Fluid Dynamics
Online learning of subgrid-scale models for quasi-geostrophic turbulence in planetary interiors
Hugo Frezat, Thomas Gastine and Alexandre Fournier
Machine learning approaches to subgrid-scale (SGS) modeling are now well established in atmospheric and oceanic applications. Among these, online end-to-end learning, where the differentiable solver participates in the training, has shown particular promise. Yet existing studies are largely restricted to idealised periodic domains, with no mechanical boundaries, precluding them from addressing the dynamics of bounded rotating flows relevant to planetary interiors. Here we consider two-dimensional quasi-geostrophic
turbulence in a rapidly rotating annular bounded domain. We examine three configurations varying the geometry of the container and the rotation rate. The system exhibits key features such as zonal jets, Rossby waves, and, in the spherical shell geometry, a slow quasi-periodic inward drift of the jets. The spectral properties of the zonal and non-zonal flow can be understood in the framework of zonostrophic
turbulence theory. We develop a differentiable solver for this system, which allows us to train SGS models online, over a time span of one turnover time, using coarse-grained data from direct numerical simulations. In all cases, a SGS model trained on a single turnover time accurately reproduces global integrated diagnostics, energy spectra as well as long-term dynamical behaviours, such as jet migration, occurring on timescales which exceed the training window by one order of magnitude. The online-trained model further outperforms classical hyperdiffusivity and Leith closure schemes, for which reducing the radial resolution is impractical. The resulting speed-up paves the way to further investigations of long-term geophysical fluid processes beyond the reach of direct numerical simulations.
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Fluid Dynamics
Uncertainty analysis of URANS simulations coupled with an anisotropic pressure fluctuation model
Ali Eidi and Richard P. Dwight
Accurate prediction of pressure and velocity fluctuations in turbulent flows is essential for understanding flow-induced vibration and structural fatigue. This study investigates the role of
turbulence model parameter uncertainty in such predictions using a combination of global sensitivity analysis, surrogate modeling, and
Bayesian inference. The methodology is applied to two fluid-only flow cases: turbulent channel flow and turbulent annular flow. In the channel flow case, calibrated parameter distributions lead to improved agreement with reference data. In the annular case, limited parameter identifiability is observed, though predictions remain consistent with high-fidelity trends. The results demonstrate both the potential and limitations of model calibration strategies in wall-bounded turbulent flows.
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Instrumentation and Detectors
Direct Neutron Detectors based on Carborane Containing Conjugated Polymers
Aled Horner, Fani E. Taifakou, Choudhry Z. Amjad et al. (+6 more)
Thermal
neutron detectors are crucial to a wide range of applications, including
nuclear safety and security, cancer treatment, space research, non-destructive testing, and more. However,
neutrons are notoriously difficult to capture due to their absence of charge, and only a handful of isotopes have a sufficient
neutron cross-section. Meanwhile, commercially available $^3$He gas filled proportional counters suffer from depleting $^3$He feedstocks and complex device structures. In this work, we explore the potential of a carborane containing conjugated
polymer ($o$CbT$_2$-NDI) as a thermal
neutron detector. The natural abundance of $^{10}$B in such a
polymer enables intrinsic thermal
neutron capture of the material, making it the first demonstration of an organic
semiconductor with such capabilities. In addition, we show that thermal
neutron detection can be achieved also by adding a $^{10}$B$_4$C sensitiser additive to the analogous carborane-free
polymer PNDI(2OD)2T, whereas unsensitised PNDI(2OD)2T control devices only respond to the fast
neutron component of the radiation field. This approach allows us to disentangle the fast and thermal
neutron responses of the devices tested and compare the relative performance of the two approaches to thermal
neutron detection. Both the carborane containing and the $^{10}$B$_4$C sensitised devices displayed enhancement due to thermal
neutrons, above that of the unsensitised
polymer. The detector response is found to be linear with flux up to $1.796\,\times\,10^7\,$cm$^{-2}$s$^{-1}$ n$_{th}\bar{v}$ and saturates at high drive biases. This study demonstrates the viability of carboranyl
polymers as
neutron detectors, highlights the inherent chemical tuneability of organic
semiconductors, and opens the possibility of their application to a number of different low-cost, scalable, and easily processable detector technologies.
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All authors Aled Horner, Fani E. Taifakou, Choudhry Z. Amjad, Filip Aniés, Elizabeth George, Chris Allwork, Adrian J. Bevan, Martin Heeney, Theo Kreouzis
Optics
Fractal Polariton Topological Insulator
Khalil Sabour and Yaroslav V. Kartashov
We introduce higher order polariton topological
insulator (HOTI) realized with fractal array of microcavity pillars arranged into Sierpinski gasket-like geometry. This system exhibiting self similarity in different generations, can support localized modes either in the external or multiple internal corners depending on the controllable distortion introduced into first generation structure. Nonlinear corner modes can be selectively excited in this dissipative system using resonant optical pumping. Strong polariton-polariton interactions lead to tilt of the resonance curves and bistability as the pump amplitude increases, allowing control over corner state profiles. Linear stability analysis illustrates dynamical stability of such states. Our results show how nontrivial
topology manifests itself in self similar, aperiodic structures and indicate that fractal geometry may bring qualitatively new localization scenarios in polariton HOTIs.
