Computational Physics · Cross-listed from cs.LG
Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions
Faranak Hatami and Mousa Moradi
Background: Graph neural networks improve computational prediction of polypharmacy side effects, but standard binary cross-
entropy training allocates equal capacity to well-classified and difficult examples, potentially missing clinically significant interactions. We evaluated whether an asymmetric focal objective could improve multi-relational drug-drug interaction (DDI) prediction by emphasizing difficult positive interactions. Methods: ClinicalFocal loss was integrated into a relation-aware graph convolutional network using molecular fingerprints, physicochemical descriptors, and learned
embeddings. The model was evaluated on TWOSIDES using five-fold cross-validation with identical experimental conditions (architecture, features, data partitions, hyperparameters, and random seeds) for ClinicalFocal loss and binary cross-
entropy baseline. Results: ClinicalFocal loss increased accuracy from 0.699 to 0.892 (+19.3 percentage points) and F1 score from 0.700 to 0.894 (+19.4 percentage points). AUROC increased from 0.766 to 0.914, and AUCPR increased from 0.714 to 0.860. The false-negative rate decreased from 29.8% to 9.1%, while specificity increased from 69.6% to 87.5%. Overall classification error decreased from 30.1% to 10.8%, corresponding to a 64.1% relative reduction. Improvements were consistent across all five folds. Conclusions: Asymmetric focal optimization improved classification and ranking performance while achieving 90.9% recall for observed interaction triples, without modifying the underlying architecture. Loss-function design is a direct, tunable lever for improving graph-based DDI prediction.
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Biological Physics
Directional bias of a single polarized cell under confinement
Andreas Buttenschön and Calina Copos
Chiral patterns have been observed in various processes from swirling bacterial colonies to tissue morphogenesis and cytoskeletal organization, yet the physical mechanisms underlying
chiral cell motion remain poorly understood. Motivated by experiments demonstrating directional bias in the circular motion of confined
cells, we use the tools of dynamical systems analysis with computer simulations to identify minimal intrinsic and extrinsic mechanisms capable of generating persistent biased migration. The dynamical systems framework reveals a common organizing principle: directional bias emerges through changes in the stability and/or basins of attraction of the clockwise and counter-clockwise motility states. We find four distinct routes to such bias. First, intrinsic torque in a polarized cytoskeleton can be spatially integrated to produce biased circular motion. Second, anisotropic
cell-substrate friction can generate directional preference when reduced friction along the polarity axis is coupled to a directional offset. Third, a
chiral wall-
alignment response can also produce a persistent directional preference. Finally, substrate patterns that break mirror
symmetry, such as dextral or sinistral ridges and troughs, can likewise bias rotational direction. Together, these mechanisms yield distinct, testable predictions and suggest a unifying lens for experimental interrogation of cellular chirality and the design of synthetic systems with programmable
chiral motion.
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Biological Physics
DNA handles bias force-dependent looping times
Wout Laeremans, Jef Hooyberghs and Wouter G. Ellenbroek
DNA loop formation is central to gene regulation, and its kinetics depend sensitively on mechanical tension. In both single-molecule experiments and biological settings, this tension is typically transmitted through flanking DNA segments rather than acting directly at the looping sites. Using molecular dynamics simulations of the wormlike chain model, we show that such flanking segments substantially enhance the force dependence of looping, with the effect saturating once their length reaches approximately one DNA persistence length and disappearing when the connection is made flexible. We develop an analytic theory in which the handles extend the effective looping coordinate. This extension is set by two quantities: an
equilibrium property of the construct that can be measured without force, and the force-dependent length over which a handle remembers the orientation of its binding site. The resulting theory quantitatively reproduces published optical-tweezer measurements, without adjustable parameters. Furthermore, we demonstrate that the longstanding discrepancy between theory and experiment does not originate from neglected DNA-specific elastic effects, but is explained by the indirect transmission of force through the handles, establishing it as the dominant missing ingredient in the theory of DNA looping under force.
