvalue problem is solved using the Chebyshev spectral collocation method and a longwave asymptotic analysis. Our results reveal new longwave instability modes that emerge for non-zero rotational Reynolds numbers. It is observed that the most unstable mode, characterised by the lowest critical parameters, corresponds to longwave spanwise disturbances with vanishing streamwise wavenumber. The asymptotic analysis, which shows excellent agreement with numerical results, analytically confirms the existence of this instability. Thus, the present study demonstrates the hitherto unreported combined influence of wind stress and rotation of Earth on ocean dynamics.
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All authors S. Preethi, Ankush Kamboj, Ramkarn Patne, P. A. L. Narayana, Kirti Chandra Sahu
Physics and Society
Loops, not groups: Long cycles are responsible for discontinuous phase transitions in higher-order network contagions
Leah A. Keating and Laurent Hébert-Dufresne
We study a self-consistent approach to introduce higher-order effects in a branching process model of complex contagion on clustered networks. Branching processes operate over an infinite population such that they never circle back and interact with previously exposed parts of the system. This infinite, treelike, structure makes it tricky to account for complex contagion mechanisms such as group effects, peer pressure, or social reinforcement where multiple exposures interact in synergistic ways. Here we present a self-consistent solution that accounts for local group structure and global cycles where the process can feedback on itself. This allows us to distinguish multiple exposures that stem from a single transmission chain, from those occurring at the intersection of different transmission chains. We find that only the latter mechanism can give rise to a discontinuous phase transition in the size of global cascades, which is a defining feature of complex contagions. Group effects alone, without long cycles, produce standard continuous phase transitions.
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Optics
van de Hulst Essay: Geometric-phase portrayal of electromagnetic scattering by a three-dimensional object in free space
Akhlesh Lakhtakia
The concept of
geometric phase was applied to initiate the
geometric-phase portrayal of electromagnetic scattering by a three-dimensional object in free space. Whereas the incident electromagnetic field is that of an arbitrarily polarized plane wave, the direction-dependent far-zone scattering amplitude is used to define direction-dependent Stokes parameters for the scattered field. Both symmetric and asymmetric Poincaré spinors are formulated to characterize the polarization states of incident plane wave and the far-zone scattering amplitude, and two different
geometric phases are defined therefrom. Density plots of both
geometric phases were calculated for six different homogeneous isotropic spheres with different linear constitutive properties and boundary conditions: dielectric-
magnetic spheres (non-dissipative and dissipative), impedance spheres, perfect electrically conducting spheres, charged dielectric-
magnetic spheres, dielectric-
magnetic spheres with topologically insulating surface states, and isotropic
chiral spheres. The incident plane waves were taken to be linearly and circularly polarized, for the sake of illustration. Numerical results revealed that
geometric-phase density plots possess significantly richer features than their counterparts for the differential scattering efficiency. The
geometric-phase portrayals exhibit enhanced sensitivity to changes in the size and composition of the scatterer, the boundary conditions, and the incident polarization state, suggesting promise for inverse-scattering problems.
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Plasma Physics
Material processing by laser-plasma-filament-guided high voltage discharges
Kristian Cvecek, Markus Döring, Alexander Romboy et al. (+2 more)
We investigate ablation experiments performed by
laser-
plasma-filament guided electrical discharges at high-voltages of up to 145 kV. The guiding was accomplished via fs-
laser-generated
plasma filaments across a gap of 201 mm of air onto steel 1.3343 samples. This method combines remote material processing and enables the steering and deflection of high voltage discharges with the ease-of-use of remote
laser processing technology. We observe an increase of the per-pulse-(and-discharge)-ablated volume by a factor of 1.67 over an ablation regime when the discharges are not present and up to a factor of 12.5 over the case when neither discharges nor
plasma filaments, only a loosely focused
laser beam, are present.