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Fluid Dynamics
Physics-guided Bayesian neural networks for zonal corrections and uncertainty quantification in separated flows
Ali Eidi, Tyler Buchanan, Letian Jiang et al. (+1 more)
Data-driven techniques have improved the accuracy of Reynolds-averaged Navier-Stokes (RANS) models in fluid dynamics. However, modeling separated flows remains challenging due to their complex physics and sensitivity to local conditions. Existing approaches often struggle with generalization beyond training cases and lack robust uncertainty quantification frameworks, limiting their utility in complex flow regimes. We propose a
Bayesian neural network (BNN)-based framework specifically designed for two-dimensional separated flows. By focusing on flow zones near separated regions, we ensure targeted training and enhance predictive reliability. The BNN framework incorporates physics-guided, invariant inputs to maintain consistency with
turbulence physics. Correction terms predicted by the BNN are selectively applied to specific regions of the flow domain using a novel classifier, improving accuracy. A key feature of this approach is propagating BNN-derived corrections to flow solutions, enabling uncertainty quantification in unseen test cases. This probabilistic characterization of modeling errors offers insights into the reliability of RANS predictions across geometries with similar
topologies. Preliminary results demonstrate that this method accurately predicts correction terms for Reynolds stress anisotropy and turbulent kinetic energy production in separated flow regions, effectively addressing dominant modeling errors and advancing
turbulence modeling through uncertainty quantification.
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All authors Ali Eidi, Tyler Buchanan, Letian Jiang, Richard P. Dwight
Medical Physics · Cross-listed from cond-mat.mtrl-sci
Composition-Dependent Properties of $\mathrm{Ce_{x}La_{0.95-x}Tb_{0.05}F_{3}}$ Nanopowders Tailored for X-Ray Photodynamic Therapy and Cathodoluminescence Imaging
Xenie Lytvynenko, Marie Urbanová, Ondřej Lalinský et al. (+4 more)
This study investigates the synthesis and luminescence behavior of $\mathrm{Ce_{x}La_{0.95-x}Tb_{0.05}F_{3}}$ nanoparticles with varying $\mathrm{Ce^{3+}}$ content. The materials were prepared via a wet chemical route and thermally annealed to improve crystallinity and reduce defects. Phase composition and structural parameters were examined by X-ray diffraction (XRD), while elemental composition was determined by X-ray fluorescence (XRF). Cathodoluminescence (CL) intensity mapping was used to evaluate emission uniformity and monitor the degradation of luminescence under electron beam exposure. Photoluminescence (PL) and radioluminescence (RL) spectroscopy confirmed energy transfer from $\mathrm{Ce^{3+}}$ to $\mathrm{Tb^{3+}}$ ions. Luminescence intensities were found to depend strongly on both Ce content and thermal treatment. The results contribute to the understanding of defect-related quenching mechanisms and are relevant for the design of rare-earth-based luminescent nanomaterials for biomedical applications.
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All authors Xenie Lytvynenko, Marie Urbanová, Ondřej Lalinský, Vilém Vojta, Jan Bárta, Lenka Prouzová Procházková, Václav Čuba
Fluid Dynamics
Solving Navier-Stokes Equations Using Data-free Physics-Informed Neural Networks With Hard Boundary Conditions
Ritik Pal, Soubhik Mukherjee, Urmi Dutta et al. (+1 more)
In recent years, Physics-Informed Neural Networks (PINNs) have emerged as a powerful and robust framework for solving nonlinear differential equations across a wide range of scientific and engineering disciplines, including biology, geophysics, astrophysics and fluid dynamics. In the PINN framework, the governing partial differential equations, along with initial and boundary conditions, are encoded directly into the loss function, enabling the network to learn solutions that are consistent with the underlying physics. In this work, we employ the PINN framework to solve the dimensionless Navier-Stokes equations for three two-dimensional incompressible, steady, laminar flow problems without using any labeled data. The boundary and initial conditions are enforced in a hard manner, ensuring they are satisfied exactly rather than penalized during training. We validate the PINN predicted velocity profiles, drag coefficients and pressure profiles against the conventional computational fluid dynamics (CFD) simulations for moderate to high values of Reynolds number ($Re$). It is observed that the PINN predictions show good agreement with the CFD results at lower $Re$. We also extend our analysis to a transient condition and find that our method is equally capable of simulating complex time-dependent flow dynamics. To quantitatively assess the accuracy, we compute the $L_2$ normalized error, which lies in the range $\mathcal{O}(10^{-4})$ - $\mathcal{O}(10^{-1})$ for our chosen case studies.
