Fluid Dynamics
Non-equilibrium formulation for inertial particles in turbulent swirling flows
Bernardo L. Español, Martin Obligado, Joachim Peinke et al. (+3 more)
We study the dynamics of inertial particles in
turbulence using
datasets obtained from both direct numerical simulations and laboratory experiments of turbulent swirling flows. By analyzing time series of particle velocity increments at different scales, we show that their
evolution is consistent with a Markov process across the inertial range. This Markovian character enables a coarse-grained description of particle dynamics through a Fokker-Planck equation, from which we can extract drift and
diffusion coefficients directly from the data. The inferred coefficients reveal scale-dependent relaxation and noise amplitudes, indicative of inertial filtering and intermittency effects. Beyond the kinematic description, we analyze the thermodynamic properties of particle trajectories by computing the trajectory-dependent
entropy production. We show that the statistics of
entropy fluctuations satisfy both the Integral Fluctuation
Theorem and, under certain conditions, the Detailed Fluctuation
Theorem. These results establish a quantitative bridge between
stochastic thermodynamics and particle-laden flows, and open the door to modeling turbulent transport using effective
stochastic theories constrained by data and physical consistency.
Read the full abstract ▾
All authors Bernardo L. Español, Martin Obligado, Joachim Peinke, Marcelo Noseda, Pablo J. Cobelli, Pablo D. Mininni
Computational Physics
A Continuous Nonlinear Optimization Perspective on the Spin Glass Problem
Phil Duxbury, Carlile Lavor and Luiz Leduino de Salles-Neto
We present a continuous nonlinear optimization model for the
Spin Glass Problem (SGP), building on a classical result by Rosenberg (1972), which shows that for a class of multilinear
polynomial problems the optimal values of the continuous relaxation and the corresponding discrete model coincide. Using the SGP as a case study, we provide a simple, problem-specific argument showing how any optimal solution returned by a continuous solver can be converted into an optimal discrete
spin configuration, even when the solver outputs non-integer values. The relaxed model remains nonconvex and does not alter the inherent computational hardness of the problem, but it offers a direct and conceptually transparent continuous formulation that can be handled by modern global optimization software. Computational experiments on standard
benchmark instances indicate that this approach can match, and in several cases surpass, recent integer programming linearization techniques, making it a practical and complementary tool for researchers working at the interface between statistical physics and combinatorial optimization.
Read the full abstract ▾
Instrumentation and Detectors
Construction and characterization of a seven-chip GridPix X-ray detector for solar axion searches
Klaus Desch, Jochen Kaminski, Christoph Krieger et al. (+2 more)
In the scope of
solar axion searches, detectors which are able to measure low energetic X-rays with high efficiency are required. For this purpose a detector based on the GridPix technology was built for the CAST experiment at CERN. The GridPix is a pixelised readout ASIC (Timepix) with a Micromegas-like gas amplification stage (grid) built photolithographically on top. In order to reduce the detector's background level, several hardware and software vetoes were implemented. Hardware-wise, these vetoes consist of a ring of six GridPixes around a central GridPix, a readout of the induced grid signal, and two scintillators. On the software side, multiple approaches to distinguish between background events and X-ray
photons are implemented. Here, also the hardware features, like the six surrounding GridPixes, are used. The new detector was tested in a long ($3500\,\text{h}$) background data taking campaign. The performance of the new vetoes was evaluated. The detector performance itself, for low energetic X-rays, was also evaluated with a variable X-ray generator using eight different energies from 0 to $10\,\text{keV}$. The efficiency for very low energetic X-rays and the energy resolution was determined.
Read the full abstract ▾
All authors Klaus Desch, Jochen Kaminski, Christoph Krieger, Tobias Schiffer, Sebastian Schmidt
Applied Physics
Design of a High-Power and High-Efficiency GaN-HEMT VCO Based on an Inverse Class-F Amplifier
Junlin Mi, Ruinan Fan, Liping Yan et al. (+2 more)
This letter proposes a high-power and high-efficiency GaN-HEMT voltage-controlled oscillator. The VCO consists of a coupled-line coupler, an inverse class-F amplifier, and a novel frequency-tunable stepped-impedance resonator. Using a harmonic control
circuit and a parasitic parameter compensation
circuit, the power amplifier operates in the inverse class-F state to achieve high efficiency. The feedback
circuit uses a coupled-line coupler instead of the traditional coupling capacitor to control feedback power precisely. The measurement results show that the VCO with an oscillation frequency of 2.41-2.45 GHz achieves a maximum conversion efficiency of 74.5% at 2.44 GHz and an output power of 40.2 dBm. It is a candidate for the microwave source in a wireless power transmission system.
Read the full abstract ▾
All authors Junlin Mi, Ruinan Fan, Liping Yan, Yuhao Feng, Changjun Liu
Geophysics
Sensitivity of neutrino oscillations to the Earth's interior properties
Isabel Goos, Nobuaki Fuji, Stéphanie Durand et al. (+4 more)
Understanding the Earth s internal structure remains a major challenge, as traditional geophysical methods face ambiguities in linking seismic observations to temperature, composition, or mass density variations. Atmospheric
neutrinos offer a complementary probe: while traversing the Earth, they undergo
flavor oscillations that depend on the local electron density, which reflects both mass density and composition.