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All authors Kristian Cvecek, Markus Döring, Alexander Romboy, Johannes Heberle, Michael Schmidt
Plasma Physics
Characterisation of X- and O-points in Wendelstein 7-X with respect to coil currents
Robert Davies, Christopher B. Smiet, Charlotte Batzdorf et al. (+3 more)
This work analyses
vacuum magnetic field
topology in Wendelstein 7-X (W7-X) with respect to changes in the current in the superconducting coils. We develop a fast automated scheme to locate fixed points (such as X- and O-points) and calculate the trace of the Jacobian of the field line map for them (Tr(M)), which represents several important properties of the fixed point. We perform two sets of coil current scans: (1) scans where each coil current is varied individually, using the "standard", "high iota" and "low iota" configurations as starting points; (2) a scan of over 200,000
magnetic configurations in which the coil currents are randomly sampled. In both cases we constrain the coil currents to the normal range of W7-X. We verify the principal roles of the non-planar, planar and control coils: the non-planar coils establish island chains with a certain phase; the planar coils modify the location of the island chain by both controlling the iota profile and shifting the configuration "inward" and "outward"; the control coil affects the island size and phase. We also find that |Tr(M)-2| (a quantity closely related to the magnitude of the Greene's residue) tends to increase with the minor radius of the fixed points, and that Tr(M) for X- and O-points can be very differently affected by the control coil current. Finally, we show that |Tr(M)-2| serves as a proxy for island size for internal island chains, which may help identification of suitable experimental candidates.
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All authors Robert Davies, Christopher B. Smiet, Charlotte Batzdorf, J. Geiger, J. Loizu, S. A. Henneberg
Geophysics
Learning from Imperfect Labels: A Physics-Aware Neural Operator with Application to DAS Data Denoising
Yang Cui, Denis Anikiev, Umair Bin Waheed et al. (+1 more)
Supervised deep learning methods typically require large
datasets and high-quality labels to achieve reliable predictions. However, their performance often degrades when trained on imperfect labels. To address this challenge, we propose a physics-aware loss function that serves as a penalty term to mitigate label imperfections during training. In addition, we introduce a modified U-Net-Enhanced
Fourier Neural Operator (UFNO) that achieves high-fidelity feature representation while leveraging the advantages of operator learning in function space. By combining these two components, we develop a physics-aware UFNO (PAUFNO) framework that effectively learns from imperfect labels. To evaluate the proposed framework, we apply it to the denoising of distributed acoustic sensing (DAS) data from the Utah FORGE site. The label data were generated using an integrated filtering-based method, but still contain residual coupling noise in the near-wellbore channels. The denoising workflow incorporates a patching-based data augmentation strategy, including an uplifting step, spatial-domain convolutional operations, spectral convolution, and a projection layer to restore data to the desired shape. Extensive numerical experiments demonstrate that the proposed framework achieves superior denoising performance, effectively enhancing DAS records and recovering hidden signals with high accuracy.
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All authors Yang Cui, Denis Anikiev, Umair Bin Waheed, Yangkang Chen
Atomic Physics
Study of ground state electronic structure of XH$^+$ (X : Cd, Hg and Yb) molecular ions via coupled-cluster approach
Ankush Thakur, Renu Bala and H. S. Nataraj
The present work reports the spectroscopic parameters and molecular properties for the ground electronic state, $^1\Sigma^+$, of CdH$^+$, HgH$^{+}$, and YbH$^{+}$ molecular ions. We have used the state-of-the-art relativistic coupled cluster method together with the relativistic core-valence triple- and quadruple
zeta quality basis sets for the calculation of structural parameters. The computed results have been extrapolated to the complete basis set limit using a two-point
polynomial fit. The reliability of the results has been confirmed by their remarkable agreement with existing experimental and theoretical values. Further, we have calculated the relevant vibrational parameters by solving the vibrational Schrödinger equation using the potential energy curve and the permanent dipole moment curve of the electronic ground state. Subsequently, the lifetimes of the vibrational states have been determined by calculating the spontaneous and black-body radiation (BBR) induced transition rates. At room temperature, the lifetimes of the lowest ro-vibrational state ($v$ = \(0\), $J$ = \(0\)) due to BBR-induced transitions are estimated to be \(98.48\)\,s for CdH$^+$, \(204.85\)\,s for HgH$^+$, and \(1250.28\)\,s for YbH$^+$. Additionally, the rotational energies within each vibrational state are also calculated in this work.