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All authors Ritik Pal, Soubhik Mukherjee, Urmi Dutta, Arghya Choudhury
Classical Physics
A reinterpretation of classical magnetism via the regular representation of displacement current
Jin Jer Huang
The displacement current, introduced by Maxwell, has long caused confusion regarding its relationship to the
magnetic field. To develop a new understanding of classical magnetism, this work first decomposes the displacement current into a localized internal component and an external field distribution by employing a discal regularization of the dipole distribution. Due to a surprising cancellation of the electric current by this internal displacement current, an integral expression for the
magnetic field in terms of the external displacement current (or total current) enables a reinterpretation of classical magnetism. Consequently, the current equivalence is resolved and refined through this integral representation in both quasi-steady and quasi-static states. In the latter case, the nonzero external displacement current is exactly equal to—and can easily be mistaken for—the current in the
circuit system. This paper proposes a potential solution to several longstanding historical disputes concerning the displacement current.
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Applied Physics
Secondary electron topographical contrast formation in scanning transmission electron microscopy
Evgenii Vlasov, Wouter Heyvaert, Tom Stoops et al. (+3 more)
Secondary electron (SE) imaging offers a powerful complementary capabilities to conventional scanning transmission electron microscopy (STEM) by providing surface-sensitive, pseudo-3D topographic information. However, contrast interpretation of such images remains empirical due to complex interactions of emitted SE with the
magnetic field in the objective field of TEM. Here, we propose an analytical physical model that takes into account the physics of SE emission and interaction of the emitted SEs with
magnetic field. This enables more reliable image interpretation and potentially lay the foundation for novel 3D surface reconstruction
algorithms.
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All authors Evgenii Vlasov, Wouter Heyvaert, Tom Stoops, Sandra Van Aert, Johan Verbeeck, Sara Bals
Optics
Reflectivity-based refractive index measurement of van der Waals materials
Xavier Zambrana-Puyalto, Alexander Johan Olsen and Søren Raza
We present a reflectivity-based method for measuring the in-plane refractive index of transparent van der Waals (vdW) materials. The approach enables the characterization of as small as $3 \times 3$~{\textmu}m$^2$ exfoliated flakes on a non-transmissive substrate without assuming any specific spectral shape of the refractive index. Exfoliated flakes are most commonly obtained through mechanical exfoliation, which generally produces vdW flakes with tens-of-micron lateral dimensions. As a result, conventional ellipsometry - which depends on large, uniform areas and specific spectral models - becomes challenging to apply. Our method determines the refractive index directly from the spectral position of reflectivity minima, provided the flake thickness and the substrate complex refractive index are known. We demonstrate the technique on hafnium disulfide (HfS$_2$), a vdW
semiconductor with high refractive index and low absorption, retrieving its in-plane refractive index across the visible range. The results both validate previous ellipsometry measurements and establish this method as an accessible and spectral-model-free alternative for refractive index characterization of vdW materials.
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Fluid Dynamics
Metric Geometry Governs Optimal Control in Driven Stokes Flows: Magnetic Driving and Beyond
Kyle McKee
In a canonical Stokes flow geometry, the Hele-Shaw
cell, we show that tunable circulations induced by Lorentz forces in a conducting fluid enable particle control. We reveal that energy-optimal control paths correspond to geodesics of an emergent Riemannian metric defined over the fluid domain, which are time-optimal under a maximum-power constraint. Subject to random boundary forcing, particle paths exhibit metric-governed anisotropic
diffusion. Our
geometric concepts governing optimal control, though developed explicitly for circulation-driven flows, generalize to generic driven Stokes flows and so elucidate recent observations in a three-dimensional context.
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History and Philosophy of Physics
MacLaurin and Morality
Isobel Falconer and David Horowitz
Scottish mathematician Colin MacLaurin (1698-1746) is best known for his A Treatise of Fluxions (1742), An Account of Sir Isaac Newton's Philosophical Discoveries (1748), and the appellation for a type of power series. However, it is hardly known that in 1714 at the age of sixteen MacLaurin penned a short manuscript wherein he tried to apply Newtonian principles to morality, in an approach to mathematization that suggests strong continuities with earlier centuries. De viribus mentium bonipetis (On the good-seeking forces of minds) remained unpublished and hidden in the papers of the Colin Campbell Collection at the University of Edinburgh for over 250 years; it was only uncovered at the end of the twentieth century. De viribus provides a remarkable glimpse into how the young MacLaurin dealt with early Newtonianism, the tenets of the Church of Scotland, and the nascent interface between science and religion just prior to the dawn of the Scottish Enlightenment. Perhaps the most intriguing aspect of De viribus is the personal snippets related to Scottish Presbyterian morality that MacLaurin interjects throughout his mathematical discussion. These are often vague and oblique, and one must look to his mathematics, his contemporaries, and the social fabric of his surroundings to understand them. In the process, one gains insight not only into MacLaurin's family background and personal religious thought, but also into the culture and nature of teaching in Scottish universities at the time. De Viribus not only demonstrates that ideas of mathematising morality were being canvassed in Scotland much earlier than the better known, but less sophisticated, later attempts by the likes of Frances Hutcheson, David Hume and George Turnbull, but suggests continuities with approaches to mathematization in earlier centuries, providing a strong bridge into the Enlightenment era.
