Optics
Elina A. Kokurina, Aleksandra V. Otinova, Maxim Mazanov et al. (+2 more)
We investigate a magnetophotonic
crystal formed by the pairs of layers with the opposite out-of-plane magnetization. Despite vanishing total magnetization such
antiferromagnetic pattern opens photonic bandgap, gives rise to nontrivial topological phases and enables strong cross-polarized light reflection at frequencies inside the gap which can be harnessed for magnetically tunable polarization-rotating mirrors. We systematically explore optical properties of this structure highlighting fruitful connections to topological photonics and
axion electrodynamics.
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All authors Elina A. Kokurina, Aleksandra V. Otinova, Maxim Mazanov, Vladimir I. Belotelov, Maxim A. Gorlach
Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech
Neural variational framework for random Young-diagram limit shapes
Qian Chen and Bo-Xuan Ge
We develop a structure-preserving neural variational framework for random Young-diagram ensembles, with representations adapted to the structure and scaling of each measure. The method is validated on the Plancherel, uniform, minimal-difference, and fixed-\(q\) \(q\)-Plancherel ensembles, using known asymptotic profiles only for post-training comparison. We then study a quartically deformed hook-length ensemble without assuming an analytical saddle shape. Large-\(n\) neural profiles are compared with finite-size MAP profiles obtained from exact-action searches and with mean profiles obtained from corner-transfer Metropolis–Hastings sampling. Increasing the deformation suppresses the leading rows and broadens the support, while the neural, discrete, and sampled mean profiles agree at the percent level. These results provide numerical evidence for a deformation-dependent macroscopic saddle family.
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Plasma Physics
Rotating mirror with all-directional pinch compressions - The beauty and simplicity in controlled nuclear fusion
Linjin Zheng
Controlling
nuclear fusion is so challenging that for decades, people have been asking: Are we closer to infinite clean energy? Upon the philosophy of beauty and simplicity, the current work points out that there can be a shortcut: the rotating mirror with detached electrodes and all-directional pinch compressions. This is based on the provisional patents filed recently by the University of Texas at Austin. The device combines the steady-state and fast processes in the two main streams of controlled
nuclear fusion research:
magnetic confinement fusion and inertial confinement fusion. The fuel
plasma is preheated in a steady-state process in a rotating mirror with detached electrodes and then the pinch compressions in both radial and longitudinal directions are applied as the fast process. Preheating and longitudinal compression, in addition to the radial compression, significantly boost the
nuclear fusion rate. Fast compression after the preheating limits the time for thermalization between ions and electrons and, therefore, minimizes the impact of electron bremsstrahlung radiation loss on ions. Based on the existing experimental results, the current method can be extrapolated to have the potential to reach or exceed the Lawson criterion for the first demonstration of the feasibility of peaceful usage of
nuclear fusion energy.
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Applied Physics
Watershed vs. Region Growing for Individual Tree Segmentation from Airborne LiDAR: An Urban Case Study in Bologna
Aldo Canfora, Tommaso Rondini, Matteo Falcioni et al. (+1 more)
We present a
LiDAR-based pipeline for the
segmentation and structural characterisation of tall urban vegetation in Bologna. Starting from airborne
LiDAR point clouds previously classified as high vegetation by a Random Forest model, we implement and compare two individual-tree
segmentation strategies: a watershed
algorithm applied to the Canopy Height Model and a point-wise region growing
algorithm operating directly on the three-dimensional cloud. Over an area of 12 tiles covering the Talea district, the watershed method detects a larger number of trees (7589), dominated by short trees with a height peak around 5 meters, whereas region growing yields fewer trees (6432) but retains the highest returns, yielding individuals above 40 meters, reflecting the absence of a smoothing step in the latter. We further confront the
segmentation results with the municipal Open Data catalogue Alberi in manutenzione: on a sample tile roughly half of the
LiDAR-detected trees turn out to be absent from the catalogue, several catalogued positions correspond to locations where no tree is observed, and the majority of the height records date back about two decades, which makes the
dataset unsuitable as a ground truth reference. From the segmented trees we extract height and crown radius and use them to compute preliminary vegetation indicators, namely above-ground biomass and carbon storage through allometric relations and annual pollen production through species-specific inflorescence coefficients. All results are collected in an interactive per-tree metadata map. The pipeline is modular and can be recalibrated as improved classifications, field campaigns, or complementary sensing technologies become available, providing a scalable basis for data-driven urban greenery planning.
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All authors Aldo Canfora, Tommaso Rondini, Matteo Falcioni, Mirko Degli Esposti
Computational Physics · Cross-listed from cond-mat.mes-hall
Anisotropic Spin Polarization and magnetic spin hall effect in Ferromagnets
Jiabin Wang, Zhenhua Zhang, Wancheng Zhang et al. (+4 more)
Spin-dependent transport in ferromagnets underpins the development of high-density spintronic
memories.
