General Physics
An ideal Fermi gas under uniform gravity
Pattarapon Tanalikhit and Wittaya Kanchanapusakit
We consider an ideal
Fermi gas in a container subject to a uniform gravitational field at absolute zero temperature. Under a semiclassical approximation, we examine the density profile of the particles and derive an expression for the chemical potential. A critical value of the chemical potential separates the weak- and strong-
gravity regimes, and the kinetic and potential energies of the
Fermi gas are determined in both regimes.
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Atomic Physics
Suppression of ionization stabilization in a driven Morse-Soft-Coulomb system
Murilo D. Forlevesi, Emanuel Fernandes de Lima and Gabriel Albertin Amici
Ionization stabilization is a well-known phenomenon in strongly driven Soft-Coulomb atomic models, where the ionization probability decrease as the field amplitude increases. In this work, we investigate how this process is affected by introducing a repulsive Morse barrier into the binding potential, leading to the Morse-Soft-Coulomb (MsC) model. A systematic comparison between the Soft-Coulomb and Morse-Soft-Coulomb systems is performed for different values of the softening parameter. Ionization probabilities, escape-time and Lagrangian descriptor maps reveal that the stabilization observed in the Soft-Coulomb model is suppressed in the Morse-Soft-Coulomb system. To elucidate the origin of this behavior, we analyze the corresponding Kramers-Henneberger effective potentials. While the Soft-Coulomb model develops a symmetric double-well structure supporting a large trapping region, the Morse-Soft-Coulomb potential exhibits a single effective minimum with a significative smaller trapping region. Our result indicate that the repulsive barrier leads to the suppression of the ionization stabilization.
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Medical Physics · Cross-listed from eess.IV
Beyond Algorithms: Conceptual Innovation in Medical Imaging AI
Mark A. Anastasio
Artificial intelligence has driven rapid progress in medical imaging research, producing increasingly sophisticated
algorithms and steady improvements on
benchmark tasks. However, this
algorithm-centric trajectory has also revealed a growing imbalance: while computational methods advance rapidly, the conceptual foundations that define imaging tasks, evaluation metrics, and clinical meaning sometimes remain underexamined. In this Perspective, we distinguish algorithmic innovation, which focuses on improving computational implementations and performance within a fixed problem definition, from conceptual innovation, which reframes what problems are posed, how success is measured, and why an approach is clinically relevant. We argue that prevailing incentive structures, training pathways, and publication norms disproportionately reward algorithmic novelty, particularly for early-career researchers, while at times undervaluing conceptual contributions that are essential for scientific maturation and clinical translation. Through representative examples from medical imaging AI, we show how insufficient conceptual grounding can lead to misaligned objectives, fragile generalization, and limited real-world impact. We conclude with actionable recommendations for researchers, mentors, reviewers, and journals to better recognize, support, and integrate conceptual innovation alongside algorithmic advances.
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Plasma Physics
Bivariate incomplete-Bessel kernels for the first nonlinear Vlasov-Maxwell response
Roberto Ricci
The weakly nonlinear response of a homogeneous magnetised
plasma is usually written as a double cyclotron-harmonic expansion. This representation is explicit, but the repeated use of the Jacobi-Anger expansion produces long sums of Bessel functions and nested resonance denominators. In the linear problem we recently obtained an alternative formulation by keeping the Larmor phase unexpanded and by evaluating the characteristic integral in terms of the newly introduced incomplete-Bessel function $G_\mu(z,\psi)$. In this article we apply the same idea directly to the first nonlinear Vlasov-Maxwell equation. The nonlinear source contains the linear response of an inner mode. When this linear response is written in incomplete-Bessel form, the outer characteristic integral produces a bivariate orbit-resolvent $G_{\mu,\nu}^{(r)}(z,\psi;w,\chi)$. This function emerges therefore as the natural orbit integral generated by the first nonlinear characteristic problem. We derive the nonlinear distribution function in terms of these bivariate functions, collect the identities needed for the current projection, recover an extension of the classical double-harmonic Liu-Tripathi formula by expansion, and indicate how the nonlinear susceptibility tensor is obtained from bivariate angular contractions.
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Fluid Dynamics
Multi-objective Bayesian optimization of rigid and flexible nozzles for energy-efficient pulsed jet propulsion
Paras Singh, Yukesh Karki, Victor Hernandez et al. (+3 more)
The biomechanics of pulsed-jet propulsion in aquatic animals, including squids and jellyfish, provide valuable insights into energy-efficient locomotion. In these organisms, flexible funnel deformation enables rapid acceleration and maneuverability while minimizing energy use. Drawing inspiration from these biological systems, this study investigates performance trade-offs between rigid and flexible nozzle geometries in pulsed-jet propulsion systems. A multi-objective
Bayesian optimization framework integrated with three-dimensional fluid-structure interaction (FSI) simulations identifies nozzle designs that maximize hydrodynamic impulse and minimize jet energy input. The optimization reveals fundamentally distinct performance characteristics for rigid and flexible nozzles. Rigid nozzles achieve the highest impulse amplification, up to 5 times that of a baseline cylindrical nozzle, but at substantially increased energy expenditure. In contrast, flexible nozzles yield lower peak impulse enhancement of about 2.5 times while achieving significantly greater propulsion efficiency. The maximum normalized impulse-to-energy ratio for flexible nozzles is about 1.8 times higher than that of rigid configurations, indicating more effective conversion of input energy into useful propulsive output. Analysis of the flow physics shows that optimized rigid nozzles enhance performance through geometry-induced internal entrainment, secondary
vortex formation, and contraction-driven jet acceleration. This results in stronger
vortex circulation and downstream convection. Flexible nozzles use traveling expansion-contraction deformation waves that promote additional entrainment during expansion and accelerate the internally entrained fluid during contraction to improve pressure recovery, reduce pressure-energy expenditure, and mitigate negative pressure impulse contributions.