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Computational Physics · Cross-listed from cs.LG
MiAD: Mirage Atom Diffusion for De Novo Crystal Generation
Andrey Okhotin, Maksim Nakhodnov, Nikita Kazeev et al. (+3 more)
In recent years,
diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However, most of these models don't have the ability to change the number of atoms in the
crystal during the generation process, which limits the variability of model sampling trajectories. In this paper, we demonstrate the severity of this restriction and introduce a simple yet powerful technique, mirage infusion, which enables
diffusion models to change the state of the atoms that make up the
crystal from existent to non-existent (mirage) and vice versa. We show that this technique improves model quality by up to x2.5 compared to the same model without this modification. The resulting model, Mirage Atom
Diffusion (MiAD), is an equivariant joint
diffusion model for de novo
crystal generation that is capable of altering the number of atoms during the generation process. MiAD achieves an 8.2% S.U.N. rate on the MP-20
dataset, which substantially exceeds existing state-of-the-art approaches. Code: https://github.com/andrey-okhotin/miad.git
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All authors Andrey Okhotin, Maksim Nakhodnov, Nikita Kazeev, Mikhail Lazarev, Andrey E Ustyuzhanin, Dmitry Vetrov
Biological Physics · Cross-listed from cond-mat.soft
Inertial active particles in a Poiseuille flow: negative mobility and particle separation
Ankit Gupta and P. S. Burada
The diffusive behavior of small entities is strongly influenced by the flow of the surrounding medium, which is ubiquitous in natural and artificial environments. In this study, we investigate the transport characteristics of the inertial active Brownian particles (ABPs) in a microfluidic channel under a Poiseuille flow. The interplay between the inertia of the particles and the imposed fluid flow leads to interesting diffusive behaviors. For instance, in the overdamped regime ($m \to 0$), particles exhibit a negative average velocity $\langle v \rangle$ due to upstream movement. As $m$ increases, particles tend to move along the flow direction with an increase in $\langle v \rangle$ in the positive direction, exhibiting a maximum at optimal $m$, and diminish for higher $m$ values. The effective
diffusion coefficient $D_{eff}$ also shows a peak at this optimal $m$. Interestingly, at higher $m$ values, $D_{eff}$ decreases with increasing the noise strength. The self-propelled velocity of the particles further enhances the upstream movement. Further, the rotation rate of the particles also contributes positively to the upstream motion, and enhances the
diffusion of the particles by many orders in the limit of higher $m$. This study reveals that inertia not only modifies swimmer flow interactions but also enables new dynamical regimes, where mass-dependent trajectories can be harnessed for selective control. Such control holds promise for mass based particle separation in precisely engineered environments and lab on a chip devices for technological applications.
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Classical Physics · Cross-listed from cond-mat.stat-mech
On the First Quantum Correction to the Second Virial Coefficient of a Generalized Lennard-Jones Fluid
Daniel Parejo and Andrés Santos
We derive an explicit analytic expression for the first
quantum correction to the second virial coefficient of a $d$-dimensional fluid whose particles interact via the generalized Lennard-Jones $(2n,n)$ potential. By introducing an appropriate change of variable, the correction term is reduced to a single integral that can be evaluated in closed form in terms of
parabolic cylinder or generalized Hermite functions. The resulting expression compactly incorporates both dimensionality and stiffness, providing direct access to the low- and high-temperature asymptotic regimes. In the special case of the standard Lennard-Jones fluid ($d=3$, $n=6$), the formula obtained is considerably more compact than previously reported representations based on hypergeometric functions. The knowledge of this correction allows us to determine the first
quantum contribution to the Boyle temperature, whose dependence on dimensionality and stiffness is explicitly analyzed, and enables quantitative assessment of
quantum effects in noble gases such as helium, neon, and argon. Moreover, the same methodology can be systematically extended to obtain higher-order
quantum corrections.
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Data Analysis and Statistics
Integral Bayesian symbolic regression for optimal discovery of governing equations from scarce and noisy data
Oriol Cabanas-Tirapu, Sergio Cobo-Lopez, Savannah E. Sanchez et al. (+3 more)
Understanding how systems evolve over time often requires discovering the differential equations that govern their behavior. Automatically learning these equations from experimental data is challenging when the data are noisy or limited, and existing approaches struggle, in particular, with the estimation of unobserved derivatives. Here, we introduce an integral
Bayesian symbolic regression method that learns governing equations directly from raw time-series data, without requiring manual assumptions or error-prone derivative estimation. By sampling the space of symbolic differential equations and evaluating them via numerical integration, our method robustly identifies governing equations even from noisy or scarce data. We show that this approach accurately recovers ground-truth models in synthetic
benchmarks, and that it makes quasi-optimal predictions of system dynamics for all noise regimes. Applying this method to bacterial growth experiments across multiple species and substrates, we discover novel growth equations that outperform classical models in accurately capturing all phases of microbial proliferation, including lag, exponential, and saturation. Unlike standard approaches, our method reveals subtle shifts in growth dynamics, such as double ramp-ups or non-canonical transitions, offering a deeper, data-driven understanding of microbial physiology.