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Fluid Dynamics
Experimental evidence of Kelvin wave turbulence along a vortex core
Jason Barckicke, Christophe Gissinger and Eric Falcon
Wave
turbulence is a regime of interacting nonlinear waves occurring in most physical systems. Kelvin waves are helical distortions that propagate along
vortex filaments and are believed to play a central role in
quantum turbulence up to atmospheric
vortices. Yet, Kelvin wave
turbulence has remained inaccessible to direct experimental observation. Here, we report the first direct experimental observation of Kelvin-wave
turbulence along a single
vortex filament in a classical fluid under controlled conditions. Using high-resolution spatiotemporal measurements, we resolve Kelvin-wave dynamics over a broad range of scales and obtain wave-amplitude spectra consistent with the predicted weak-
turbulence cascade. We identify six-wave resonant interactions as the mechanism driving this energy transfer, providing direct experimental support for a long-standing prediction of weak-
turbulence theory. These results establish an experimental platform for investigating energy transport along
vortex filaments, with broader implications for both classical and
quantum turbulent systems.
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Optics · Cross-listed from quant-ph
Spin Textures and Eigenstate Evolution of Isospectrally Patterned Lattices
Peter Schmelcher
Isospectrally patterned
lattices exhibit a composite band structure with a tunable ratio of localized versus delocalized eigenstates that is controlled by the underlying phase gradient. We show that the
lattice Hamiltonian can be interpreted as that of a single
spin exposed to a rotating
magnetic field which is allowed to hop with a
spin-flip across the
lattice. In the low- and high-energy part of the band the localized states show an envelope of oscillatory character separated by quasi-nodes.
Spin peaks occur at the locations of these quasi-nodes and provide a unique
spin texture to the eigenstates which becomes increasingly complex with increasing degree of excitation. The crossover from localization to delocalization and vice versa leaves its fingerprints in the
Fourier spectrum of the eigenstates: the original bimodal frequency distribution widens with increasing degree of excitation, moves across the spectral window and finally culminates in an extremely narrow frequency peak. In the course of this
evolution the
spin texture undergoes a rearrangement transition involving different characteristic (ir)regular patterns which we quantify by considering the total variation of the local
spin fluctuations. Our results demonstrate the variety of the spectral properties of isospectrally patterned
lattices which holds great prospect in particular when considering higher
lattice or
cell dimensions.
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Computational Physics
Collaborate to decorrelate in path space: Hamiltonian replica exchange transition interface sampling (HRETIS)
Sina Safaei, Parham Rezaee and An Ghysels
We present
Hamiltonian Replica Exchange Transition Interface Sampling (HRETIS), a path sampling framework designed to efficiently sample rare events in systems with complex potential energy landscapes. HRETIS introduces a helper potential within a
Hamiltonian replica exchange scheme, which enhances exploration of path space when the underlying potential is not well suited for conventional path sampling approaches. This is particularly advantageous for systems exhibiting multiple pathways separated by orthogonal barriers such as in drug (un)binding, where standard
algorithms often show slow convergence since they become trapped within specific pathways. By exchanging
Hamiltonians between the path ensembles, HRETIS overcomes these limitations and increases the decorrelation between subsequent paths in the Monte Carlo chain. We demonstrate that HRETIS provides robust and accurate kinetics in several systems, including coarse-grained simulations of amino acid permeation through a dipalmitoylphosphatidylcholine (DPPC) membrane. Moreover, HRETIS is found to improve sampling efficiency and convergence, illustrating its potential as a powerful tool for rare event sampling in complex molecular systems.
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Optics
Chip-scale nanostructured chaotic billiards for broadband speckle spectrometry
Matthew R. Wilson, Benoit Guilhabert, Jack A. Smith et al. (+2 more)
Computational on-chip spectrometers are emerging as a powerful platform for portable spectral analysis, combining photonic integration with advanced signal processing to enable a wide range of in-situ sensing applications. We propose a broadband reconstructive spectrometer based on wave chaos in a stadium microresonator with a nanostructured scattering layer for full-area speckle readout. Wavelength dependent interference within the chaotic microresonator encodes the spectral information into a spatial intensity pattern that can be computationally inverted to reconstruct the input spectra. The optimal fabrication parameters of the SU-8
polymer nanostructured layer yield a surface roughness of 176nm and a root mean square thickness of ~2um. We experimentally validate our spectrometer at visible and infrared wavelengths, with resolutions of 43pm at 630nm and 8.2pm at 1550nm. The spectral reconstruction is demonstrated for single and multiple narrowline sources as well as for a broadband (~1nm) pulsed
laser source. The broad experimental validation and compact footprint (0.05mm2) establishes our chaotic microresonator-based speckle spectrometer as a robust and versatile platform for high-resolution, on-chip spectral sensing.