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All authors Paras Singh, Yukesh Karki, Victor Hernandez, Daehyun Choi, Saad Bhamla, Chandan Bose
Computational Physics
Acceleration of an algebraic multigrid pressure solver using graph neural networks
Eric Chillón, Artur K. Lidtke, Nguyen Anh Khoa Doan et al. (+1 more)
Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditional linear solvers to mesh irregularities. This work introduces a data-driven algebraic multigrid (AMG) smoother that uses a modified graph convolutional isomorphism network (GCIN). The graph neural network predicts optimal
polynomial coefficients to construct a sparse pseudo-inverse operator across diverse grid
topologies. The coefficients are optimized to reduce the residual after each V-cycle iteration. By directly capturing the algebraic structure of the system from the sparse coefficient
matrix, the proposed method maintains the solver's linearity while adapting to local anisotropies in unstructured grids. Our framework demonstrates significant performance gains by reducing the number of V-cycles required for a given tolerance and delivering wall-clock speedups from 4% to 37% across diverse
benchmarks. Notably, the model exhibits robust generalization by maintaining efficiency on meshes up to 128 times larger than those seen in training, and by accelerating the solver's convergence on unseen industry-relevant problems such as the AirfRANS
dataset.
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All authors Eric Chillón, Artur K. Lidtke, Nguyen Anh Khoa Doan, Bernat Font
Medical Physics
Introduction to Quantum Ophthalmology
Mukhit Kulmaganbetov, Dmitry Pushin, Taranjit Singh et al. (+6 more)
Quantum technologies are rapidly advancing across multiple research domains, with a growing impact on biomedical imaging and sensing. We examine their emerging role in ophthalmology through four complementary directions:
photon-limited retinal imaging, correlation based imaging, nanoscale optical probes, and
quantum-limited visual perception. Advances in optical coherence
tomography and single-
photon detection enable imaging under strict
photon budget constraints, reducing phototoxicity while preserving image quality. Correlation-based approaches, including ghost imaging, offer alternative strategies for image formation in low-light and scattering environments, although practical implementation remains limited by detection efficiency and acquisition time. In parallel, nanoscale optical platforms such as
quantum dots provide tunable and photostable probes for enhanced contrast and targeted delivery, with ongoing challenges related to biocompatibility and clinical translation. Finally, experiments at the single-
photon level and with structured light fields demonstrate how the visual system itself operates near physical detection limits and can be probed using controlled optical states. While many of these approaches remain at an early stage, they collectively illustrate how
quantum and
quantum-inspired methods may augment current ophthalmic imaging and diagnostic technologies while providing new tools for studying visual function under well-defined physical constraints.
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All authors Mukhit Kulmaganbetov, Dmitry Pushin, Taranjit Singh, Pinki Chahal, David Cory, Iman Salehi, Andrew Silva, Ben Thompson, Dusan Sarenac
Optics · Cross-listed from quant-ph
Spontaneous parametric down-conversion pumped by spatiotemporal structured light
Lukas Montenegro and Rafael F. Barros
Here we investigate the all-optical control of spectral correlations in spontaneous parametric down-conversion. We show that when
photon pairs are projected onto high-order spatial modes, the spatial structure of the pump field defines the phase-matching function of the nonlinear interaction. Thus, by structuring the pump field in both space and spectrum, the biphoton spectral correlations are fully controlled. Considering a standard periodically-poled
crystal as the nonlinear medium, we show that the Gouy phase matching method proposed here can generate both spectrally uncorrelated and high-dimensional spectrally entangled
photon pairs, similarly to what is achieved with aperiodically-poled
crystals. Furthermore, we show that our method can generate a wider class of
quantum states if the pump field is a spatiotemporal wavepacket, that is, if its spatial and spectral structures are correlated.