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Medical Physics
Qualitative and quantitative hard-tissue MRI with portable Halbach scanners
Jose Borreguero, Luiz G. C. Santos, Lorena Vega Cid et al. (+14 more)
Purpose: To demonstrate the feasibility of performing in-vivo imaging and quantitative relaxation mapping of soft and hard tissues using a low-cost, portable MRI scanner, and to establish the methodological foundations for zero echo time (ZTE) imaging in systems affected by strong field inhomogeneities. Methods: A complete framework for artifact-free ZTE imaging at low field was developed, including: (i) RF pulse pre/counteremphasis calibration to minimize ring-down and electronics switching time; (ii) an extension of a recent single-point double-shot (SPDS) protocol for simultaneous B0 and B1 mapping; and (iii) a model-based reconstruction incorporating these field maps into the encoding
matrix. ZTE imaging and variable flip angle (VFA) T1 mapping were performed on phantoms and in-vivo human knees and ankles, and benchmarked against standard RARE and STIR acquisitions. Results: The optimized PETRA sequence produced 3D images of knees and ankles within clinically compatible times (< 15 min), revealing hard tissues such as ligaments, tendons, cartilage, and bone that are invisible in
spin-echo sequences. The extended SPDS method enabled accurate field mapping, while the VFA approach provided the first in-vivo T1 measurements of hard tissues at B0 < 0.1 T. Conclusions: The proposed framework broadens the range of pulse sequences feasible in portable low-field MRI and demonstrates the potential of ZTE for quantitative and structural imaging of musculoskeletal tissues in affordable Halbach-based systems.
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All authors Jose Borreguero, Luiz G. C. Santos, Lorena Vega Cid, Elisa Castañón, Marina Fernández-García, Pablo Benlloch, Rubén Bosch, Jesús Conejero, Pablo García-Cristóbal, Alba González-Cebrián, Teresa Guallart-Naval, Eduardo Pallás, Laia Porcar, Lucas Swistunow, Jose Miguel Algarín, Fernando Galve, Joseba Alonso
Space Physics
Turbulence in the terrestrial magnetosheath: space-time correlation using the Magnetospheric Multiscale mission
Francesco Pecora, William H. Matthaeus, Antonella Greco et al. (+4 more)
Spatiotemporal correlation of
magnetic field fluctuations is investigated using the Magnetospheric Multiscale mission in the terrestrial magnetosheath. The first observation of the
turbulence propagator in space emerges through analysis of more than a thousand intervals. Results show clear features of spatial and spectral anisotropy, leading to a distinct behavior of relaxation times in the directions parallel and perpendicular to the mean
magnetic field. Full space-time investigation of the Taylor hypothesis reveals a scale-dependent anisotropy of magnetosheath fluctuations that can be compared to the effect of flow propagation on spacecraft frame time decorrelation rates as well as with Eulerian estimates. The
turbulence propagator reveals that the amplitudes of the perpendicular modes decorrelate according to sweeping or Alfvénic propagation mechanisms. The decorrelation time of parallel modes instead does not depend on the parallel wavenumber, which could be due to resonant interactions. Through direct observation, this study provides unprecedented insight into the space-time structure of turbulent space
plasmas, while giving critical constraints for theoretical and numerical models.
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All authors Francesco Pecora, William H. Matthaeus, Antonella Greco, Pablo Dmitruk, Yan Yang, Vincenzo Carbone, Sergio Servidio
Physics Education
A critical review of pre-post surveys designed to measure student epistemology in undergraduate science courses
Kyriaki Chatzikyriakidou, Kristi L. Hall, Edward F. Redish et al. (+1 more)
The epistemology of science students, i.e., their beliefs about the nature of the knowledge they are learning, about what they have to do to learn it, and about how they will use that knowledge, often plays a powerful role in what they learn in their science courses. This perspective paper provides a broad overview of the theoretical frameworks, designs, and applications of online pre-post surveys that are available to assess the potential shifts in epistemic perspectives in undergraduate science courses. We pay particular
attention to a recent survey for biology courses called the Maryland Biological Expectation Survey (MBEX). The MBEX was developed to probe four epistemic themes that are closely aligned with the
Vision and Change initiative for reforming undergraduate biology education. This review is intended to inform STEM teachers about the availability of online epistemological surveys for evaluating the epistemic effects of their courses. These surveys can also help STEM education researchers readily evaluate how different pedagogies, classroom contexts, and other features of learning environments affect the epistemic perspectives of science students.