Read the full abstract ▾
All authors Isabel Goos, Nobuaki Fuji, Stéphanie Durand, Véronique Van Elewyck, João A.B. Coelho, Eric Mittelstaedt, Yael Deniz
Chemical Physics
Enhanced quantum transport in bilayer two-dimensional materials
José Campos-Martínez and Marta I. Hernández
Two-dimensional (2D) materials have been proposed, among many other applications, as a efficient tool for the separation of atomic and molecular species and their corresponding isotopes, given the confinement provided by their subnanometric dimensions. In this work we present three dimensional
quantum wave packet calculations revealing an enhancement in the
quantum transport in
bilayer over monolayer graphdiyne membranes, one of the most popular 2D materials which is commonly employed for this purpose. Besides, resonances emerge superimposed over the typical monolayer profile for transmission probabilities, a feature that is general to other
bilayer nanoporous 2D
heterostructures and that shows a strong dependence on the interlayer separation.
Read the full abstract ▾
Optics
Optical Tautochrone and Squeezing Dynamics in Nonuniform Lattices
Ioannis Kiorpelidis, Matthias Heinrich, Alexander Szameit et al. (+2 more)
We present exact analogies between the tautochrone problem of classical mechanics and the squeezed states of
quantum optics to optical
lattices. Both phenomena emerge in the same physical system, that of waveguide arrays with nonuniform couplings. Extension to two dimensions yields Lissajous-type trajectories and multidirectional tautochrone focusing. Furthermore, we investigate the impact of Kerr nonlinearity and show that it determines the diffraction behavior, namely coherent-state-like or squeezed propagation. These
quantum inspired classical
lattices highlight the role of the coupling coefficients to beam engineering and light control in complex media.
Read the full abstract ▾
All authors Ioannis Kiorpelidis, Matthias Heinrich, Alexander Szameit, Georgios A. Siviloglou, Konstantinos G. Makris
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Size-effects on shift-current in layered CuInP$_2$S$_6$
Francesco Delodovici, Brahim Dkhil and Charles Paillard
Two-dimensional ferroelectrics have recently emerged as a promising avenue for next-generation optoelectronic and photovoltaic devices. Due to the intrinsic absence of inversion
symmetry, 2D ferroelectrics exhibit bulk photovoltaic effect (BPVE), which relies on hot, non-thermalized photo-excited carriers to generate a photo-induced current with enhanced performances thanks to efficient charge separation mechanisms. The absence of a required p-n junction architecture makes these materials particularly attractive for nanoscale energy harvesting. Recent studies have reported enhanced BPVE in nanometer-thick CuInP$_2$S$_6$ ferroelectric embedded between two
graphene wafers, driven by relatively strong polarization and reduced dimensionality. Short
circuit photocurrent density values have been observed to reach up to mA/cm$^2$. In this paper, we demonstrate that the shift-current mechanism alone cannot fully account for these high conductivity values, suggesting that additional mechanisms may play a significant role. Furthermore, our work confirms the existence of a strong size effect, which drastically reduces the shift-conductivity response in the bulk limit, in agreement with experimental observations.
Read the full abstract ▾
Medical Physics
Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm
Moritz Blumenthal, Tina Holliber, Jonathan I. Tamir et al. (+1 more)
Purpose: The Unadjusted Langevin
Algorithm (ULA) in combination with
diffusion models can generate high quality MRI reconstructions with uncertainty estimation from highly undersampled k-space data. However, sampling methods such as
diffusion posterior sampling (DPS) or likelihood annealing suffer from long reconstruction times and the need for parameter tuning. The purpose of this work is to develop a robust sampling
algorithm with fast convergence. Theory and Methods: In the reverse
diffusion process used for sampling the posterior, the exact likelihood is multiplied with the diffused prior at all noise scales. To overcome the issue of slow convergence, preconditioning is used. The method is trained on fastMRI data and tested on retrospectively undersampled
brain data of a healthy volunteer. Results: For posterior sampling in Cartesian and non-Cartesian accelerated MRI the new approach outperforms annealed sampling and DPS in terms of reconstruction speed and sample quality. Conclusion: The proposed exact likelihood with preconditioning enables rapid and reliable posterior sampling across various MRI reconstruction tasks without the need for parameter tuning.