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Computational Physics
Discovering a well-conditioned analytic continuation problem via dictionary learning
Thomas Chuna, Phil-Alexander Hofmann, Alexander Benedix-Robles et al. (+1 more)
Many fields of physics use
quantum Monte Carlo (QMC) simulations to simulate
quantum systems in imaginary-time $\tau$ and estimate imaginary-time correlation functions (ITCF). However, extracting dynamic $\omega$-dependent quantities from ITCFs is a notoriously difficult task, known as analytic continuation (AC), that amounts to solving an exponentially ill-conditioned inverse problem. Within the AC literature, there are competing
stochastic and regularized approaches, as well as an emerging collection of works using parameterized models like neural networks. Here we transcend the traditional divides between the communities, introducing the regularized
stochastic optimization method (RSOM). This method reformulates AC as a dictionary learning problem, discovering a sparse dictionary to represent the solution. Our approach is motivated by the astounding results dictionary learning has produced in many scientific fields. Remarkably, RSOM discovers a sparse dictionary that maps an ill-conditioned inverse problem to a low-dimensional problem that is well-conditioned. We demonstrate that the method yields competitive results for common synthetic test problems as well as for authentic QMC data from the finite temperature electron gas. This work exposes that a dictionary exists within all
stochastic and regularized methods and that dictionary learning provides a new angle of attack for future AC methods.
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All authors Thomas Chuna, Phil-Alexander Hofmann, Alexander Benedix-Robles, Tobias Dornheim
Accelerator Physics · Cross-listed from cond-mat.mtrl-sci
Direct large-area observation of subsurface plastic activity in conditioned copper electrodes
Yinon Ashkenazy, Inna Popov, Victoria M. Bjelland et al. (+2 more)
High-field conditioning is the process by which
radio-frequency structures in particle accelerators and other high-gradient devices reach their operating fields, yet the underlying physical mechanism remains an open question. Models and indirect measurements point to subsurface dislocation dynamics, but large-area structural measurements have been missing. We present electron backscatter diffraction measurements spanning millimeter-scale regions on a copper cathode conditioned at pulsed direct-current fields up to $\sim$80~MV/m in a sloped-anode geometry, which imposes a known gradient of field exposure across a single electrode. Across nine regions of interest spanning this exposure range, the mean intragrain misorientation of field-exposed regions exceeds that of unexposed references by $\sim$75%; the difference is reproduced by three independent misorientation metrics and confirmed by Kolmogorov–Smirnov tests. To our knowledge, this is the first large-area observation of structural differences between conditioned and unconditioned regions of a high-field electrode. The misorientation separates into three tiers (high-field center and edge, low-field periphery, and unexposed reference) that match the spatial profile of the conditioning-state variable $E_S$ predicted by Monte Carlo simulations. These observations point to the evolving subsurface dislocation population as a candidate physical basis of conditioning.
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All authors Yinon Ashkenazy, Inna Popov, Victoria M. Bjelland, William L. Millar, Walter Wuensch
Optics
High-speed electrically driven liquid-crystal compact optical skyrmion encoder
Yu-Ping Tang, Zhenyu Guo, Ze-Yu Wang et al. (+8 more)
Optical skyrmions possess topological polarization textures that can maintain topological robustness under external perturbations, making them promising carriers for disturbance-resistant optical information transmission. However, existing optical skyrmion generation schemes mostly rely on static optical elements or fixed nanostructures, making high-speed dynamic switching of the topological state difficult. Here, we propose a high-speed switchable optical skyrmion generator based on a patterned
liquid-
crystal spin-orbit device. The device employs the in-plane orientation of
liquid crystals to imprint a fixed Pancharatnam-Berry
geometric phase, while an applied voltage rapidly tunes the
liquid-
crystal retardance, enabling reversible switching between skyrmion and non-skyrmion states. Experimental results show that the device exhibits millisecond electrical response, with bidirectional response times of 1.76 ms and 0.72 ms, corresponding to an ideal cycling rate of approximately 403 Hz, making it the fastest switchable optical skyrmion generator to date. Furthermore, by exploiting this rapid topological refreshing capability, we demonstrate image encoding and decoding, providing a new
liquid-
crystal device platform for high-speed, refreshable, and disturbance-resistant topological optical information transmission.
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All authors Yu-Ping Tang, Zhenyu Guo, Ze-Yu Wang, Le Yu, Long-Yang Wang, Nilo Mata-Cervera, Yu Wang, Ning Wang, Yijie Shen, Ling-Ling Ma, Yan-Qing Lu
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Controllable Growth and Characterization of {\alpha}- and {\beta}-phase MnSe by Chemical Vapor Deposition
Jennifer E. DeMell, Elias Kallon, Michael Pedowitz et al. (+3 more)
Manganese selenide (MnSe) is a promising air-stable two-dimensional
magnetic semiconductor for which theory predicts robust ferromagnetism in monolayers with Curie temperatures approaching 250 K. However, the crystallographic phases and
magnetic properties of thin-film MnSe grown by scalable methods remain poorly understood. Here, we demonstrate the controllable growth of ${\alpha}$- and ${\beta}$-phase MnSe on C-face sapphire using a three-zone chemical vapor deposition process with elemental Se and ${MnCl_{2}}$ precursors in an $Ar/{H_{2}}$ atmosphere. Using Raman spectroscopy, atomic force microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy, we show that our process yields phase-pure ${\alpha}$-MnSe nanorods and ${\beta}$-MnSe triangular flakes with lateral sizes up to 20 ${\mu}m$ and thicknesses of 15-30 nm. Low-temperature photoluminescence of the ${\beta}$-phase films reveals a bandgap of approximately 2.0 eV. Systematic variation of growth parameters shows that precursor vapor pressure, rather than ${H_{2}}$ partial pressure, is the dominant factor controlling lateral flake size. Vibrating-sample magnetometry measurements reveal a $N{\'e}el$ temperature of 53 K in the ${\beta}$-phase films, providing clear evidence of antiferromagnetism in the multilayer regime and establishing MnSe as a tunable platform for 2D spintronic and optoelectronic devices.