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All authors Oriol Cabanas-Tirapu, Sergio Cobo-Lopez, Savannah E. Sanchez, Forest L. Rohwer, Marta Sales-Pardo, Roger Guimerà
Physics and Society
Analysis and Simulation of Cooperative Buffer Strategies for Traffic Wave Mitigation
Raphael Korbmacher, Daniel Straub, Antoine Tordeux et al. (+1 more)
Stop-and-go waves in traffic flow pose a persistent challenge, compromising safety, efficiency, and environmental sustainability. This paper presents a novel mitigation strategy identified through an exploratory multi-
agent deep reinforcement learning experiment in a ring-road environment. In this experiment, the
agents are observed to develop a driving policy in which most vehicles maintain minimal headways to maximize throughput, while a single "buffer" vehicle adopts a larger headway to absorb perturbations and prevent wave propagation. We demonstrate within a parametrised analytical framework, under a linearized long-wavelength string-stability criterion, that this strategy optimises overall flow among non-uniform time-gap distributions. We further show through simulation that adapting this cooperative strategy to classical nonlinear car-following models, such as the Intelligent Driver and Full Velocity Difference models, yields improvements in stability, traffic efficiency, and speed oscillation across broad parameter sweeps. We additionally show that sharing the buffering role among multiple vehicles reduces the per-vehicle buffering time and mitigates intra-platoon instabilities in larger platoons, which may offer a more practically feasible strategy.
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All authors Raphael Korbmacher, Daniel Straub, Antoine Tordeux, Claudia Totzeck
Plasma Physics · Cross-listed from astro-ph.SR
The Formation of Solar Prominences: Plasma Origin and Mechanisms
Yuhao Zhou
Solar prominences, or
solar filaments, are cool and dense
plasma structures in the hot
solar corona, whose formation mechanisms have remained a fundamental challenge in
solar physics. This review provides a comprehensive overview of the current theoretical, numerical, and observational understanding of prominence formation, with a focus on the origin of the dense
plasma component. We begin by summarizing the
magnetic field configurations that enable prominence support, followed by a classification of four representative
plasma formation mechanisms: injection, levitation, evaporation–condensation, and in-situ condensation. Each mechanism is analyzed in terms of its physical basis, numerical realizations, and observational diagnostics. A central focus is placed on the evaporation–condensation scenario, which has seen significant development over the past decade through numerical simulations. We also discuss recent progress in modeling in-situ condensation triggered by
magnetic reconnection and levitation dynamics. Throughout, we emphasize the complementary nature of different mechanisms and their potential coexistence in forming and maintaining prominence mass. Observational constraints and recent high-resolution data are reviewed to assess the physical plausibility of each mechanism. We conclude by highlighting open questions and future directions in connecting multi-scale physical processes to the observed diversity of prominence behaviors.
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Accelerator Physics
Determination of the total dose of bremsstrahlung X-RAY reminiscence on the high-current pulsed radiation-beam complex TEMP-B
A.B. Batrakov, S.I. Fedotov, I.N. Onishchenko et al. (+5 more)
The paper reports the results of measuring the total dose of X-ray bremsstrahlung from a powerful X-ray source based on the high-current pulsed direct-action electron accelerator Temp-B. The parameters of the high-current, tubular relativistic electron beam from the accelerator were as follows: energy 600 keV, current 13.5 kA, and pulse duration 1.0 mks. Using the pulsed
magnetic field of a solenoid, the electron beam generated in a magnetically isolated diode was transported over a 55 cm distance toward the molybdenum converter. An auxiliary coil, connected in series with the solenoid, was placed adjacent to it to provide the desired
magnetic field in the converter region and avoid beam losses. The methodology for determining the total dose of the produced X-ray bremsstrahlung is described. Polycrystalline detectors were used for measuring the X-ray bremsstrahlung dose. They were located 80 mm behind the converter in the polar plane, with a 20 deg separation. Measurements of the dose distribution over the polar angle showed symmetrical distributions of the radiation at both polar and azimuthal angles relative to the axis. Taking into account such
symmetry and the fact that the electron beam radiates solely into the forward hemisphere, the integration over the entire sphere can be reduced to integration over just a fraction of 1/8 of the spherical surface, thereby reducing the required number of sensors. The experimentally obtained value of the total dose of the X-ray bremsstrahlung is 388.5 Gy per single pulse of the accelerator current, which is concentrated in a 120 deg cone in the direction of the beam movement.
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All authors A.B. Batrakov, S.I. Fedotov, I.N. Onishchenko, E.G. Glushko, A.M. Gorban, O.L. Rak, O.V. Nevara, Yu.N. Volkov
Computational Physics · Cross-listed from nlin.AO
Oscillation Quenching Induced By Time-Varying Coupling Functions
Dushko Stavrov, Aneta Koseska and Tomislav Stankovski
The oscillatory dynamics of natural and man-made systems can be disrupted by their time-varying interactions, leading to oscillation quenching phenomena in which the oscillations are suppressed. We introduce a framework for analyzing, assessing, and controlling oscillation quenching using coupling functions. Specifically, by observing limit-cycle oscillators we investigate the bifurcations and dynamical transitions induced by time-varying diffusive and periodic coupling functions. We studied the transitions between oscillation quenching states induced by the time-varying form of the coupling function while the coupling strength is kept invariant. The time-varying periodic coupling function allowed us to identify novel, non-trivial inhomogeneous states that have not been reported previously. Furthermore, by using dynamical
Bayesian inference we have also developed a Proportional Integral (PI) controller that maintains the oscillations and \red{prevents oscillation quenching from occurring}. In addition to the present implementation and its generalizations, the framework carries broader implications for identification and control of oscillation quenching in a wide range of systems subjected to time-varying interactions.