Read the full abstract ▾
All authors Moritz Blumenthal, Tina Holliber, Jonathan I. Tamir, Martin Uecker
Data Analysis and Statistics · Cross-listed from cs.LG
Learnability Window in Gated Recurrent Neural Networks
Lorenzo Livi
We develop a statistical theory of temporal learnability in recurrent neural networks, quantifying the maximal temporal horizon $\mathcal{H}_N$ over which gradient-based learning can recover lag-dependent structure at finite sample size $N$. The theory is built on the effective learning rate envelope $f(\ell)$, a function that captures how gating mechanisms and adaptive optimizers jointly shape the coupling between state-space dynamics and parameter updates during Backpropagation Through Time. Under heavy-tailed ($\alpha$-stable) fluctuations, where empirical averages concentrate at rate $N^{-1/\kappa_\alpha}$ with $\kappa_\alpha = \alpha/(\alpha-1)$, the interplay between envelope decay and statistical concentration yields explicit scaling laws for the growth of $\mathcal{H}_N$: logarithmic,
polynomial, and exponential temporal learning regimes emerge according to the decay law of $f(\ell)$. These results identify envelope decay as the key determinant of temporal learnability. Slower attenuation of $f(\ell)$ enlarges $\mathcal{H}_N$, while heavy-tailed fluctuations compress it by weakening statistical concentration. Moreover, envelope geometry outweighs
dataset size: slowing the envelope's decay enlarges $\mathcal{H}_N$ more than adding data, so more complex architectures that realize slower-decaying envelopes can be more data-efficient than simpler ones. Experiments across multiple gated architectures and optimizers corroborate these structural predictions.
Read the full abstract ▾
Fluid Dynamics
Nature of continuous spectra in wall-bounded shearing flows of FENE-P fluids
Pratyush Kumar Mohanty, P. S. D. Surya Phani Tej, Ganesh Subramanian et al. (+1 more)
Owing to the spatially local nature of the constitutive equations typically used to model polymeric stresses, the differential operators governing the linearized dynamics of bounded viscoelastic shearing flows have singular points. As a result, the eigenspectra of such shearing flows contain, in addition to discrete
eigenvalues, continuous spectra (CS) comprising singular eigenfunctions. A clear understanding of the theoretical CS loci is crucial in discriminating physically genuine (discrete)
eigenvalues from the poorly approximated numerical CS. For rectilinear shear flows of Oldroyd-B fluids, the CS are a pair of line segments, with lengths equal to the base-state range of velocities. In this study, we provide the first comprehensive account of the nature of the CS for both rectilinear and curvilinear shearing flows of the FENE-P fluid. In stark contrast to the CS for the Oldroyd-B fluid mentioned above, we show analytically that there are up to six distinct continuous spectra for shearing flows of FENE-P fluids. When the finite extensibility parameter $L > 50$, as appropriate for large molecular weight
polymers used in experiments, three of the CS are nearly identical, and independent of the solvent-to-solution viscosity ratio ($\beta$). The other three CS are $\beta$-dependent, with one of them being the analogue of the solvent (viscous) continuous spectrum in the Oldroyd-B fluid. The remaining two $\beta$-dependent CS are novel features of the FENE-P spectrum, and can have phase speeds outside the base range of velocities, including negative ones. The complexity of the CS predicted here for shearing flows of FENE-P fluids is expected to carry over to other nonlinear viscoelastic models that exhibit a shear-thinning rheology.
Read the full abstract ▾
All authors Pratyush Kumar Mohanty, P. S. D. Surya Phani Tej, Ganesh Subramanian, V. Shankar
Fluid Dynamics
Feasibility study for physics-informed direct numerical simulation describing particle suspension in high-loaded compartments of air-segmented flow
Otto Mierka, Raphael Münster, Henrik Julian Felix Bettin et al. (+2 more)
The Archimedes Tube Crystallizer (ATC) employs air-segmented flow in coiled tubes to achieve narrow residence time distributions for continuous crystallization. Taylor and Dean
vortices drive particle suspension in this system. However, one-way coupled models fail to capture the fluid-particle feedback that becomes critical at higher loadings. We present a particle-resolved Direct Numerical Simulation (DNS) framework based on a Finite Element-Fictitious Boundary Method with hard-contact modeling of particle interactions. Simulations of L-alanine suspensions across varying particle sizes, solid contents, and rotational speeds are validated against experimental side-view imaging. Three quantitative metrics-axial distribution, radial index, and vertical asymmetry-are introduced to classify suspension regimes. The DNS results reproduce the experimentally observed flow map zones (green, yellow, red/yellow, red) and resolve subtle transitions such as rear loading and loss of vertical
symmetry. This feasibility study demonstrates that DNS can reliably predict dense suspension behavior and provides a mechanistic foundation for crystallizer design.
Read the full abstract ▾
All authors Otto Mierka, Raphael Münster, Henrik Julian Felix Bettin, Kerstin Wohlgemuth, Stefan Turek
Fluid Dynamics
Simulating non-trivial incompressible flows with a quantum lattice Boltzmann algorithm
David Jennings, Kamil Korzekwa, Matteo Lostaglio et al. (+4 more)
Quantum algorithms have been identified as a potential means to accelerate computational fluid dynamics (CFD) simulations, with the
lattice Boltzmann method (LBM) being a promising candidate for realizing
quantum speedups. Here, we extend the recent
quantum algorithm for the incompressible LBM to account for realistic fluid dynamics setups by incorporating walls, inlets, outlets, and external forcing. We analyze the associated complexity cost and show that these modifications preserve the asymptotic scaling, and potential
quantum advantage, of the original
algorithm. Moreover, to support our theoretical analysis, we provide a classical numerical study illustrating the accuracy, complexity, and convergence of the
algorithm for representative incompressible-flow cases, including the driven Taylor-Green
vortex, the lid-driven cavity flow, and the flow past a cylinder. Our results provide a pathway to accurate
quantum simulation of nonlinear fluid dynamics, and a framework for extending
quantum LBM to more challenging flow configurations.