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All authors Jennifer E. DeMell, Elias Kallon, Michael Pedowitz, Jimmy C. Kostakidis, Ihteyaz Aqaeed Avash, Kevin M. Daniels
Optics
Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
Kasper Helverskov Petersen, François R J Cornet, Martin Ovesen et al. (+3 more)
Scalable prediction of optical spectra is a critical component of high-throughput materials screening for optoelectronic applications such as
solar cells. Existing surrogate models are trained on spectra computed from lower levels of theory or rely on rotation-invariant scalar features, limiting their
geometric expressiveness. We explore the use of equivariant graph neural networks for optical spectra prediction, adapting GotenNet to this task and evaluating it on multiple
datasets including a recently published collection of 10,533 structures with spectra computed at the level of the random phase approximation (RPA). The proposed model outperforms the current state of the art, with the largest gains in the 0-8 eV range and on predicting the static real permittivity, both of particular relevance for thin-film optics.
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All authors Kasper Helverskov Petersen, François R J Cornet, Martin Ovesen, Mikkel Jordahn, Kristian S. Thygesen, Mikkel N. Schmidt
Instrumentation and Detectors
Prospects for Observing Gravity-gradient Noise and Earthquake Gravity Signals with CHRONOS
Mario Juvenal S. Onglao III, Yuki Inoue and Daiki Tanabe
Ground-based gravitational-wave detectors operating in the sub-Hertz regime are expected to be strongly limited by environmental
gravity-gradient fluctuations, commonly referred to as Newtonian Noise (NN). At the same time, this frequency band provides unique opportunities to probe terrestrial gravitational perturbations associated with seismic and atmospheric processes. In this work, we investigate the feasibility of using the proposed Cryogenic sub-Hz cROss torsion-bar detector with
quantum NOn-demolition speed meter (CHRONOS) as a platform for studying
gravity-gradient noise and detecting
prompt gravitational signals from earthquakes. We model
gravity-gradient contributions from Rayleigh-wave-induced seismic fields, atmospheric infrasound fluctuations, and transient mass redistribution during earthquakes, and project these onto the CHRONOS torsion-bar response. CHRONOS achieves a peak strain sensitivity of order ~1e-18 Hz^(-1/2) near ~2 Hz. Rayleigh-wave NN is found to be the dominant environmental contribution below approximately 0.5 Hz, while atmospheric NN remains several orders of magnitude smaller throughout the frequency range considered. We further assess the detectability of
prompt gravitational signals from earthquakes. For a representative Mw = 5.2 event, sources within approximately 90 km may produce detectable signals. At 40 km distance, we obtain a signal-to-noise ratio (SNR) of approximately 3.62 integrated over the sub-Hz band, with a corresponding strain amplitude reaching the CHRONOS sensitivity curve around 0.2 to 0.6 Hz. The gravitational signal is expected to precede seismic P-wave arrival by several seconds, depending on the assumed propagation velocity. These results demonstrate the potential of CHRONOS to probe both
gravity-gradient noise and transient geophysical
gravity signals in the sub-Hertz regime.
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Optics
Extreme mid-infrared field enhancement and anapoles in high-index plasmonic metamaterials
Zoltan Sztranyovszky, Nicolas Spiesshofer, Caleb Todd et al. (+5 more)
High-refractive-index materials underpin a wide range of optical technologies, including communications, imaging,
lasers, and integrated photonic systems. Here, we demonstrate a self-assembled metamaterial platform based on gold nanoparticle aggregates with nanometer-scale gaps exhibit remarkably high effective refractive indices exceeding 15 in the mid-infrared regime, while simultaneously producing gap-field enhancements of at least two-orders of magnitude. This combination of high refractive index and extreme field enhancement enables exceptionally strong light-matter interactions. We demonstrate this by designing a compact high-index metamaterial device supporting an anapole, which further enhances the nanogap field. By placing
quantum emitters with terahertz transitions inside the plasmonic gaps, we show a stimulated-emission response enhanced by at least three orders of magnitude, highlighting applications in non-linear optics, frequency up-conversion and vibrational strong coupling.