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All authors Kyriaki Chatzikyriakidou, Kristi L. Hall, Edward F. Redish, Todd J. Cooke
Atomic Physics
First ionization potentials of Cr, Mo, and W calculated with SHCI
Zachary T. Jerzyk, David R. Smith and Matthew Otten
The design and performance of future fusion power plants will depend on accurate atomic data for
plasma-facing material and
plasma impurity species. A leading candidate for the
plasma-facing material is tungsten due to its high melting point, however, the energy levels and wavefunctions of high-Z atoms with many electrons (e.g. 30 or more), including tungsten, are difficult to calculate with high accuracy. Gaps and large uncertainties in atomic data for tungsten introduce design and performance uncertainties for a fusion power plant. Specifically, improved atomic data for ionization potential, excited state energies, and collisional excitation rates are needed for the low charge states of atomic tungsten. We aim to address these shortcomings by using the semistochastic heat-bath configuration interaction (SHCI) method, which nearly exactly calculates the energies that can be determined at higher cost with the full configuration interaction. Adding well-motivated approximations to SHCI, including orbital optimization and effective core potentials, we demonstrate good agreement between our calculated first ionization potentials and the best available experimental values for chromium, molybdenum, and tungsten. The efficiency and accuracy achieved in calculating these ionization potentials demonstrates that our SHCI workflow can yield improved electron structure data for ions with many electrons, suggesting that the method could also be useful for collisional processes, such as state-selective charge exchange reactions and electron impact ionization.
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Instrumentation and Detectors
High Density Hybridisation Using ENIG Bumping and Anisotropic Conductive Films
Haripriya Bangaru, Giovanni Calderini, Dominik Dannheim et al. (+5 more)
Fine-pitch hybridisation processes are essential for next-generation pixel detectors and high-density microelectronic assemblies. Conventional bump-bonding techniques, although reliable, remain costly and difficult to implement for single-die applications. In this work, we present flip-chip hybridisation results combining Electroless Nickel Immersion Gold (ENIG) bumping with Anisotropic Conductive Film (ACF) bonding, both developed in-house. This method enables fine-pitch interconnections without requiring wafer-level processing. The feasibility of the ENIG-ACF process for functional devices was demonstrated by hybridising functional Timepix3 ASIC and Ti-LGAD (Trench Isolated Low-Gain Avalanche Detector) sensors with 55 um pitch. The approach was then extended to test structures with 25 um pitch, using aligned-particle ACFs. The analysis revealed that the main limitation originated from the mismatch between ACF thickness and bump height, underlining the importance of interface geometry optimisation. To address this, a dedicated simulation program was developed to model the isotropic growth of ENIG bumps and to determine the optimum bump height as a function of ACF thickness and pixel-
matrix geometry. The results provide valuable guidelines for future high connection density assemblies and demonstrate the potential of the ENIG-ACF process as a scalable, low-cost alternative to conventional bump-bonding techniques.