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All authors Matthew R. Wilson, Benoit Guilhabert, Jack A. Smith, Michael J. Strain, Xavier Porte
Fluid Dynamics
A fully one-sided diffuse-interface immersed boundary method for wall-modeled large-eddy simulation
Qian Mao, Yoshiharu Tamaki, Song Zhao et al. (+3 more)
Diffuse-interface immersed boundary methods (DIBMs) provide a simple and robust approach for simulating flows involving complex geometries. However, their inherent
diffusion effect can contaminate the near-wall flow field and significantly degrade wall-shear-stress prediction in wall-modeled large-eddy simulation (WMLES). To address this limitation, we develop a WMLES approach based on a fully one-sided diffuse-interface immersed boundary method (FODIBM). By performing interpolation and spreading exclusively inside the immersed body, the proposed method removes the cross-boundary
diffusion effect that adversely affects wall modeling in conventional DIBMs. A wall-shear-stress enforcement strategy is developed by coupling the wall-parallel immersed-boundary forcing with the wall shear stress predicted by an explicit wall model. In addition, a tau-model based on the modeled turbulent shear-stress tensor is introduced to preserve the total shear-stress balance below the reference height. The method is first validated in high-Reynolds-number turbulent channel flows, showing good agreement with DNS data for the mean velocity, Reynolds shear stress, and skin-friction coefficient. Sensitivity studies with respect to grid resolution, reference height, wall inclination angle, and Reynolds number demonstrate the robustness of the method. Compared with the conventional DIBM, the proposed method substantially improves the overall prediction accuracy, particularly at low reference heights. The approach is further assessed for turbulent flow over a NACA23012 airfoil, where the predicted pressure distribution and lift coefficient agree well with experimental data.
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All authors Qian Mao, Yoshiharu Tamaki, Song Zhao, Jérôme Jacob, Pierre Boivin, Julien Favier
Fluid Dynamics
An improved fully one-sided diffuse-interface immersed boundary method with target-value reconstruction for compressible flows
Qian Mao, Song Zhao, Pierre Boivin et al. (+1 more)
Although one-sided spreading has been shown to improve the near-wall accuracy of diffuse-interface immersed boundary methods (DIBMs), the effect of its asymmetric kernel support on the effective boundary location remains insufficiently understood. In this work, a detailed analysis of the one-sided spreading operator reveals an inward displacement of the effective boundary relative to the
geometric boundary. To compensate for this displacement, a target-value reconstruction strategy is developed to ensure consistency between the values imposed at the effective boundary and the prescribed conditions at the
geometric boundary. The strategy is incorporated into the fully one-sided diffuse-interface immersed boundary method (FODIBM) and applies to both Dirichlet and Neumann boundary conditions. Although confined to the target-value evaluation step, the modification substantially improves boundary-condition enforcement with negligible additional computational cost. Coupled with a hybrid
lattice Boltzmann solver, the improved method consistently reduces L_2 and L_{\infty} error norms across different grid resolutions while retaining approximately second-order grid convergence. The no-slip and isothermal boundary-condition errors are reduced by 77% and 85%, respectively. Simulations involving various two- and three-dimensional geometries further show improved predictions relative to both the conventional DIBM and the original FODIBM. The results agree well with body-fitted reference solutions and experimental data, demonstrating accurate and computationally efficient simulations of compressible flows around complex geometries.