Read the full abstract ▾
All authors David Jennings, Kamil Korzekwa, Matteo Lostaglio, Paul Mannix, Richard Ashworth, Emanuele Marsili, Stephen Rolston
Applied Physics
Zero- to low-field J-spectroscopy with a diamond magnetometer
Muhib Omar, Jingyan Xu, Raphael Kircher et al. (+8 more)
We report measurements of zero- to ultra-low-field
nuclear magnetic resonance (ZULF NMR) signals at frequencies of a few hertz with a diamond-based
magnetic sensor. The sensing diamond is a truncated pyramid with 0.18 mm height and a 0.5 mm x 0.5mm base. The minimum stand-off distance is < 1 mm, and the sensor sensitivity is 13 pT/(Hz)^(1/2) at frequencies f above 5 Hz with 1/f-like behavior at lower frequencies. NMR signals were generated via signal amplification by reversible exchange (SABRE) parahydrogen-based hyperpolarization resulting in zero-field signals at 1.7 Hz and 3.4 Hz corresponding to the expected hetero-
nuclear J-coupling pattern of acetonitrile. This work demonstrates a magnet-free platform for detecting chemically specific NMR signals at ultra-low frequencies paving the way for portable noninvasive diagnostics in microscopic sample volumes for biomedicine, industrial sensing through metal enclosures, and field-deployable
quantum analytical devices.
Read the full abstract ▾
All authors Muhib Omar, Jingyan Xu, Raphael Kircher, Pouya Sharbati, Shaowen Zhang, Georgios Chatzidrosos, James Eills, Roman Picazo-Frutos, Dmitry Budker, Danila A. Barskiy, Arne Wickenbrock
Optics
Analysis of multi-pass pumped thin-disk laser performance with measured disk deformation
Hanjin Jo, Jiří Mužík, Pawel Sikocinski et al. (+7 more)
Predicting the steady-state performance of high-power thin-disk
lasers requires not only pump-signal energy transfer but also how disk deformation contributes intra-cavity mode formation. In this work, we address the output-power reduction that occurs even when the
laser remains in a single-mode regime with $\mathrm{M^2}$ around 1.1. We developed a numerical model in which the pump-induced inversion is initialized from a non-lasing multi-pass absorption model and then coupled to two-dimensional cavity-field propagation using measured disk optical path difference (OPD) maps. Applied to the Yb:YAG thin-disk
laser, the model reproduces the residual pump fraction and predicts the signal power, beam diameter, and $\mathrm{M^2}$ with errors of 3.0$\%$, 1.7$\%$, and 0.05, respectively. To interpret the measured OPD, the disk surface is further analyzed by Zernike decomposition, and the defocus term is converted into an equivalent radius of
curvature (eROC). The eROC-based simulation provides the defocus-only reference performance that is theoretically reachable in the absence of higher-order aberrations, whereas the measured-OPD simulation reproduces the experimentally observed power reduction at high pump intensity. The comparison quantitatively shows that higher-order aberrations beyond defocus reduce the overlap with the fundamental cavity mode and limit power scaling, even before strong $\mathrm{M^2}$ degradation appears. This result identifies aberration-induced modal loss as a key limitation in high-power single-mode thin-disk
lasers.
Read the full abstract ▾
All authors Hanjin Jo, Jiří Mužík, Pawel Sikocinski, Magdalena Sawicka-Chyla, Michal Chyla, Yuya Koshiba, Yoann Levy, Kohei Hashimoto, Martin Smrž, Tomáš Mocek
Physics and Society · Cross-listed from cs.LG
Towards agent-based-model informed neural networks
Nino Antulov-Fantulin
In this article, we present a framework for designing neural networks that remain consistent with the underlying principles of
agent-based models. We begin by highlighting the limitations of standard neural differential equations in modeling complex systems, where physical invariants (like energy) are often absent but other constraints (like mass conservation, information locality, bounded rationality) must be enforced. To address this, we introduce
Agent-Based-Model informed Neural Networks (ABM-NNs), which leverage restricted graph neural networks and hierarchical decomposition to learn interpretable, structure-preserving dynamics. We validate the framework across three case studies of increasing complexity: (i) a generalized Generalized Lotka–Volterra system, where we recover ground-truth parameters from short trajectories in presence of interventions; (ii) a graph-based SIR contagion model, where our method outperforms state-of-the-art graph learning baselines (GCN, GraphSAGE, Graph
Transformer) in out-of-sample
forecasting and noise robustness; and (iii) a real-world macroeconomic model of the ten largest economies, where we learn coupled GDP dynamics from empirical data and demonstrate counterfactual analysis for policy interventions
Read the full abstract ▾
Optics
Double-flat-top half-vortices and self-bound solitary wave billiards in cubic-quintic media with intermodal attraction
Dmitry A. Zezyulin
We consider a bimodal light field envelope propagating in a bulk medium characterized by competing cubic and quintic nonlinearities. The subfields are coupled by a cross-phase modulation term and experience effective attraction. We find dynamically stable stationary states which have two distinct flat-top regions with different intensities. These solutions represent half-
vortices, where the first and second components are essentially different and, in particular, carry different topological charges: zero for one component and nonzero for the other. The typical propagation of an unstable half-
vortex leads to the splitting of the central
vortex core into several fragments which quasielastically interact with the boundary of the flat-top region. This behavior is interpreted as a self-bound solitary wave billiard, where the emerging fragments are the billiard balls and the flat-top region is the dynamically deforming table.