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All authors Haripriya Bangaru, Giovanni Calderini, Dominik Dannheim, Rui De Oliveira, Ahmet Lale, Moritz Lauser, Mateus Vicente Barreto Pinto, Peter Svihra
Computational Physics
A Full-Induction Magnetohydrodynamics Solver for Liquid Metal Fusion Blankets in Vertex-CFD
Eirik Endeve, Doug Stefanski, Marc-Olivier G. Delchini et al. (+4 more)
Multiphysics modeling of
liquid metal fusion blankets, which produce tritium and convert energy of
neutrons created via fusion reactions into heat, is crucial for predicting performance, ensuring structural integrity, and optimizing energy production. While traditional blanket modeling of
liquid metal flows during normal steady operating conditions commonly employs the inductionless approximation of the magnetohydrodynamics (MHD) equations, transient scenarios, when the
plasma-confining
magnetic field varies on millisecond time scales, require a full-induction MHD approach that dynamically evolves the
magnetic field via the time-dependent induction equation. This paper presents the formulation, implementation, and initial verification of a full-induction MHD solver integrated within the open-source Vertex-CFD framework, which aims to achieve tight multiphysics coupling, a flexible software design enabling easy extension and addition of physics models, and performance portability across computing platforms. The solver utilizes finite element spatial discretization, implicit Runge–Kutta time integration, and an inexact Newton method to solve the resulting discrete nonlinear system, leveraging Trilinos packages for efficient computation. Verification against selected
benchmark problems demonstrates accuracy and robustness of the solver. Furthermore, when the solver is applied to an idealized blanket model in 2.5D and full 3D, results obtained with Vertex-CFD are in good agreement with recently published quasi-2D simulations. These findings establish a computational foundation for future simulations of transient MHD phenomena in
liquid metal blankets with Vertex-CFD, and open avenues for future extensions and performance optimizations.
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All authors Eirik Endeve, Doug Stefanski, Marc-Olivier G. Delchini, Stuart Slattery, Cory D. Hauck, Bruno Turcksin, Sergey Smolentsev
Physics and Society
Spatiotemporal Activity-Driven Networks
Zsófia Simon and Jari Saramäki
Temporal-network models have provided key insights into how time-varying connectivity shapes dynamical processes such as spreading. Among them, the activity-driven model is a widely used, analytically tractable
benchmark. Yet many temporal networks, such as those of physical proximity, are also embedded in space, and spatial constraints are known to affect dynamics unfolding on the networks strongly. Despite this, there is a lack of similar simple and solvable models for spatiotemporal contact structures. Here, we introduce a spatial activity-driven model in which short-range contacts are more frequent. This model is analytically tractable and captures the joint effects of space and time. We show analytically and numerically that the model reproduces several characteristic features of social and contact networks, including strong and weak ties, clustering, and triangles having weights above the median. These traits can be attributed to space acting as a form of
memory. Simulations of spreading dynamics on top of the model networks further illustrate the role of space, highlighting how localisation slows down spreading. Furthermore, the framework is well-suited for modelling social distancing in a principled way as an intervention measure aimed at reducing long-range links. We find that, unlike for non-spatial networks, even a small spatially targeted reduction in the total number of contacts can be very effective. More broadly, by offering a tractable framework, the model enables systematic exploration of dynamical processes on spatiotemporal networks.
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Atomic Physics
Wavelengths and Energy Levels of Neutral Manganese (Mn I) Determined Using High-Resolution Fourier Transform and Grating Spectroscopy
Christian P. Clear, Gillian Nave, Richard Blackwell-Whitehead et al. (+3 more)
An extensive analysis of the spectrum of neutral manganese has been performed using spectra of manganese-neon and manganese-argon hollow cathode discharges measured using high resolution
Fourier transform (FT) and grating spectroscopy over the range 151 - 5112 nm (1956 - 65876 cm-1). Wavelengths for 10426 spectral lines were extracted from the FT spectra, with uncertainties at least an order-of-magnitude lower than previous measurements. Wavelengths for 13397 lines from new grating spectra were determined for spectral regions beyond the FT spectra range or to provide wavelengths for weak transitions not observed in FT spectra. To aid in level identification, selected, previously published grating lines were included in the energy level optimisation, but no levels in this work relied solely on previously published wavelengths. In total, 24237 lines were included in the final spectral linelist, and these were used to identify 2186 Mn I transitions. These classified spectral lines were then used to optimise the values of 384 previously published energy levels of Mn I, with typical uncertainties of a few 10-3 cm-1, again typically an order-of-magnitude improvement in accuracy. Our study then expanded the known energy level structure of Mn I through the establishment of 18 new energy levels, reported here for the first time. In total, 2187 lines and 402 energy levels of Mn I have been determined as a result of our work, marking a substantial advance in the precision of Mn I atomic data which will enable far more accurate analyses of Mn I lines in astrophysical spectra. Please note: The final wavelength and energy level
datasets are withheld from this pre-print to ensure that only the peer-reviewed, definitive versions are released. This approach prevents the propagation of duplicate or inconsistent data across widely used atomic databases and modelling frameworks.