Spin-dependent transport in strong
spin-orbit-coupled ferromagnets exhibits a significant anisotropy. Both the overall
spin polarization during charge transport and the
magnetic spin Hall conductivity are found to exhibit pronounced anisotropy when the magnetization is tilted away from the crystallographic easy axis or when the electric field is rotated relative to the
crystal axes. These anisotropic responses originate primarily from
spin-orbit coupling, which is identified as the key driver of the large anisotropy observed in ferromagnet. Furthermore, strain tunability of the
magnetic spin Hall anisotropy is demonstrated, with tensile strain progressively enhancing the oscillatory amplitude of the
spin Hall conductivity. These findings establish strong
spin-orbit-coupled ferromagnets as a platform for anisotropic
spin-current generation and field-free spintronic devices that exploit intrinsic material anisotropy for improved performance and energy efficiency.
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All authors Jiabin Wang, Zhenhua Zhang, Wancheng Zhang, Jianxiong Zhao, Yong Liu, Rui Xiong, Zhihong Lu
Chemical Physics · Cross-listed from quant-ph
Embedded quantum computing for many-body surface reaction
Dedong Wan, Xiaopeng Li, Yi Fan et al. (+3 more)
Predictive simulations of catalytic interfaces require correlated electronic-structure treatments that describe localized chemical transformations while retaining the influence of the extended metallic environment. We introduce QC-DFET, a
quantum-computing density-functional
embedding framework that maps surface-reaction active spaces to compact, environment-aware
qubit Hamiltonians. A reaction-consistent active-space protocol preserves orbital continuity along reaction coordinates, while
quantum-selected configuration interaction based on measurements from the Zuchongzhi superconducting
quantum processor and strongly contracted perturbation theory capture static and dynamic correlation. On Cu(111), QC-DFET treats active spaces up to 28
qubits and is validated through a hierarchy of experimentally constrained surface-chemistry challenges. H2 dissociation/desorption tests balanced bond breaking and recombination barriers, CO adsorption tests site selectivity and metal-adsorbate bonding, and formate hydrogenation tests competing hydrogenation branches with different kinetic and thermodynamic signatures. Across these cases, QC-DFET reproduces bidirectional H2 barriers, recovers the observed top-site preference and adsorption strength of CO, and reconciles the experimentally benchmarked H2COO* reverse barrier with the lower forward barrier to HCOOH*. These results establish embedded
quantum computing as a practical route to correlated surface-reaction energetics.
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All authors Dedong Wan, Xiaopeng Li, Yi Fan, Jie Liu, Xiongzhi Zeng, Zhenyu Li
Physics Education
Grades, equity and graduation rates: a quantitative analysis of the association of learning assistants with improved student outcomes
Cassandra Paul and David Webb
In this paper we use multilevel modeling of 10 introductory courses from four STEM disciplines to quantify associations of Learning Assistants (LAs) with the student-level outcomes, pass-rates, and retention. Overall we find that having an LA in a class is associated with improvement of these metrics for all students. Fur- thermore, along with all students showing improvement, we find noticeable demographic differences in the amount of improvement. For instance, LAs seem to be associated with larger improvements in pass-rates for historically marginalized groups, and larger increases in graduation rates for women identifying as belonging to historically marginalized races and ethnicities when compared to students from the same group but with class sections without LAs. This indicates that Learning Assistant programs may be an excellent investment for diverse institutions.
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Chemical Physics
Using large language models to probe the limits of atom-centered structural descriptors
Michelangelo Domina and Michele Ceriotti
Mapping an atomic structure to a compact set of
geometric descriptors is an essential step in any machine-learning application to atomic-scale modeling. A powerful and widely-used approach can be understood as a discretization of the histogram of pair distances, triangles, etc., that results in a hierarchy of
symmetry-invariant atom-centered descriptors. Unfortunately, the lower rungs on this hierarchy (two, three, four-neighbor clusters) were found to be incomplete, with
symmetry-unrelated pairs of structures having exactly the same descriptors. However, all the “degeneracies” reported so far are resolved by considering larger clusters of neighbors to build the descriptors. We report examples of 3D structures that are indistinguishable even if one considers clusters of up to seven neighbors, and to arbitrary order when considering a practical level of discretization of the descriptors, discovered with the assistance of large
language models. The key ingredients in their construction can be traced to results that have been known for decades in different communities: the model was able to find the references and recognize their significance for the problem at hand. We believe this experiment exposes an extremely fruitful usage pattern for AI in science: translating results between different communities and application domains, accelerating the process by which serendipitous discoveries in a field become breakthroughs in another.