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All authors Zoltan Sztranyovszky, Nicolas Spiesshofer, Caleb Todd, Rakesh Arul, Yeeun Roh, Rohit Chikkaraddy, Jeremy J. Baumberg, Angela Demetriadou
Fluid Dynamics · Cross-listed from cond-mat.soft
Pore-shape and its spatial organization control intrinsic permeability of porous media
Wenqiao Jiao, Isaac Pincus, Chiara Recalcati et al. (+2 more)
The structure of a porous material, and in particular its spatial variability, is known to control the intrinsic permeability of the system. We investigate how dead-end pores influence the intrinsic permeability of a porous medium beyond their contribution to total pore volume. Dead-end pores are ubiquitous in porous media, yet they are often treated as hydraulically inactive regions whose influence is assumed to be negligible or absorbed into effective-porosity descriptions. We perform pore-scale flow simulations across different dead-end pore structures, including heterogeneous arrangements, controlled granular assemblies, and a minimal single-channel model to study their impact on the system macroscopic permeability. This strategy allows us to isolate the effects of dead-end pore density, depth, and orientation while preserving the transmitting network. We find that dead-end pores can influence intrinsic permeability: increasing the density of dead-end pores along percolating flow paths enhances permeability, whereas pore depth and junction orientation have negligible effects. The observed permeability enhancement originates from localized hydrodynamic interactions at junctions between transmitting and dead-end pores. Based on these results, we propose an effective formulation that relates the density and spatial organization of dead-end pores relative to the transmitting network to macroscopic permeability. Our findings show that dead-end pore architecture provides an additional
geometric control on intrinsic permeability beyond porosity and pore-size statistics.
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All authors Wenqiao Jiao, Isaac Pincus, Chiara Recalcati, Alberto Guadagnini, Pietro de Anna
Atmospheric and Oceanic Physics
AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning
Ewan Pinnington, Peter Lean, Mihai Alexe et al. (+8 more)
We introduce the Artificial Intelligence
Forecasting System for Direct Observation Prediction (AIFS-DOP). AIFS-DOP is trained on a 40-year harmonized
dataset of gridded observations, without using numerical weather prediction (NWP) reanalysis or model data. The resulting model is competitive with ECMWF's Integrated
Forecasting System (IFS) when scored on a one year period of forecasts across 2021/2022. This progress on Direct Observation Prediction represents the first time that a data-driven model, trained solely on observations, is competitive with the IFS at medium ranges for several key upper-air and surface headline scores, when verified against observation data.
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All authors Ewan Pinnington, Peter Lean, Mihai Alexe, Eulalie Boucher, Simon Lang, Patrick Laloyaux, Gert Mertes, Tomas Kral, Patricia de Rosnay, Matthew Chantry, Anthony McNally
Biological Physics · Cross-listed from cond-mat.soft
Chiral Packings in Cylinders are Ultrasensitive to Confinement Deformation
Xuebin Wang, Jiahao Guo and Yao Li
Sphere packings in circular cylinders have attracted substantial research interest, among which the discovery of
chiral helical structures is the most iconic. However, recent experimental results on zebrafish do not match the known packing structures in circular cylinders. To account for the inherent imperfections of biological tubes, we take
elliptic cylinders as the canonical deformation of circular cylinders and investigate the densest packings of hard spheres in them using simulation, theory, and experiments. Starting from the
chiral structures in circular cylinders, we demonstrate that even a weak cross-sectional deformation can trigger entirely new phases, including ones that either eliminate global chirality or significantly complicate the
chiral structures. This reveals the significant effect of cylindrical anisotropy. The new helical phases under anisotropic confinement remain
chiral and develop hierarchical periodic structures, which are difficult to obtain by simulations but are predicted by our newly developed theory for helical phases in
elliptic cylinders. The theory also predicts double oscillated-chain phases without chirality, which perfectly match the simulations. Our work offers fresh insights into understanding packings in anisotropic cylinders, which will help researchers to design new materials and to understand many living systems.
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Fluid Dynamics · Cross-listed from cond-mat.soft
Enucleated incompressible red blood cells in shear flow: theoretical analysis of shape instabilities
Avraham Moriel, Howard A. Stone and Simon Mendez
Red blood
cells (RBCs) are essential for oxygen transport, and their remarkable ability to undergo significant deformations during flow is a crucial feature for their physiological function. At intermediate shear rates typical of the microcirculation, RBCs can adopt complex, multi-lobed shapes, signifying a dynamic instability. Here we adopt a perturbative theoretical framework of a quasi-spherical RBC under external shear flow to study such shape instabilities. To better capture RBC maturation and enucleation, we first extend the framework to explicitly account for different excess areas between the stress-free and current membrane shapes. We revisit the reduced equations of motion obtained for an ellipsoidally-shaped RBC, and demonstrate the effect of different excess areas and initial orientation on the dynamical trajectories. Then, we introduce additional spatial modes and show that an emerging instability critically depends on the RBC's shear and bending
moduli, the internal to external viscosity ratio, and the excess area, mainly through the RBC's membrane tension. We also study the instability-induced saturation of the membrane tension, and the resulting excess area redistribution at long times. The theoretical framework and the emerging picture of the different instabilities provide insights into the emergence of stomatocyte and trilobe shapes exhibited by RBCs under external flow.