Read the full abstract ▾
Chemical Physics
Ferroelectricity in dipolar liquids: the role of annealed positional disorder
M. G. Izzo
Ferroelectric ordering in polar
liquids has been observed in numerical simulations and
liquid-
crystal experiments. Within mean-field framework, this behaviour remains associated with sample-shape dependent, surface contribution to the free energy, which does not vanish in the thermodynamic limit due to the long-range nature of dipolar interaction. Yet, numerical simulations performed under conducting periodic boundary conditions, for which the surface contribution vanishes, still exhibit ferroelectric order, pointing to an intrinsic bulk origin of the transition. Moving beyond the mean-field approximation, Kirkwood seminal study of the dielectric properties of polar
liquids emphasized the role of hindered dipolar rotation in shaping the corresponding pair correlations. In Kirkwood analysis, hindered rotation stems from the mean force between nearest-neighbor dipoles, placing the focus on local structure. Introducing a different perspective while retaining the central role of hindered dipolar rotation in the onset of ferroelectricity, the present study establishes, as an original finding, that annealed averaging of dipolar interaction over positional disorder generates hindered dipolar rotation favoring dipole
alignment, and able to drive a ferroelectric phase transition. As a result, unlike approaches centered on local structure, ferroelectricity emerges not in spite of the
liquid nature, but because of it. This ferroelectric phase transition is intrinsic to the bulk. Annealed averaging over positional disorder generates an effective dipolar interaction that is shorter-ranged than the bare potential, analogous to the Keesom interaction where screening arises from annealed dipolar disorder. Derived within classical density functional theory, these findings are exact in the infinite-dimensional limit and remain valid within the optimized cluster expansion for dimensions greater than two.
Read the full abstract ▾
Data Analysis and Statistics · Cross-listed from astro-ph.EP
Exoplanet formation inference using conditional invertible neural networks
Remo Burn, Victor F. Ksoll, Hubert Klahr et al. (+1 more)
The interpretation of the origin of observed
exoplanets is usually done only qualitatively due to uncertainties of key parameters in
planet formation models. To allow a quantitative methodology which traces back in time to the
planet birth locations, we train recently developed conditional invertible neural networks (cINN) on synthetic data from a global
planet formation model which tracks growth from
dust grains to evolved final
giant planets. In addition to deterministic single
planet formation runs, we also include gravitationally interacting
planets in multiplanetary systems, which include some measure of chaos. For the latter case, we treat them as individual
planets or choose the two or three
planets most likely to be discovered by
telescopes. We find that training on multiplanetary data, each
planet treated as individual point, is promising. The single-
planet data only covers a small range of
planets and does not extrapolate well to
planet properties not included in the training data. Extension to planetary systems will require more training data due to the higher dimensionality of the problem.
Read the full abstract ▾
All authors Remo Burn, Victor F. Ksoll, Hubert Klahr, Thomas Henning
Chemical Physics
On the role of nuclear quantum effects on the stability of peptides
Jing Shen, Ming-Zheng Du, Dong H. Zhang et al. (+2 more)
Nuclear quantum effects (NQEs) arising from the light mass of hydrogen can influence the structure and stability of hydrogen-bonded biomolecules, yet their role in determining peptide and
protein folding remains unclear. Experiments show that substituting H$_2$O with D$_2$O often stabilizes folded states, but the microscopic mechanism associated with this phenomena remains unresolved. Through ab initio-level path-integral molecular dynamics simulations enabled by machine-learning interatomic potentials, we address the fundamental question of the role of NQEs in peptides by investigating both their overall impact and isotope substitution effects. Overall, NQEs systematically destabilize compact three-dimensional structures across peptide systems, independent of secondary structure type or side-chain interactions. Contrary to the conventional picture that places central importance on hydrogen bonds, we find that the dominant destabilization instead arises from the
quantum C-H vibrations. In addition, we reveal microscopic insights into the stabilization of folded peptides upon H$_2$O to D$_2$O substitution, showing that the H/D isotope substitution of active peptide hydrogens, previously considered unimportant, produces free-energy changes within the range of experimentally observed shifts. These findings provide a new interpretation of isotope effects in biological systems, indicating that seemingly small H$\to$D substitutions within peptides can be as important as, or even outweigh, solvent contributions.