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Atomic Physics · Cross-listed from quant-ph
Mean-field Pulse Adaptation for the Circularization of Interacting Rydberg Atoms
Matthias Hüls, Felix Motzoi, Tommaso Calarco et al. (+1 more)
Arrays of circular Rydberg atoms provide a promising platform for
quantum simulation and computation; however, their preparation in the presence of interatomic interactions remains a major challenge. While optimal control methods have enabled the design of fast and accurate
radio-frequency pulses for the circularization of a single atom and of an atom pair, the extension to more atoms is fundamentally limited by the exponential growth of the
Hilbert space, which renders numerical simulations computationally infeasible. Here, we introduce an effective model that treats interactions within a mean-field approximation, thereby enabling the simulation of large atomic systems. Our model further enables the adaptation of pulses optimized for non-interacting atoms to interacting systems, based on the computation of a single time
evolution. For two interacting $^{87}\mathrm{Rb}$ atoms, we demonstrate that the error of our method remains below $1 \, \%$ and that our adapted pulses recover the initial performance of optimal pulses in the regime of weak to moderate interaction strengths.
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All authors Matthias Hüls, Felix Motzoi, Tommaso Calarco, Eloisa Cuestas
Applied Physics · Cross-listed from cond-mat.mes-hall
Two-magnon scattering in the framework of the Lippmann-Schwinger equation
Jorge Marquez Chavez, Ondřej Wojewoda, Yixuan Song et al. (+2 more)
Controlling
magnetic losses requires identifying the microscopic origin of extrinsic linewidth broadening. We introduce a Lippmann–Schwinger framework for two-
magnon scattering from weak defect potentials, showing that scattering is set by the overlap between the perturbation of the effective field and degenerate
spin-wave states. Applied to iron garnet films, YIG/GGG(111) and YIG/GGG(110), we identify crystallographic directions along which these perturbation are elongated. This approach provides a link between the crystallography, effective field on the sample scale and two-
magnon scattering.
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All authors Jorge Marquez Chavez, Ondřej Wojewoda, Yixuan Song, Geoffrey S. D. Beach, Caroline A. Ross
Plasma Physics
Measurement of multiple mechanical properties from multi-dimensional signals in nanosecond laser ablation via PINN
Ying Zhou, Jian Wu, Ziyuan Song et al. (+6 more)
Accurate evaluation of mechanical properties in steels under ageing or service conditions remains a major challenge. We propose a thermo-mechanical coupling framework for nanosecond
laser ablation based on energy conservation, which is embedded into a physics-informed neural network (PINN) to enable simultaneous inversion of multiple mechanical properties. A thermo-mechanical coupling coefficient is defined to uniformly describe the dynamic allocation of input
laser energy among thermal
diffusion, mechanical work and
plasma shielding across different deformation stages under
laser irradiation. Furthermore, hard-to-measure physical characteristics in the coupled equation are replaced with experimentally accessible features obtained through the simultaneous acquisition of spectroscopic, shockwave and surface-wave signals. Using 210 experimental
datasets, the framework simultaneously recovers Young's modulus, yield strength, ultimate tensile strength and micro-Vickers hardness with high accuracy (R2=0.9927, 0.9912, 0.9916 and 0.9959 respectively), significantly outperforming the baseline method (ultrasonic velocity regression for E, R2=0.0012). Comparisons with linear normalization and unconstrained neural networks demonstrate that PINN achieves near-unity accuracy through the
embedding of conservation-law constraints. Partial dependency analysis further uncovers the nonlinear coupling laws between input features and mechanical properties. The proposed paradigm, integrating conservation laws, measurable features and physics-informed learning, offers a universal approach for non-contact, high-precision and physically consistent multi-to-multi inversion of multiple material properties under nanosecond
laser ablation conditions.
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All authors Ying Zhou, Jian Wu, Ziyuan Song, Jinghui Li, Xinyu Guo, Hao Sun, Yuhua Hang, Cuixiang Pei, Xingwen Li
Geophysics · Cross-listed from math.NA
A Spectral-Domain Pseudo-Inverse Construction Method for Unitary Diagonalizable Linear Inverse Problems
Shengchang Chen
Linear inverse problems are prevalent in geophysics, signal processing, image restoration, and medical imaging. Mathematically, they can be formulated as $Gm = d$. When $G$ is ill-conditioned or singular, the problem becomes ill-posed and requires regularization methods or generalized inverse methods for a stable solution. However, both types of methods encounter significant computational difficulties for large-scale linear inverse problems. In this paper, we establish a spectral-domain pseudo-inverse construction method for a class of linear inverse problems that can be diagonalized by
unitary matrices. The core idea is to construct a stable pseudo-inverse operator directly in the transform domain, starting from the
unitary diagonalization structure of the
matrix. We first provide the analytic Singular Value Decomposition (SVD) of this class of
matrices, clarifying the correspondence between spectral decomposition and SVD. On this basis, we define spectral-domain regularization filtering factors, construct a stable spectral-domain pseudo-inverse operator, and prove its bounded stability as well as its consistency in converging to the Moore–Penrose generalized inverse. This construction is numerically equivalent to zeroth-order Tikhonov regularization and converges to the Moore–Penrose generalized inverse as $\alpha \to 0^+$, but its methodological path differs from both. This paper reveals that the stable generalized inverse of a class of structured
matrices can be directly constructed from their spectral decomposition, providing an efficient solution method for large-scale structured inverse problems. The
Fourier transform case is a special instance of this method when the
unitary matrix is taken as the discrete
Fourier transform
matrix.