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Biological Physics
External Entropy Production and Human Evolution toward Multi-body Life
Yasuji Sawada and Kenji Toma
Ancient human beings started "external
entropy production" in a late stage of
evolution, in addition to the internal
entropy production by which energy was dissipated within the body of life, as previously described consistently with the birth of life by maximum
entropy production principle. In this paper, the mechanism for development of external
entropy production, which is strongly related with use of tools and controlling fire, is theoretically investigated. Archaeological data show that the
brain size of ancient human beings started rapid increase around 2.5 million years ago when the usage of tools and control of fire started. It may be natural to assume that the rapid growth of
brain size is related to the growth of awareness which helped cooperation with the other human beings for control of fire. Coupled equations for the growth rate of
brain including awareness and for growth rate of size of the interacting human beings are analyzed. The external
entropy production per one human being which is directly related to the group size of cooperating human beings is estimated to increase as about 20 million years in the beginning from the critical time. This
evolution created coexistence of internal
entropy production of traditional multi-cellular life and new external
entropy production of multi-body life. A psychological problem due to the coexistence of two kinds of
entropy production mechanism in human being and concept of technologies based on the present thermodynamic
evolution theory are discussed. It is suggested that the evolutionary understanding of the origin of global warming based on the external
entropy production may be important to create an useful countermeasure.
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Computational Physics · Cross-listed from math.NA
A performance portable fast Ewald summation for Stokes flow
Gabriel Kosmacher, Ziyu Du, Joar Bagge et al. (+1 more)
We present GPU
algorithms for Ewald summation methods for accelerating N-body Stokes flow problems in periodic domains. Like most N-body codes, Ewald sums use a near-field/far-field decomposition. The near field involves particle-to-particle (P2P) interactions. The far field primarily involves particle-to-grid (P2G) and grid-to-particle (G2P) interactions, as well as Fast
Fourier Transforms. For each interaction, we investigate several algorithmic variants. Our implementation uses PyKokkos, a Python interface for the Kokkos C++ parallel programming framework, which supports portability to AMD/NVIDIA GPU and ARM/x86 CPU architectures. Double and single-precision numerical results, alongside analytical performance models, confirm the efficiency of our
algorithms on AMD and NVIDIA GPU and on ARM and AMD CPU architectures. The P2P interaction achieves around 73% compute efficiency on NVIDIA H200, 84% on NVIDIA A100, 60% on AMD MI300, 52% on Grace CPU, and 68% on AMD Epyc CPU. A straightforward implementation of the P2G kernel can become a computational bottleneck. We introduce a novel P2G
algorithm that achieves up to 16$\times$ speedup compared to a baseline GPU implementation. The overall Ewald sum code processes approximately 8 million particles per second on a H200 GPU, and about a half-million particles per second on a Grace CPU, for nine digits of accuracy. We also perform a multi-GPU weak scaling test on up to 256 million particles (64 GPUs) that shows bounded communication cost for all stages except the all-to-all particle sorting, which can be reduced to neighbor communication in the relevant time-stepping regime.
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All authors Gabriel Kosmacher, Ziyu Du, Joar Bagge, George Biros
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
A Hybrid LSTM–Vision Transformer Architecture for Predicting HRRR Forecast Errors
David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia et al. (+2 more)
Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena. Previous work demonstrated that Long Short-Term
Memory (LSTM) networks can successfully predict forecast errors in the High-Resolution Rapid Refresh (HRRR) model using mesonet observations, but we believe performance degradation is linked to periods of complex vertical atmospheric
evolution. To address this limitation, we develop a hybrid LSTM-
Vision Transformer (LSTM-ViT) framework that combines temporal sequence learning from surface observations with atmospheric profiles from the New York State Mesonet profiler network. The LSTM-ViT framework is trained to predict HRRR hourly precipitation, 10 m wind speed, and 2 m temperature forecast errors at individual mesonet stations. Across all three predictors, incorporation of profiler-derived atmospheric structure improves forecast error prediction skill relative to the baseline LSTM architecture, with the largest gains occurring at shorter forecast lead times and during periods of enhanced PBL activity. Improvements are particularly pronounced for precipitation forecast error, where the LSTM-ViT framework achieves approximately a twofold increase in predictive skill relative to the baseline LSTM while better capturing convectively driven error
evolution and reducing degradation associated with PBL processes. These results demonstrate that combining temporal sequence learning with vertically informed
attention mechanisms provides a physically meaningful pathway for improving forecast error prediction in operational NWP systems. Our research offers forecasters enhanced guidance regarding model bias and forecast confidence.
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All authors David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia, Nick P. Bassill, Chris D. Thorncroft
Fluid Dynamics
Global branches of Stokes waves of variable period on stratified fluids
Vladimir Kozlov
We consider stratified steady water waves in a two dimensional channel. Our subject is branches of Stokes waves, bifurcating from laminar flows. We assume that the mass flux and the Bernoulli constant are fixed and consider the period of the wave as a parameter, which can change its value along the branch. A new class of density and Bernoulli functions is presented, for which laminar flows generate global bifurcation branches. The laminar flows are not necessary unidirectional and we show that the bifurcation branch can bifurcate from the laminar flow with arbitrary large period.
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Fluid Dynamics
Intermittency in Shell Models of Turbulent Cascades: from Single-Branch to Multi-Branch
Flavio Tuteri, Sergio Chibbaro and Alexandros Alexakis
Intermittency is one of the central features of turbulent transfer: the multi-scale energy cascade is mediated by rare and intense fluctuations. We investigate this phenomenon in a multi-branch shell model, which combines quasi-local triadic nonlinear interactions with a branching structure that mimics the growth of degrees of freedom toward small scales. Comparison with the standard Sabra model shows that branching enhances intermittency, as measured by anomalous scaling exponents of energy-flux structure functions. We further use multiplier statistics and large deviation estimates to characterize the multiplicative nature of the cascade. Our results suggest that reduced descriptions of turbulent intermittency should retain both nonlinear dynamics and geometrical organization. Implications on Navier-Stokes
turbulence are discussed.