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Atomic Physics · Cross-listed from quant-ph
Robust Two-Qubit Geometric Phase Gates using Amplitude and Frequency Ramping
Christina Bowers, Deviprasath Palani, John Barta et al. (+4 more)
We demonstrate a method for generating
entanglement between trapped atomic ions based on adiabatically ramped state-dependent forces. By ramping both the amplitude of the state-dependent force and the motional mode frequencies, we realize an entangling operation that is robust to motional mode occupation and drifts in the mode frequencies. We measure Bell state fidelities above 0.99 across a broad range of ramp parameters and with motional occupations up to 10
phonons. This technique enables high-fidelity entangling operations without ground-state cooling, has a reduced calibration overhead, and is well suited for both
quantum logic spectroscopy applications and scalable
quantum computing architectures.
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All authors Christina Bowers, Deviprasath Palani, John Barta, Tyler Guglielmo, Stephen Libby, Dietrich Leibfried, Daniel Slichter
Biological Physics · Cross-listed from quant-ph
Quantum Biology, Quantum Simulation and Quantum Coherent Devices
Rong-Hang Chen, Jing Dong, Wen Yang et al. (+2 more)
Many living organisms can exploit
quantum mechanical effects to gain distinct biological advantages. In plants, photosynthesis uses
quantum coherence to achieve near 100% efficiency in energy transfer. With advances in experimental techniques, two-dimensional electronic spectroscopy can reveal dynamic processes such as coherence and coupling within a system, and it plays an important role in studying energy transfer in photosynthesis. On the theory side, methods such as the generalized Bloch-Redfield theory and the hierarchical equations of motion are used to model photosynthetic systems.
Quantum simulation, as a high-efficiency and low-complexity approach, has also made progress across various platforms in the study of photosynthesis. In recent years, a series of studies has introduced
quantum coherence into artificial systems to enhance energy transfer efficiency, laying the groundwork for the design of coherent devices with efficient energy transport. Birds can use the weak geomagnetic field and
spin-dependent chemical reactions to detect direction. Theoretical frameworks for animal
navigation include magnetite-based mechanisms, magnetoreceptor genes, and the radical-pair mechanism.
Quantum simulations of
navigation have also advanced on multiple platforms. Inspired by animal
navigation, diverse
quantum effects have been applied to improve sensing and to support
navigation tasks. This paper presents a comprehensive review of progress on
quantum coherence in photosynthesis and avian
navigation, along with related theoretical methods,
quantum simulation approaches, and research on
quantum coherent devices.
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All authors Rong-Hang Chen, Jing Dong, Wen Yang, Qing Ai, Gui-Lu Long
Computational Physics · Cross-listed from cs.LG
Enforcing hidden physics in physics-informed neural networks
Nanxi Chen, Sifan Wang, Rujin Ma et al. (+2 more)
Physics-informed neural networks (PINNs) represent a new paradigm for solving partial differential equations (PDEs) by integrating physical laws into the learning process of neural networks. However, ensuring that such frameworks fully reflect the physical structure embedded in the governing equations remains an open challenge, particularly for maintaining robustness across diverse scientific problems. In this work, we address this issue by introducing a simple, generalized, yet robust irreversibility-regularized strategy that enforces hidden physical laws as soft constraints during training, thereby recovering the missing physics associated with irreversible processes in the conventional PINN. This approach ensures that the learned solutions consistently respect the intrinsic one-way nature of irreversible physical processes. Across a wide range of
benchmarks spanning traveling wave propagation, steady combustion, ice melting, corrosion
evolution, and crack growth, we observe substantial performance improvements over the conventional PINN, demonstrating that our regularization scheme reduces predictive errors by more than an order of magnitude, while requiring only minimal modification to existing PINN frameworks.
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All authors Nanxi Chen, Sifan Wang, Rujin Ma, Airong Chen, Chuanjie Cui
Plasma Physics
Plasma oscillations within Israel-Stewart theory
Lorenzo Gavassino
It is well known that, at zero wavenumber, the non-hydrodynamic frequencies of uncharged kinetic theory are purely imaginary. On the other hand, it was recently shown that, in resistive magnetohydrodynamics, the interplay between the Israel-Stewart relaxation equation and the Ampère-Maxwell law can give rise to a pair of oscillating non-hydrodynamic modes. In this work, we analyze this phenomenon in detail. We first demonstrate that these oscillatory modes are exact solutions of the Drude model, corresponding to ordinary
plasma oscillations. We then invoke the Onsager-Casimir principle to explain that their oscillatory nature reflects the distinct PT-transformation properties of the degrees of freedom: the distribution function is even, while the electric field is odd. Finally, we establish that, in a kinetic theory of charged particles, there can be at most one such pair of oscillatory modes per spatial dimension, while all other modes still must sit on the imaginary axis.