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Optics
Spiking Photonic Neurons Based on Two-Section InP Quantum-Well Lasers Integrated on Silicon
Menelaos Skontranis, Benoit Charbonnier, Olivier Girard et al. (+2 more)
In this work we experimentally investigate the spiking dynamics of two-section InP
quantum-well
lasers monolithically integrated on silicon. By appropriately tuning the electrical bias conditions, we realize multiple neuronal-like operating regimes, such as integrate-and-fire and resonate-and-fire, highlighting the device's versatility as a high-speed photonic
neuron. A systematic investigation of
laser design parameters, including cavity length and gain/saturable absorber ratio, elucidates their impact on spiking-related properties (such as pulse repetition frequency) and traces the operational parameter space that unlocks stable spiking. Finally, these findings pave the way toward scalable neuromorphic photonic integrated
circuits, where low-loss silicon synapses coexist with versatile
laser neurons.
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All authors Menelaos Skontranis, Benoit Charbonnier, Olivier Girard, Adonis Bogris, Charis Mesaritakis
Classical Physics · Cross-listed from cond-mat.soft
Instability-induced bistable shape-morphing kirigami structures
Xiaoyuan Ying and Marcelo A. Dias
Deployable shape-morphing structures that transform from flat sheets into stable three-dimensional configurations are highly desirable for applications ranging from soft robotics and biomedical devices to adaptive architecture and aerospace systems. Existing kirigami-based morphing systems primarily rely on isotropic deployment, compliant soft materials, or external constraints to maintain deployed shapes, which limits
geometric programmability, structural integrity, and applicability in rigid-material systems. Here, we present an inverse design framework for anisotropic bistable kirigami structures that enables programmable shape morphing through controlled
geometric frustration and instability-induced deployment. The framework combines a semi-analytical mechanical model with geometry to establish a direct connection between
geometric transformation and the underlying energy landscape. We show that instability-induced shape morphing leads to tunable bistability and directional deployment in anisotropic kirigami structures. The results are validated through finite element simulations and experiments, demonstrating stable deployed configurations and programmable anisotropic morphing. The proposed framework further provides a general design strategy that can be integrated with various active actuation systems, enabling broader engineering applications.
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Geophysics
How to quantify earthquake predictability? Advances in earthquake forecasting and predictability limits
Jiancang Zhuang and Didier Sornette
Earthquakes resist deterministic prediction, yet their occurrence is not fully random. This paper develops a unified information-theoretic framework to quantify predictability. By reviewing Shannon
entropy and the Kullback-Leibler divergence, we formalize predictability as the
entropy gap between complete randomness and the true data-generating process and clarify how this absolute notion relates to the relative skill gains used in prospective model evaluation. Within the point-process setting, we derive
entropy rates for the Poisson process and for ETAS and identify the intrinsic predictability rate as an information gain functional of the conditional intensity. Using this lens, we summarize what is currently established about earthquake predictability in time, space, and magnitude: temporal and spatial predictability are dominated by clustering and heterogeneous background rates, while magnitude predictability requires separating marginal magnitude statistics (e.g., Gutenberg-Richter and tapered laws) from genuine inter-event dependence encoded by the multivariate magnitude distribution. Finally, we show how incorporating high-dimensional pre-event observations can increase predictability through mutual information, thereby reframing
forecasting progress as the extraction of structured dependence between available information and future seismicity. This perspective provides a coherent basis for assessing predictability limits, comparing models, and identifying where additional information and physics that are most likely to yield substantive
forecasting improvements.
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Plasma Physics
Low-Temperature Co-Fired Ceramics for a Sustainable Planar Plasma Jet with Homogeneous Plasma in Large Treatment Areas for Biomedical Applications
Ivan Gomez Ho, Hua-Lin Chen, Cheng-Han Tsai et al. (+2 more)
This study presents a portable planar argon-based
plasma jet designed for large-area biomedical applications, with an emphasis on uniform discharge, stability, and safety. The device incorporates interchangeable rear and side inlet gas adapters with restriction plates and channels to equalize gas flow, while electrodes are encapsulated in low-temperature co-fired ceramics to reduce degradation and arcing during repeated operation. Flow simulations were conducted for multiple channel configurations to ensure laminar gas distribution and uniform
plasma generation. Device performance and safety were evaluated using the kinPen MED as a reference standard. Electrical characteristics, optical emission, discharge uniformity, temperature, gas velocity, ozone generation, UV irradiance, and leakage current were systematically measured. The
plasma exhibited stable voltage, current, and power after an initial warm-up period. Temperatures stabilized within minutes, with the rear-inlet configuration demonstrating lower operating temperatures due to higher outlet gas velocity. Ozone levels remained below established safety limits, while UV exposure constrained allowable treatment times. Leakage current decreased with increasing distance and approached safety thresholds at short separations. These results demonstrate that the proposed planar
plasma jet provides stable, uniform
plasma delivery while meeting key safety requirements, supporting its potential for clinical and biomedical use.