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Medical Physics
Quantitative Multi-Modal Optical Coherence Photoacoustic Elastography
Ekaterina Sherina, Lisa Krainz, Wolfgang Drexler et al. (+1 more)
We present a novel multi-modal optical coherence photoacoustic elastography framework, which combines two imaging modalities, optical coherence
tomography and photoacoustic
tomography, to enable complementary absorption-scattering measurements for the extraction of quantitative tissue features via quasi-static elastography. For this, we develop a sophisticated hybrid inversion
algorithm for merging the complementary information layers contained in both optical coherence and photoacoustic-based elastography measurements, and perform systematic evaluations to assess the impact of hybrid elastography data on strain and stiffness reconstructions. An extension to a photoacoustic
tomography and optical coherence
tomography imaging system allows precise elastographic experiments. Studies on a silicone elastomer phantom demonstrate that the combined optical coherence - photoacoustic approach outperforms single-modality optical coherence elastography and photoacoustic elastography, yielding higher strain signal-to-noise ratios and improved stiffness estimates. These results establish the advantage of multi-modal complementary imaging and data merging for accurate, high-resolution elastographic strain and stiffness mapping in both scattering and absorbing materials.
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All authors Ekaterina Sherina, Lisa Krainz, Wolfgang Drexler, Otmar Scherzer
Plasma Physics · Cross-listed from astro-ph.SR
The Quiet-Sun DEM Under Kappa: Diagnostic Degeneracy and the Failure of the Conductive Closure
Victor Edmonds
For a
plasma whose electrons carry a $\kappa \approx 2.5$ suprathermal tail, the Spitzer-Harm conductive closure does not exist: the conductive flux is the tail-carried third velocity moment, and the local conductivity integral diverges across the entire $\kappa \in [2,3]$ range – the finite value the closed-form $\kappa$-conductivity returns at $\kappa = 2.5$ is an analytic continuation of a divergent integral, not a physical conductivity. Edmonds (2026a) places the quiet
solar corona (QS) in this regime. Taking that as premise, two failures follow for any
plasma in the class: the standard EUV-DEM diagnostic cannot resolve such a
plasma, and the conductive term of the standard QS energy budget has no valid form. The diagnostic failure is shown end-to-end. A single-T $\kappa = 2.5$ probe, a multi-T $\kappa = 2.5$ source, and a multi-T Maxwellian source, all run through the regularized DEM inversion of Hannah & Kontar (2012), recover $\log T$ widths inside the FWHM distribution the same pipeline returns from 80 real quiet-Sun AIA patches; the pipeline cannot distinguish them. Two structural features also emerge: a Fe XI charge-state crossover and an EUV continuum reversal. The ionization-gated diagnostic structurally returns the tail-weighted effective temperature $T_{\mathrm{eff}}$, while Spitzer-Harm takes the bulk-core $T_{\mathrm{core}} = (\kappa - 3/2)/\kappa \cdot T_{\mathrm{eff}}$ as input. The mismatch invites a temperature substitution yielding a budget reduction – mechanically correct and physically empty, because the coefficient it corrects has no convergent form: it is the
Fourier-law closure itself that fails, not its temperature input. Two QS pillars for impulsive heating – DEM-width multi-thermality and the conductive-budget gap – lose their structural assumptions, and the budget question shifts to non-local kinetic transport outside any fluid closure.
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Optics
Thermodynamic-Kinetic Decoupling Enables Stable Excitonic Emission in Defect-Tolerant Cu-Based Quantum Dots
Haoran Chen, Zhipeng Xu, Chunjian Li et al. (+7 more)
Colloidal
quantum dots that simultaneously offer room-temperature single-
photon purity and high photoluminescence
quantum yield are sought for
quantum optics, but remain elusive in environmentally benign materials. We introduce a thermodynamic-kinetic decoupling strategy that transforms defect-tolerant CuInS2
quantum dots into bright, narrowband, and photostable single-
photon emitters. Zn2+ alloying strains the
lattice, thermodynamically suppressing native copper vacancies and narrowing the emission from a broad defect band of approximately 300 meV to an excitonic line of approximately 120 meV. Ga3+ incorporation then kinetically pins the cation sublattice against Cu+ migration, preventing defect regeneration during ZnS shell growth. The resulting Cd-free core/shell dots achieve near-unity
quantum yield of approximately 98% while retaining narrow excitonic emission. Critically, room-temperature single-dot spectroscopy reveals homogeneous linewidths as low as approximately 58 meV, strongly suppressed blinking, and high-purity single-
photon emission with g2(0) = 0.06. This stabilized excitonic emission directly reduces reabsorption losses in luminescent
solar concentrators, yielding an external optical efficiency of 12.68%. Our work establishes a generalizable framework to unlock intrinsic excitonic photophysics in ion-mobile, defect-prone
semiconductors, opening a viable path toward high-performance heavy-metal-free emitters for
quantum light sources.