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Computational Physics
Stability of Extrinsic Cohesive-Zone Model with Penalty-Based Contact in Explicit Dynamic Fragmentation Simulations
Thibault Ghesquière-Diérickx, Jean-François Molinari and Guillaume Anciaux
Dynamic fragmentation simulations are essential for predicting material response at high strain rates, yet explicit dynamic simulations that combine an extrinsic cohesive-zone model (CZM) with penalty-based contact often exhibit severe instabilities. In a two-dimensional
benchmark, we observe exponential energy growth and resulting artificial fragmentation under standard contact penalty settings and time step choices, which motivates a systematic analysis of instability sources. Three mechanisms are isolated and quantified: (i) diverging initial cohesive stiffness, which constrains the stable time step; (ii) discontinuous stiffness jumps at the cohesive-contact interface; and (iii) discontinuity introduced by cohesive softening. Analytical error estimates, phase-space diagnostics, and energy growth metrics reveal that repeated cohesive-contact switching can accumulate small per-step energy errors into long-term energy drift. Within the explored parameter space, maintaining stability requires time steps well below the usual limit. To mitigate these energy artifacts, we assess an adaptive penalty strategy that ties the contact stiffness to the evolving cohesive stiffness. This modification eliminates the discontinuity and restores energy conservation, but it allows larger interpenetration, making it suitable as a diagnostic rather than a definitive remedy. Overall, our study identifies the root causes of unphysical energy drift and demonstrates that penalty-based contact is not a viable approach for long-term, energy-consistent fragmentation simulations with physically meaningful fragment statistics.
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Physics Education
Report on the Scoping Workshop on AI in Science Education Research 2025
Marcus Kubsch, Marit Kastaun, Peter Wulff et al. (+21 more)
This report summarizes the outcomes of a two-day international scoping workshop on the role of artificial intelligence (AI) in science education research. As AI rapidly reshapes scientific practice, classroom learning, and research methods, the field faces both new opportunities and significant challenges. The report clarifies key AI concepts to reduce ambiguity and reviews evidence of how AI influences scientific work, teaching practices, and disciplinary learning. It identifies how AI intersects with major areas of science education research, including curriculum development, assessment, epistemic cognition, inclusion, and teacher professional development, highlighting cases where AI can support human
reasoning and cases where it may introduce risks to equity or validity. The report also examines how AI is transforming methodological approaches across quantitative, qualitative, ethnographic, and design-based traditions, giving rise to hybrid forms of analysis that combine human and computational strengths. To guide responsible integration, a systems-thinking heuristic is introduced that helps researchers consider stakeholder needs, potential risks, and ethical constraints. The report concludes with actionable recommendations for training, infrastructure, and standards, along with guidance for funders, policymakers, professional organizations, and academic departments. The goal is to support principled and methodologically sound use of AI in science education research.
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All authors Marcus Kubsch, Marit Kastaun, Peter Wulff, Nicole Graulich, Moriah Ariely, Alexander Bergmann-Gering, Sebastian Gombert, Bor Gregorcic, Hendrik Härtig, Benedikt Heuckmann, Andrea Horbach, Christina Krist, Gerd Kortemeyer, Ben Münch, Samuel Pazicni, Joshua M. Rosenberg, Sascha Schanze, Gena Sbeglia, Vidar Skogvoll, Christophe Speroni, Christoph Thyssen, Lars-Jochen Thoms, Brandon J. Yik, Xiaoming Zhai
Applied Physics · Cross-listed from cs.ET
Empirical Quantum Advantage in Constrained Optimization from Encoded Unitary Designs
Chinonso Onah, Roman Firt and Kristel Michielsen
We introduce the Constraint-Enhanced
Quantum Approximate Optimization
Algorithm (CE-QAOA), a shallow, constraint-aware ansatz that operates inside the one-hot product space [n]^m, where m is the number of blocks and each block is initialized in an n-
qubit W_n state. We give an ancilla-free, depth-optimal encoder that prepares W_n using n-1 two-
qubit rotations per block, and a two-local block-XY mixer that preserves the one-hot
manifold and has a constant spectral gap on the one-excitation sector. At the level of expressivity, we establish per-block controllability, implying approximate universality per block. At the level of distributional behavior, we show that, after natural block and symbol permutation twirls, shallow CE-QAOA realizes an encoded
unitary 1-design and supports approximate second-moment (2-design) behavior; combined with a Paley-Zygmund argument, this yields finite-shot anticoncentration guarantees. Algorithmically, we wrap constant-depth sampling with a deterministic feasibility checker to obtain a
polynomial-time hybrid
quantum-classical solver (PHQC) that returns the best observed feasible solution in O(S n^2) time, where S is a
polynomial shot budget. We obtain two advantages. First, when CE-QAOA fixes r >= 1 locations different from the start city, we achieve a Theta(n^r) reduction in shot complexity even against a classical sampler that draws uniformly from the feasible set. Second, against a classical baseline restricted to raw bitstring sampling, we show an exp(Theta(n^2)) minimax separation. In noiseless
circuit simulations of traveling salesman problem instances with n in {4,...,10} locations from the QOPTLib
benchmark library, we recover the global optimum at depth p = 1 using
polynomial shot budgets and coarse parameter grids defined by the problem size.