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All authors Ivan Gomez Ho, Hua-Lin Chen, Cheng-Han Tsai, Jong-Shinn Wu, Yun-Chien Cheng
Atomic Physics
Higher-order corrections to the field shift in atomic systems
V. A. Yerokhin, Z. Harman and C. H. Keitel
Differences in
nuclear charge distributions between isotopes lead to small changes in atomic spectra known as the field shift. While largely proportional to the change in the mean-square
nuclear radius, the field shift also contains higher-order contributions with different dependencies on
nuclear moments. Their knowledge is required in searches for new physics using King plots, since they contribute to deviations from King-plot linearity observed experimentally in recent studies. We present a systematic expansion of the field-shift energies in terms of
nuclear parameters and test its validity against direct numerical calculations for H-like ions. We also compute leading- and higher-order field-shift corrections for alkali-like systems from Li-like to Rb-like ions, and find that their ratios are nearly independent of the ionic charge state, agreeing with the corresponding hydrogenic $1s$ ratios on a sub-percent level. Motivated by these findings, we introduce an approximation in which these ratios are assumed to be independent of the electronic configuration. It is shown that, within this approximation, higher-order field-shift corrections do not induce King-plot nonlinearities. We conclude that only the small contributions beyond this approximation can contribute to the nonlinearities observed experimentally.
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Optics · Cross-listed from quant-ph
Revival of transport reciprocity via quantum interference in asymmetric nonlinear devices
Rupak Bag and Dibyendu Roy
Structural asymmetry combined with optical nonlinearity often leads to nonreciprocal light transport. We explore the mechanism by which 1-
photon interference effects can revive reciprocity in such nonlinear models. To this end, we study correlated 2-
photon scattering where an artificial atom is asymmetrically (a) side-coupled to an infinite waveguide at two spatially separated points, and (b) direct-coupled to two semi-infinite waveguides. The setup (a) gives robust reciprocal transport for the two
photons. However, the setup (b) shows a transition from a nonreciprocal to a reciprocal regime by tuning the interference effect via an additional tunneling path for
photons between the two waveguides.
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Fluid Dynamics
Disentangling intermittent flow structure contributions to anomalous scaling and multifractality in turbulence
Ritwik Mukherjee and Siddhartha Mukherjee
Intermittency in
turbulence manifests as intense
vortices and sharp peaks of dissipation. Causing the breakdown of Kolmogorov's simple self-similar theory, it leads to anomalous scaling, multifractality and so far remains beyond the scope of a complete theoretical description. How intermittent flow structures influence these different measurements is not known quantitatively. With a simple filtering procedure-thresholding vorticity and inverting the Biot-Savart law to generate filtered velocity fields-we show the effects of intermittent flow structures can be disentangled. As extreme vorticity contributions to the velocity field are filtered out, the energy spectrum scaling persists, while the bottleneck is flattened, and structure function scalings tend towards their Kolmogorov values. The approach is more rapid for transverse exponents, revealing the selective importance of intensely swirling flow regions. Similarly, the extent of multifractality reduces as intermittency is filtered, shrinking the range of roughness singularity exponents. The residual fields are curiously more multifractal, but their structure begins to break away from an underlying
turbulence skeleton. The effects on
vortex stretching and strain self-amplification are quantified. Our work shows that a Biot-Savart approach can selectively remove the effects of intermittency from
turbulence, and hence from its scalings.
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Fluid Dynamics
Learning Backward Transport for Source Localization
Maurizio Carbone and Lorenzo Piro
We address the problem of locating a chemical source in a flow. Based on the duality between the concentration field and Lagrangian tracer trajectories, we interpret concentration detections as evidence of paths connecting the source to the detection points. This Schrödinger bridge formulation between plausible emission positions and detection points leverages the backward propagator of passive tracers to frame source localization as the sampling of candidate emission locations via Langevin dynamics. The associated drift reveals classical chemotaxis and cast-and-surge as complementary behaviors emerging from a single transport-based principle. Applied to olfactory search in two-dimensional
turbulence, the proposed backtracking framework outperforms classical strategies across varying wind regimes using a single, Galilean-invariant learned propagator.