Read the full abstract ▾
All authors Jing Shen, Ming-Zheng Du, Dong H. Zhang, Venkat Kapil, Wei Fang
Fluid Dynamics · Cross-listed from astro-ph.EP
Radial modes of pressure bumps and dips in astrophysical discs
Armand Leclerc, Guillaume Laibe, Elliot Lynch et al. (+1 more)
This study investigates the signatures of pressure extrema on global oscillations in discs. To this end, we use the framework of wave
topology to establish a generalised local dispersion relation that includes pressure gradients. We highlight the influence of a previously unrecognized epicyclic-acoustic frequency and derive an analytical criterion for the existence of a branch of modes transiting between the inertial and the pressure bands. We find that pressure extrema consist of wave guides in which such topological modes propagate. The fundamental mode trapped at a pressure bump can propagate at all frequencies, allowing it to resonate with any temporal forcing, while the mode associated with a pressure gap propagates at a fixed frequency, propagates with arbitrary vertical phase velocity. These specific features make them attractive candidates for future discoseismology.
Read the full abstract ▾
All authors Armand Leclerc, Guillaume Laibe, Elliot Lynch, Nicolas Perez
Fluid Dynamics
A High-Order Immersed Boundary Method for Fluid-Structure Interaction Problems
Yingjie Xia, Stefano Colombo, David Huergo et al. (+3 more)
Accurate and efficient simulation of fluid-structure interaction (FSI) problems remains a central challenge in computational physics. High-order discontinuous Galerkin (DG) methods offer low numerical errors and excellent scalability on modern architectures, making them attractive for high-fidelity FSI simulations. This study presents a high-order immersed boundary method (IBM) for FSI problems which combines a volume-penalization approach with a high-order nodal DG solver. To improve near wall accuracy, an anisotropic p-adaptation strategy based on reinforcement learning is used to dynamically adjust the
polynomial orders in the mesh elements located near the moving immersed boundaries. By doing so, we show enhanced accuracy with a limited increase in computational cost. Accurate evaluation of surface forces is achieved using symmetric high-order Gaussian quadrature on immersed boundaries. The proposed method is coupled with both rigid-body and elastic-structure solvers within a partitioned framework. Numerical validations using a pitching airfoil, stall flutter of an airfoil, and flow-induced vibration of an elastic beam behind a cylinder demonstrate high-order accuracy and robustness. These results indicate that the present approach provides an effective and scalable strategy for complex moving-boundary FSI simulations.
Read the full abstract ▾
All authors Yingjie Xia, Stefano Colombo, David Huergo, Jiaqing Kou, Yuting Dai, Esteban Ferrer
Applied Physics
The tube transducer as a novel source for power ultrasound: A case study in delamination of graphite coating from lithium-ion battery anode
Shida Li, Paul Daly, Ben Jacobson et al. (+5 more)
Developing high throughput applications of sonochemistry and sonoprocessing is an outstanding ultrasonic engineering challenge that continues to limit widespread industrial adoption. Conventional mass-produced Langevin-based technologies, such as the sonotrode or cleaning bath transducers, are not particularly well suited to treating large
liquid volumes or flow-based systems, with a compromise between cavitation intensity and distribution through
liquid bulk typically required. We report on the development of a tube transducer from a single element radially poled tubular piezoceramic, excited to generate an axially focused field. High-speed imaging and sonochemiluminescence are used to characterise the cavitation generated, which is also compared to the well-known activity at the tip of a sonotrode. Tube transducer and sonotrode sonications are then assessed for the material recycling application of graphite coating delamination from lithium-ion battery anode, both for intact and flaked anode sheets. The findings show that the tube transducer generates cavitation at sonotrode-like intensities or higher but distributed throughout the bore of the tube, with peak activity at the central axis. Prospects for developing tube transducer technology for high throughput flow-based applications are discussed.
Read the full abstract ▾
All authors Shida Li, Paul Daly, Ben Jacobson, Joshua Cooke, Chunhong Lei, Andrew P. Abbott, Andrew Feeney, Paul Prentice
Physics and Society
Opinion dynamics modelling: distinct attraction and repulsion topologies highlight quantitative effects of trolling
Jake Boyce, Matteo Farina, Jody McKerral et al. (+2 more)
We introduce a model of opinion dynamics based on networked non-linear differential equations. The model combines a linear attraction with a repulsive
hyperbolic tangent interaction, labeled controversialness. For low controversialness the model displays universal consensus, which is typical of opinion models. As controversialness increases, opinion behaviours such as polarisation, clustering and dissensus emerge, dependent on the network
topology. By placing attractive and repulsive interactions on distinct networks, this model is able to simulate the manipulative effects of trolls by introducing controversy, which may be associated with mis/disinformation, toxic messaging, and encouraging provocative questioning and/or emotional posting. This work offers an analytical and statistical analysis of model results, under a wide variety of
topologies and initial conditions, whilst also generalising cluster detection
algorithms typically applied to discrete models.