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Plasma Physics · Cross-listed from astro-ph.SR
MHD waves with mixed properties / Alfvén waves with pressure variations: a review
Marcel Goossens, Iñigo Arregui, Roberto Soler et al. (+2 more)
Non-uniformity plays an important role for MHD waves. For a uniform
plasma of infinite extent the MHD waves can be subdivided in two classes with distinct properties. The first class contains the Alfvén waves. The Alfvén waves are incompressible and propagate parallel vorticity. They do not have a parallel component of displacement, they do not cause variations in pressure and are driven by
magnetic tension only. The second class contains the magneto-sonic waves. They are compressible and have a parallel component of displacement. They do not propagate parallel vorticity and are driven by pressure and
magnetic tension. In non-uniform
plasmas the situation can be very different. The clear division between Alfvén waves and magneto-sonic waves is no longer present. In a given part of the
equilibrium an MHD wave can strongly resemble a magneto-sonic wave with little or no resemblance to Alfvén waves; while in another part of the
equilibrium the MHD wave is practically an Alfvén wave, which has the amazing property of being accompanied by variations in pressure.
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All authors Marcel Goossens, Iñigo Arregui, Roberto Soler, Jaume Terradas, Tom Van Doorsselaere
Optics
Record Index-Bandgap Trade-off: CdPS3 as a High-Index van der Waals Platform for Ultraviolet-Visible Nanophotonics
M. R. Povolotskiy, A. S. Slavich, G. A. Ermolaev et al. (+21 more)
The development of nanophotonics is hindered by a fundamental trade-off between a material's refractive index (n) and its electronic bandgap (Eg), which severely restricts the choice of materials for short-wavelength applications. This challenge is particularly acute in the visible and ultraviolet (UV) spectra, where high-performance devices require materials that are simultaneously highly refractive and transparent. Here, we report on the van der Waals (vdW)
crystal cadmium phosphorus trisulfide (CdPS3) as a solution to this long-standing problem. Through comprehensive optical and structural characterization, we show that CdPS3 possesses an anomalously high in-plane refractive index across the visible spectrum approaching 3 in the near-UV, combined with a wide indirect bandgap. This combination of properties, which circumvents the empirical Moss's law, is validated by first-principles calculations and direct near-field imaging of highly confined waveguide modes. These findings establish CdPS3 as a leading material for UV-visible photonics, opening a new pathway for the development of high-density integrated
circuits and metasurfaces.
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All authors M. R. Povolotskiy, A. S. Slavich, G. A. Ermolaev, D. V. Grudinin, N. V. Pak, I. A. Zavidovskiy, K. V. Kravtsov, A. A. Minnekhanov, M. K. Tatmyshevskiy, A. V. Syuy, D. I. Yakubovsky, A. Mazitov, L. A. Klimova, A. N. Toksumakov, A. V. Melentev, E. Zhukova, D. A. Ghazaryan, G. Tselikov, I. Kruglov, S. M. Novikov, A. A. Vyshnevyy, A. V. Arsenin, K. S. Novoselov, V. S. Volkov
Computational Physics
Statistically controllable microstructure reconstruction framework for heterogeneous materials using sliced-Wasserstein metric and neural networks
Zhenchuan Ma, Qizhi Teng, Pengcheng Yan et al. (+4 more)
Heterogeneous porous materials play a crucial role in various engineering systems. Microstructure characterization and reconstruction provide effective means for modeling these materials, which are critical for conducting physical property simulations, structure-property linkage studies, and enhancing their performance across different applications. To achieve superior controllability and applicability with small sample sizes, we propose a statistically controllable microstructure reconstruction framework that integrates neural networks with sliced-Wasserstein metric. Specifically, our approach leverages local pattern distribution for microstructure characterization and employs a controlled sampling strategy to generate target distributions that satisfy given conditional parameters. A neural network-based model establishes the mapping from the input distribution to the target local pattern distribution, enabling microstructure reconstruction. Combinations of sliced-Wasserstein metric and gradient optimization techniques minimize the distance between these distributions, leading to a stable and reliable model. Our method can perform
stochastic and controllable reconstruction tasks even with small sample sizes. Additionally, it can generate large-size (e.g. 512 and 1024) 3D microstructures using a chunking strategy. By introducing spatial location masks, our method excels at generating spatially heterogeneous and complex microstructures. We conducted experiments on
stochastic reconstruction, controllable reconstruction, heterogeneous reconstruction, and large-size microstructure reconstruction across various materials. Comparative analysis through visualization, statistical measures, and physical property simulations demonstrates the effectiveness, providing new insights and possibilities for research on structure-property linkage and material inverse design.
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All authors Zhenchuan Ma, Qizhi Teng, Pengcheng Yan, Lindong Li, Kirill M. Gerke, Marina V. Karsanina, Xiaohai He