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Biological Physics · Cross-listed from cond-mat.soft
Squirming motion near corrugated surfaces
Sagnik Garai, Thomas G. Fai and Christina Kurzthaler
Swimming microorganisms often operate in complex confinement, where an interplay of long-ranged hydrodynamic interactions and a short-ranged repulsive interaction can give rise to interesting dynamical behaviors. Here, we theoretically investigate the trajectories of microswimmers - modeled as squirmers - in the presence of periodic boundaries. The latter modify their swimming velocity, leading to behaviors that differ qualitatively from swimming near planar walls. Using a perturbative approach based on bispherical coordinates and the Lorentz reciprocal
theorem, we characterize the interaction between a squirmer and a periodic surface in the limit of small surface amplitude and systematically explore its dependence on the boundary corrugation wavelength, squirmer type, orientation, and swimmer-surface distance. Most importantly, our results reveal that pullers become trapped in the valley of the surface corrugations, in contrast to their sliding motion near planar walls. Furthermore, the near-surface dynamics of pushers display oscillations, reflecting the periodicity of the surface structure. A tilt of the swimmer orientation with respect to the surface corrugations results in a wave-length dependent drift that sorts pushers from pullers. These findings highlight the impact of hydrodynamic interactions in shaping microswimmer transport near structured boundaries with potential implications for microbiological phenomena, such as biofilm formation, and technological applications.
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Fluid Dynamics
Rotational equivariance and locality in data-driven subgrid-scale closures
Ryley McConkey, Julia Balla, Elyssa Hofgard et al. (+2 more)
Data-driven subgrid-scale closures for large eddy simulation are of significant interest in many engineering and geoscience applications. In this context, several important questions remain about the role of rotational equivariance as an inductive bias for learned tensorial mappings. We investigate whether equivariance improves accuracy, parameter efficiency, and generalization for subgrid-scale modelling at realistic filter ratios. For turbulent channel flow, we compare data-augmented non-equivariant architectures to those with equivariance as an inductive bias. We compare both pointwise and nonlocal versions of these two model classes. All models are evaluated at matched parameter counts across spatiotemporal, anisotropy, and Reynolds number generalization. We show that non-augmented models learn a small degree of equivariance directly from
turbulence data, especially when that data is more isotropic. The equivariant nonlocal architecture attains the highest correlation coefficient on every generalization test at approximately half the parameter count of its non-equivariant counterpart, while the pointwise architectures do not improve on the analytical Clark baseline. Additionally, the equivariant model is more data-efficient than a non-equivariant model. The benefit of equivariance grows with the receptive field of the model, indicating that equivariance and nonlocality are both useful for the subgrid-scale closure task at realistic
dataset size, parameter counts, and filter size.
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All authors Ryley McConkey, Julia Balla, Elyssa Hofgard, Tess Smidt, Abigail Bodner
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Inferring Magnetic Material Parameters from Statistical Measures in Strongly Fluctuating Magnetization Dynamics
Kübra Kalkan, Atreya Majumdar, Ross Knapman et al. (+5 more)
Magnetic material parameters such as the exchange stiffness and
magnetic anisotropy govern the behavior and functionality of
magnetic systems, yet their local inference from magnetization data remains challenging, particularly in strongly fluctuating regimes with polycrystalline or multiphase microstructure, where conventional texture-based methods become unreliable. We introduce a magnetization-only framework for inferring material parameters from thermally driven magnetization dynamics. Using micromagnetic simulations, we extract statistical quantities such as temporal mean and latent
entropy from the magnetization dynamics, fit models to these descriptors, and invert the models to infer material parameters. We show that this framework enables material-parameter inference as well as grain-boundary detection in a heterogeneous sample. Among the descriptors considered, latent
entropy yields more accurate parameter estimates than the temporal mean. Our results establish latent
entropy as an efficient descriptor for inferring
magnetic material parameters from dynamical magnetization data and point toward its use for experimental parameter extraction at high temperatures and, more broadly, under strongly fluctuating conditions.
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All authors Kübra Kalkan, Atreya Majumdar, Ross Knapman, Omer Fetai, Franziska Scheibel, Sabrina Disch, Illia Horenko, Karin Everschor-Sitte
Space Physics · Cross-listed from astro-ph.SR
An interstellar energetic and non-aqueous pathway to peptide formation
Alfred Thomas Hopkinson, Ann Mary Wilson, Joe Pitfield et al. (+13 more)
The origin of the molecular building blocks of life is a central question in science. A few $\alpha$-amino acids such as glycine, the simplest proteinogenic amino acid, have been detected in meteorites and comets, indicating an extraterrestrial origin for some prebiotic molecules. However, the formation of peptides, short chains of $\alpha$-amino acids linked by peptide bonds, under astrophysical conditions has remained unresolved. Here we show that the building blocks of
proteins can form in interstellar ice analogues exposed to ionising radiation, without the presence of
liquid water. Using isotopically labelled glycine irradiated with
protons at cryogenic temperatures, we detect the formation of glycylglycine, the simplest dipeptide, along with deuterated and non-deuterated water as by-products. Peptide bond formation is confirmed by infrared spectroscopy and high-resolution mass spectrometry, which also reveal the production of other complex organic species. These findings demonstrate a non-aqueous route to peptide formation under space-like conditions and suggest that such molecules could form in the cold interstellar medium and be incorporated into forming planetary systems. Our results challenge aqueous-centric models of early biochemical
evolution and broaden potential settings for the origins of life.