Read the full abstract ▾
All authors Jake Boyce, Matteo Farina, Jody McKerral, Sergiy Shelyag, Mathew Zuparic
Chemical Physics · Cross-listed from cs.LG
Teaching Language Models Mechanistic Explainability Through MechSMILES
Théo A. Neukomm, Zlatko Jončev and Philippe Schwaller
Chemical reaction mechanisms are the foundation of how chemists evaluate reactivity and feasibility, yet current Computer-Assisted Synthesis Planning (CASP) systems operate without this mechanistic
reasoning. We introduce a computational framework that teaches
language models to predict reaction mechanisms through arrow-pushing formalism, a century-old notation that tracks electron flow while enforcing conservation of mass and charge. This mechanistic understanding enables three capabilities that are difficult or impossible with current methods: post-hoc validation of CASP proposals by reconstructing physically plausible electron pathways, holistic atom-to-atom mapping that tracks all atoms including hydrogens, and extraction of catalyst-aware reaction templates that distinguish recycled catalysts from spectator species. Central to our approach is MechSMILES, a compact textual format encoding molecular structure and electron flow through three arrow types, designed within a Python-based environment that enforces conservation laws and eliminates the possibility of atom
hallucination. We trained and benchmarked models on four mechanism prediction tasks of increasing complexity using the main mechanistic
datasets in the literature. On our most challenging task, predicting complete mechanisms given only reactants, conditions, and the desired product, our models achieve 93.2% and 73.3% pathway
retrieval on the FlowER and mech-USPTO-31k
datasets respectively, with top-3
retrieval reaching 97.6% and 86.5%. Furthermore, the framework rapidly learns new reaction classes, with strong mechanistic predictions for ozonolysis and Suzuki cross-coupling emerging from as few as 40 training examples each. By grounding predictions in physically meaningful electron movements, this work provides an architecture-agnostic, open-source foundation for more explainable and chemically valid CASP.
Read the full abstract ▾
Chemical Physics
Comparing the latent features of universal machine-learning interatomic potentials
Sofiia Chorna, Davide Tisi, Cesare Malosso et al. (+3 more)
The past few years have seen the development of “universal” machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential energy surface across a wide range of chemical structures and compositions with reasonable accuracy. While these models differ in the architecture and the
dataset used, they share the ability to compress a staggering amount of chemical information into descriptive latent features. Herein, we systematically analyze what the different uMLIPs have learned by quantitatively assessing the relative information content of their latent features with feature reconstruction errors, and observing how the trends are affected by the choice of training set and training protocol. We find that uMLIPs encode the chemical space in significantly distinct ways, with substantial cross-model feature reconstruction errors. When variants of the same model architecture are considered, trends become dependent on the
dataset, target, and training protocol of choice. We also observe that fine-tuning of a uMLIP retains a strong pre-training bias in the latent features. Finally, we discuss how atom-level features, which are directly output by MLIPs, can be compressed into global structure-level features via concatenation of progressive cumulants, each adding significantly new information about the variability across the atomic environments within a given system.
Read the full abstract ▾
All authors Sofiia Chorna, Davide Tisi, Cesare Malosso, Wei Bin How, Michele Ceriotti, Sanggyu Chong
Plasma Physics
Modeling the effect of MHD activity on runaway electron generation during SPARC disruptions
R Datta, C Clauser, N Ferraro et al. (+2 more)
Magnetohydrodynamic (MHD) instabilities and runaway electrons (REs) interact in several ways, making it important to self-consistently model these interactions for accurate predictions of RE generation and the design of mitigation strategies, such as massive gas injection (MGI). Using M3D-C1 - an extended MHD code with a RE fluid model - we investigate the effects of 3-D nonlinear MHD activity, material injection, and 2-D axisymmetric vertical displacement events (VDEs) on RE
evolution during disruptions on SPARC - a high-field, high-current tokamak designed to achieve a fusion gain Q > 1. Several cases, comprising different combinations of neon (Ne) and deuterium ($\text{D}_2$) injection, are considered. Our results demonstrate key effects that arise from the self-consistent RE + MHD coupling, such as an initial increase in RE generation due to MHD instability growth, decreased saturation energies of the m/n = 1/1 mode driving sawteeth-like activity, RE losses in
stochastic magnetic fields, and subsequent RE confinement and plateau formation due to re-healing of flux surfaces. Large RE plateaus (>5 MA) are obtained with Ne-only injection ($2-5 \times 10^{21}$ atoms), while combined $\text{D}_2$ + Ne injection ($2 \times 10^{21}$ Ne atoms; $1.8 \times 10^{22} \, \text{D}_2$ molecules) produces a lower RE current (<2 MA). With $\text{D}_2$ + Ne injection, a post thermal quench "cold" VDE terminates the RE beam, preventing a steady plateau. These simulations couple REs, 3-D MHD instabilities, MGI, and axisymmetric VDEs for the first time in SPARC disruption simulations and represent a crucial step in understanding RE generation and mitigation in high-current devices like SPARC.