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All authors Haoran Chen, Zhipeng Xu, Chunjian Li, Lei Hou, Dechao Yu, Xiaobin Xie, Yue Liu, Bohua Dong, Lixin Cao, Chenghui Xia
Fluid Dynamics
APU-Accelerated Large Eddy Simulation with the Discontinuous Galerkin Solver GALÆXI
Spencer Starr, Anna Schwarz, Justin Du Plessis et al. (+5 more)
The exascale computing era, driven by heterogeneous GPU architectures, requires a fundamental redesign of traditional CFD solvers to fully leverage those heterogeneous systems. The discontinuous Galerkin spectral element method (DGSEM) provides an ideal foundation for this transition due to its high-order accuracy and local computational stencil. This work presents recent advances in the development and application of the architecture-agnostic DGSEM framework GALÆXI by linking hardware optimization, software implementation, and physical validation. The performance of GALÆXI on the AMD MI300A Accelerated Processing Units (APUs) featured on the Hunter supercomputer is analyzed. Specifically, evaluations of the strong and weak scaling performance and the impact of the compute partitioning modes available on the AMD MI300As are performed. Second, the strategy used to integrate the
algorithms necessary for wall-modeled large eddy simulations into the GPU-accelerated framework is outlined. Validation of those
algorithms is presented in the form of a plane turbulent channel testcase. Finally, the solver is applied to a demanding flow problem in the form of a wall-resolved large eddy simulation of a transonic compressor cascade. The results from this investigation demonstrate the capabilities of GALÆXI to accurately capture complex shock-wave/turbulent boundary-layer interactions.
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All authors Spencer Starr, Anna Schwarz, Justin Du Plessis, Andreas Wanninger, Johanna Hintz, Rohan Kaushik, Patrick Kopper, Andrea Beck
Atmospheric and Oceanic Physics
Persistence without Multifractality in Tropical Atlantic SST Indices: Scaling-Range Artefacts and a Reappraisal of the ENSO Association
Sebastián Jaroszewicz, Nahuel Mendez, Maria P. Beccar-Varela et al. (+1 more)
We investigate the scaling properties of three weekly tropical Atlantic sea-surface-temperature indices (SAT, TSA and the interhemispheric gradient TASI) over 1981–2025 using multifractal detrended fluctuation analysis and its sign-preserving cross-correlation extension. Exponents are estimated over $s\in[18,100]$ weeks, bounded by $s_{\min}\gtrsim6(m+1)$ for second-order detrending and a crossover near 100 weeks. On these scales all three indices are strongly persistent and locally non-stationary ($h(2)=1.32$–$1.35$). Comparison with shuffled and phase-randomised surrogates shows that none of the indices are multifractal within record resolution. SAT ($\Delta h=0.077$) and TASI ($\Delta h=0.054$) are fully consistent with both null models. Although TSA yields a larger width ($\Delta h=0.217$), its increasing $h(q)$ implies the unphysical condition $f(\alpha)>1$, indicating a crossover rather than a multiplicative cascade. At the available record length ($N\approx2300$), apparent multifractal widths are not identifiable because confidence intervals overlap and rankings remain sensitive to analysis choices. The increments are only mildly leptokurtic, excluding heavy tails as the source of the apparent widths. Pairwise couplings reveal strong positive cross-correlations between the regional indices and anticorrelation of the gradient with both, all described by a single cross-scaling exponent, consistent with monofractal behaviour. A lagged analysis further shows that the previously reported TASI–ENSO association disappears after surrogate testing accounting for multiple lags. These results substantially revise downward previous estimates of multifractal strength in tropical Atlantic SST and show that reported widths should not be interpreted without verifying both the sign of $h'(q)$ and the stability of the scaling range.
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All authors Sebastián Jaroszewicz, Nahuel Mendez, Maria P. Beccar-Varela, Maria Cristina Mariani
Optics · Cross-listed from cond-mat.stat-mech
Thermodynamics of photonic nonlinear Aharonov-Bohm cages
Stefano Iubini and Carlo Danieli
We investigate
equilibrium and non-
equilibrium thermodynamics of one-dimensional photonic diamond
lattices with Kerr nonlinearity. The
equilibrium phase diagram is obtained as a function of the synthetic
magnetic flux acting on each plaquette. In the linear regime, the
magnetic flux can induce Aharonov-Bohm caging, flattening all Bloch bands and suppressing particle and energy currents. In this caging regime, non-vanishing currents are enabled by nonlinearity. By imposing stationary temperature- and chemical potential- imbalances at the system boundaries, we show that at weak nonlinearity fine tuning the flux at the Aharonov-Bohm caging transforms the system from a conductor to an
insulator. For intermediate nonlinear strength, the system remains conducting for all
magnetic fluxes; however, the caging condition significantly enhances the Seebeck coefficient and thermoelectric figure of merit, improving the thermoelectric features of the system. Our results give evidence of a novel route towards optimization of coupled transport devices, based on the control of linear versus nonlinear conduction channels via a synthetic
magnetic flux.
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