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All authors Alfred Thomas Hopkinson, Ann Mary Wilson, Joe Pitfield, Alejandra Traspas Muiña, Richárd Rácz, Duncan V. Mifsud, Péter Herczku, Gergő Lakatos, Béla Sulik, Zoltán Juhász, Sándor Biri, Robert W. McCullough, Nigel J. Mason, Carsten Scavenius, Liv Hornekær, Sergio Ioppolo
Fluid Dynamics
Can we live Danckwerts' dream? Mixing Analysis in a Baffled Stirred Tank Reactor Based on 4D-Particle Tracking Experiments
Eike Steuwe, Thanh Tung Thai, Christian Weiland et al. (+5 more)
We present an experimental investigation of mixing dynamics within a laboratory-scale 3-liter stirred tank reactor (STR) equipped with two Rushton turbines and three baffles. Using time-resolved, four-dimensional particle tracking velocimetry, we successfully capture trajectories of up to 40,000 tracer particles in the full reactor volume despite obstructions by stirrer and baffles, providing unprecedented time-resolved flow and mixing information. From these Lagrangian data, we analyze velocities, accelerations, and spatial dispersion, revealing anisotropic mixing. By utilizing novel network-based analysis methods on the experimental particle trajectories, we identify coherent fluid compartments that exhibit strong internal mixing but weak exchange with neighboring compartments. We uncover five distinct compartments acting as transport barriers, which have a high impact on substrate distribution in chemical and biochemical processes. Our approach thus realizes and extends early thought experiments from Danckwerts and Levenspiel by providing detailed insight into the behavior of single fluid parcels and Lagrangian mixing withing chemical and biochemical reactors, offering a valuable approach for evaluation and optimization of chemical and biochemical processes. The trajectory data are made freely available to serve as an experimental reference for further research.
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All authors Eike Steuwe, Thanh Tung Thai, Christian Weiland, Anna Klünker, Jan Hendrick Nissen, Michael Schlüter, Kathrin Padberg-Gehle, Alexandra von Kameke
Biological Physics
Navigation driven by bidirectional information transmission between sensing and actuation
Avishek Das and Pieter Rein ten Wolde
A wide variety of biological functions are driven by feedback between sensing and actuation. A paradigmatic example is cellular
navigation. During
navigation, the sensory system maps the environmental input signal onto a sensory output, which then drives an actuation response, thereby changing the future sensory input. How the accuracy of this bidirectional information transmission controls
navigation is not currently understood. Here, we study how information controls
navigation by analytically solving two generic models that describe two major classes of biological navigators: spatial- and temporal-sensing
cells. We find that, in the linear-response regime of shallow gradients,
navigation performance is fully determined by bidirectional information transmission alone, as quantified by feedforward and feedback transfer
entropies. Elementary system parameters affect
navigation performance only through their effect on these information flows. We call these relations Behavioral Equations of State (BESTs): equalities that map information to function in a system-independent way. BESTs predict an experimentally testable data collapse for the performance of navigators with different sensing and actuation parameters. We test the validity of our theory by performing
stochastic simulations of chemotaxis of the bacterium Escherichia coli, computing the relevant transfer
entropies exactly with the TE-PWS
algorithm. The observed performance obeys the BEST without any fitting or scaling parameters. Thus, our theory identifies bidirectional information transmission between sensing and actuation as an organizing principle for
navigation.
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Biological Physics · Cross-listed from math.AP
Nonlinear Stability and Instability of Finite-Energy Solutions of the Compressible Euler-Riesz Equations with General Pressure Laws
Jose A. Carrillo, Samuel R. Charles, Gui-Qiang G. Chen et al. (+1 more)
The compressible Euler-Riesz equations arise in the modeling of a wide range of physical phenomena, including
stellar dynamics,
plasma physics, and mathematical biology. In this paper, we investigate the nonlinear stability and instability of steady states for the multidimensional compressible Euler-Riesz equations under general pressure laws. In the polytropic case, we establish the nonlinear instability of steady states in the mass-supercritical regime for attractive potentials; this is achieved by analyzing the concavity of the free energy along mass-preserving dilations. At the mass-critical exponent, we show that, for any steady state, there exist solutions that start arbitrarily close to it, but develop growing support. For general pressure laws, we employ a concentration-compactness approach to prove the existence of energy minimizers and establish the nonlinear stability of steady states. Moreover, we quantify the finite-time stability by deriving a relative
entropy bound for finite-energy solutions, without requiring uniform pointwise upper and lower bounds on the density. We further exploit the convexity of the second moment to obtain quantitative growth estimates for solutions with positive energy, thereby proving the local nature of the stability result. Finally, we prove the global existence of finite-energy weak solutions to the compressible Euler-Riesz equations with spherical
symmetry for general pressure laws via the compensated compactness method, thereby yielding unconditional stability around steady states within the class of weak solutions. The approach developed in this paper should be useful for solving other nonlinear partial differential equations involving similar difficulties.