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All authors Christian P. Clear, Gillian Nave, Richard Blackwell-Whitehead, Maria Teresa Belmonte, Stephen Ingram, Juliet C. Pickering
Chemical Physics
Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics
Jakob J. Kresse, Alexander Sikorski and Marcus Weber
Interpretable reaction coordinates are essential for understanding rare conformational transitions in molecular dynamics. The Atomistic Mechanism Of Rare Events in Molecular Dynamics (AMORE-MD) framework enhances interpretability of deep-learned reaction coordinates by connecting them to atomistic mechanisms, without requiring any a priori knowledge of collective variables, pathways, or endpoints. Here, AMORE-MD employs the ISOKANN
algorithm to learn a neural membership function $\chi$ representing the dominant slow process, from which transition pathways are reconstructed as minimum-energy paths aligned with the gradient of $\chi$, and atomic contributions are quantified through gradient-based sensitivity analysis. Iterative enhanced sampling further enriches transition regions and improves coverage of rare events enabling recovery of known mechanisms and chemically interpretable structural rearrangements at atomic resolution for the Müller-Brown potential, alanine dipeptide, and the elastin-derived hexapeptide VGVAPG.
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History and Philosophy of Physics
Dark Matter Realism: How Referential Semantics Restricts Realism in Contemporary Fundamental Physics
Simon Allzén
Philosophers increasingly treat semantics as decisive for realism about dark matter. In this paper, I consider a recent proposal from Vaynberg (2024) anchored in the causal-descriptive theory of reference from Psillos (1999, 2012). I argue that the application of Psillos' general scientific realist framework in the local context of dark matter is misguided, partly because of the overlooked metaphysical commitments underpinning causal-descriptivism, and partly because the extension of 'dark matter' on this account includes entities we do not currently consider to be dark matter, and exclude entities that we currently consider could be dark matter. Furthermore, I argue that this discord between scientific realism and dark matter should be regarded endemic in contexts where empirical evidence is scarce: the semantic details required by the proposed scientific realism is dependent on canonical empirical confirmation, because it is against that background that scientific realism has been formulated and developed.
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Optics · Cross-listed from cond-mat.mes-hall
Long-Range Magnetic Order in Structurally Embedded Mesospin Metamaterials
Christina Vantaraki, Oier Bikondoa, Matías P. Grassi et al. (+11 more)
Engineered assemblies of interacting
magnetic elements-
magnetic metamaterials-provide a powerful route to tailor collective
magnetic order and dynamics. By structuring matter at the mesoscale, they bridge atomic magnetism and macroscopic functionality, enabling emergent behaviour inaccessible in conventional materials. However, realizing large-area metamaterials that combine high morphological uniformity with intrinsic long-range order has remained challenging, largely due to the structural disorder inherent to lithographic fabrication. Here we demonstrate a scalable route to structurally and magnetically coherent metamaterials by
embedding iron-ions to form mesospins within a non-
magnetic thin film palladium host
matrix. Using controlled implantation, we realize morphologically uniform arrays that spontaneously develop extended
antiferromagnetic order in the as-fabricated state - without the need of external annealing or field cycling. Resonant X-ray scattering and microscopy reveal sharp
magnetic Bragg peaks modulated by the mesospin form factor, evidencing long-range
antiferromagnetic order coupled to structural coherence. This embedded architecture establishes a platform for exploring coherent
spin-
photon interactions and functional X-ray scattering in
magnetic metamaterials free from lithographic topography and disorder.
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All authors Christina Vantaraki, Oier Bikondoa, Matías P. Grassi, Brindaban Ojha, Alkaios Stamatelatos, Natalia Kwiatek-Maroszek, Miguel Angel Niño Orti, Michael Foerster, Thomas Saerbeck, Daniel Primetzhofer, Max Wolff, Nicolas Jaouen, Thomas P. A. Hase, Vassilios Kapaklis