Read the full abstract ▾
All authors R Datta, C Clauser, N Ferraro, R Sweeney, R A Tinguely
Chemical Physics
Measurement and assignment of E-symmetry states in the 6010-6110 cm$^{-1}$ and 8940-9150 cm$^{-1}$ ranges of methane using optical frequency comb double-resonance spectroscopy
Adrian Hjältén, Vinicius Silva de Oliveira, Michael Rey et al. (+3 more)
We use sub-Doppler optical-optical double-resonance (OODR) spectroscopy with a 3.3 ${\mu}$m single-frequency pump and a cavity-enhanced 1.65 ${\mu}$m comb probe to measure 33 ladder-type (3${\nu}$${_3}$ ${\leftarrow}$ ${\nu}$${_3}$) and 8 V-type (2${\nu}$${_3}$) transitions in the 5880-6090 cm$^{-1}$ range of methane, reaching states with rotational E
symmetry in the region of the P6 and P4 polyads, respectively. We assign the ladder-type transitions using new
Hamiltonian predictions and the ExoMol line list, and the V-type transitions using the new
Hamiltonian, ExoMol, HITRAN2020, and the WKLMC line lists. While 7 of the states in the 3${\nu}$${_3}$ range have been previously observed either in earlier OODR work (without cavity enhancement) with 1.5 MHz accuracy or in FTIR measurements of cold bands with 150 MHz resolution, the states reported here have uncertainties down to 150 kHz (5 $\times$ 10$^{-6}$ cm$^{-1}$). The E-
symmetry states exhibit first-order Stark splitting, which will be reported in our future work.
Read the full abstract ▾
All authors Adrian Hjältén, Vinicius Silva de Oliveira, Michael Rey, Isak Silander, Kevin K. Lehmann, Aleksandra Foltynowicz
Atomic Physics · Cross-listed from cond-mat.quant-gas
Quenching dynamics of vortex in spin-orbit coupled Bose-Einstein condensates
Juan Wang, Zhenze Fan and Yan Li
We investigate the ground states and rich dynamics of
vortices in
spin-orbit coupled Bose-Einstein condensates (BEC) subject to position-dependent detuning. Such a detuning plays the role of an effective rotational frequency, causing the generation of a synthetic
magnetic field. Through scanning the detuning gradient, we numerically obtain static
vortex lattice structures containing 1 to 6
vortices using the coupled Gross-Pitaevskii equations. When quenching detuning gradient below its initial value, the
vortex lattices exhibit interesting periodic rotation motion, and their dynamical stability can persist for up to 1000ms. In particular, depending on the detuning gradient, the twin
vortices exhibit either a scissors-like rotational oscillation or a clockwise periodic rotation, reflecting the response to the
magnetic field gradient experienced by the condensates. We fit the numerical results to quantitatively analyze the relation between rotation dynamics and
magnetic field gradients. When quenching the detuning gradient beyond its initial value, additional
vortices appear. Our findings may motivate further experimental studies of
vortex dynamics in synthetic
magnetic fields and offer insights for engineering a BEC-based
magnetic field gradiometer.
Read the full abstract ▾
Optics · Cross-listed from cs.CV
Physics-Informed Graph Neural Networks for Frequency-Aware Optical Aberration Correction
Yong En Kok, Bowen Deng, Alexander Bentley et al. (+4 more)
Optical aberrations significantly degrade image quality in microscopy, particularly when imaging deeper into samples. These aberrations arise from distortions in the optical wavefront and can be mathematically represented using Zernike
polynomials. Existing methods often address only mild aberrations on limited sample types and modalities, typically treating the problem as a black-box mapping without leveraging the underlying optical physics of wavefront distortions. We propose ZRNet, a physics-informed framework that jointly performs Zernike coefficient prediction and optical image Restoration. We contribute a Zernike Graph module that explicitly models physical relationships between Zernike
polynomials based on their azimuthal degrees-ensuring that learned corrections align with fundamental optical principles. To further enforce physical consistency between image restoration and Zernike prediction, we introduce a Frequency-Aware
Alignment (FAA) loss, which better aligns Zernike coefficient prediction and image features in the
Fourier domain. Extensive experiments on CytoImageNet demonstrates that our approach achieves state-of-the-art performance in both image restoration and Zernike coefficient prediction across diverse microscopy modalities and biological samples with complex, large-amplitude aberrations. We further validate on experimental PSF data from a physical microscope and demonstrate robustness to realistic sensor noise, confirming generalisation beyond simulated conditions. Code is available at https://github.com/janetkok/ZRNet.
Read the full abstract ▾
All authors Yong En Kok, Bowen Deng, Alexander Bentley, Andrew J. Parkes, Michael G. Somekh, Amanda J. Wright, Michael P. Pound