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All authors Jose A. Carrillo, Samuel R. Charles, Gui-Qiang G. Chen, Difan Yuan
Atomic Physics · Cross-listed from nucl-th
Recent Progress in Ab-Initio Nuclear Theory for Precision Physics Searches in Muonic Atoms and Superallowed $\beta$ Decays
Simone Salvatore Li Muli and Michael Gennari
Precision tests of the Standard Model at low energy are increasingly limited by
nuclear-structure theory rather than by experiment. We review two such cases: the two-
photon-exchange correction to the Lamb shift in muonic atoms, and the \texorpdfstring{$\gamma W$}{gamma-W} box radiative correction to superallowed \texorpdfstring{$\beta$}{beta} decays. Although they probe different physics, both are governed by the same generalized hadronic tensor, so that the
chiral effective field theory
Hamiltonians and currents, Lanczos-based response methods, and
Bayesian uncertainty quantification developed for one carry over directly to the other. We summarize recent ab initio progress in light
nuclei and its impact on
nuclear charge radii, on the helium isotope-shift puzzle, and on the extraction of \texorpdfstring{$V_{ud}$}{Vud} for the top-row CKM unitarity test, and state a future outlook.
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Optics
Twin reflections from a moving space-time boundary
Yukun Yang, Hao Hu, Youxiu Yu et al. (+7 more)
An interluminal interface, a space-time boundary propagating at a velocity between the group velocities of the surrounding media, enables extraordinary wave phenomena such as nonreciprocal amplification and analogues of Hawking radiation. A particularly intriguing prediction is that such an interface splits an incident wave into three outgoing waves: one transmitted and two reflected. Yet, despite decades of theoretical study, this triple-wave scattering has remained experimentally unobserved, owing to stringent requirements on interface velocity and modulation speed. Here, we introduce a programmable spatiotemporal microstrip transmission-line platform that realizes step-modulated interluminal interfaces with controlled velocity. Using this system, we report the direct observation of bi-reflection from an interluminal interface, confirming the emergence of two distinct reflected waves alongside a transmitted one. Measured frequencies and scattering coefficients show excellent agreement with longstanding theoretical predictions. Furthermore, we reveal that the two reflections possess fundamentally different causal
symmetries: one is spatially inverted, while the other is time-reversed. These findings resolve a half-century-old puzzle in moving-boundary electrodynamics and establish a versatile experimental platform for studying wave interaction with dynamic interfaces. Our work opens pathways to velocity-independent scattering devices, broadband frequency conversion, and advanced spatiotemporal wave engineering.
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All authors Yukun Yang, Hao Hu, Youxiu Yu, Linyang Zou, Liangliang Liu, Jiang Xiong, Baile Zhang, Francisco J. Garcia-Vidal, Zhuo Li, Yu Luo
Fluid Dynamics
Enhancing hydrogen production in alkaline electrolyzers by using an ultra-fast kinetics surfactant
D. Fernández-Martínez, G. Martín-Zazo, A. Rubio-González et al. (+4 more)
We study the effect of surfactant adsorption kinetics on water electrolysis performance using Surfynol 465, an ultrafast-kinetics surfactant. High-speed optical diagnostics reveal that Surfynol 465 reduces bubble residence time by an order of magnitude. Compared to the slower surfactant Triton X-100 and the surfactant-free baseline, it also prevents the growth of large bubbles ($>200\ \mu$m). We validated these results on an anion-exchange membrane electrolyzer (AEMEL) test bench. Adding Surfynol 465 to the $1\text{ M KOH}$ electrolyte decreases
cell overpotential by 140–150 mV. It nearly triples the current density (from 0.13 to 0.37 A/cm$^2$ at 2 V) and increases average power by 40% during a week-long test. Furthermore, downstream corrosion analyses reveal a strong
alloy-dependent response. The surfactant reduces degradation in stainless steel and brass but accelerates corrosion in carbon steel.
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All authors D. Fernández-Martínez, G. Martín-Zazo, A. Rubio-González, J. L. Serrano-Marcos, J. M. Montanero, F. J. Perosanz, E. J. Vega