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All authors Qian Mao, Song Zhao, Pierre Boivin, Julien Favier
Physics and Society
Hyperlocal monitoring of urban activity reveals responses to heat exposure
Dominique Geissler, Felix Creutzig, Ramit Debnath et al. (+1 more)
Rising temperatures create new challenges for local heat adaptation. Yet, it remains unclear how urban activity changes during hot periods and which urban environments people concentrate in as temperatures rise. Here, we perform a hyperlocal spatiotemporal analysis of urban activity across 10 German cities over a two-month period in 2024 with different levels of heat exposure. To monitor urban activity, we use fine-grained telecommunication data to map locations of people with high spatio-temporal resolution (i.e., hourly at 100m x 155m grid
cells), yielding more than 100 million data points. We then link activity counts with hourly weather records and point-of-interest data. We find that sustained periods of hot weather, defined as at least three consecutive days with daily maximum temperatures $\geq 25${\deg}C, are characterized by below-expected city-wide presence, with activity counts that are 1.5 percentage points below regular urban activity. During hot periods, urban activity concentrates more strongly around leisure- and culture-oriented amenities (e.g., cafés or swimming pools), with an increase of up to around 10 percentage points relative to cooler days, while public-service environments (e.g., educational and health facilities) show weaker or negative shifts. Our study provides policy-makers with fine-grained monitoring of which urban areas attract citizens during heat exposure, which can enable evidence-based, spatially-targeted urban heat adaptation plans.
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All authors Dominique Geissler, Felix Creutzig, Ramit Debnath, Stefan Feuerriegel
Optics
Coexistence and manipulation of multiple singularities in a reconfigurable non-Hermitian metasurface
Xintong Shi, Yanjie Wu, Rui Zhou et al. (+6 more)
Non-Hermitian frameworks extend conventional Hermitian physics, offering a powerful paradigm for describing open systems. Central to this field are various singularities within the complex parameter space, such as exceptional points (EPs) and scattering zeros, which dictate exotic physical behaviors. As research shifts from isolated singularities toward multi-singularity interactions, conventional planar metasurfaces remain constrained by limited tuning dimensions. Here, we propose a mirror-coupled design that maps a metasurface into a quasi-high-dimensional parameter space. By employing a metallic plane to generate image resonators, this scheme multiplies the system degrees of freedom without increasing the number of physical resonators. Its implementation on a reconfigurable platform integrated with PIN diodes yields the coexistence and manipulation of an EP and multiple reflection zeros. Through simulations and microwave experiments, we characterize the dynamic
evolution of these singularities and exploit their synergistic effects for two distinct applications. First, for tunable absorption, multiple reflection zeros are spectrally coordinated to achieve a near-perfect absorption band exceeding $99.9\%$ across the X-band, thereby dynamically suppressing target scattering. Second, for enhanced sensing, a reflection zero couples with the EP to form a hybrid singularity. This hybrid state inherits the power-law sensitivity of the EP while substantially boosting robustness against fluctuations, resolving the conventional trade-off between sensitivity and stability and simplifying detection to direct peak tracking rather than complex multimode
eigenvalue fitting. Our work provides a general methodology to circumvent parameter competition among non-Hermitian singularities, opening new avenues for multifunctional metadevices across the electromagnetic spectrum.
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All authors Xintong Shi, Yanjie Wu, Rui Zhou, Zuxing Lu, Tengyu Li, Tingting Liu, Hai Lin, Qiegen Liu, Shuyuan Xiao
Physics and Society
Allocation Condensation in Redistributive Networks
T. Ashwin Bhattathiripad and Vipin P. Veetil
We study a fixed network on which new mass is allocated in proportion to a power of each node's stock and then redistributed. Transport retains a fraction of both old and new mass at each node and divides the rest according to fixed background shares. We show that a small amount of retention can sustain concentration on networks where complete redistribution would leave every node's share of new mass vanishing as the network grows. A node receiving most new mass may retain little relative to the network total, yet much more than redistribution alone would supply. For a power above one, the allocation rule amplifies this relative stock advantage at the next step. We identify retention rates tending to zero with network size that allow widely spread and concentrated stationary allocations to coexist on the same network. One of them stays close to the allocation under complete redistribution. Each node can also dominate a separate concentrated allocation, receiving a share of new mass tending to one. The stocks supporting these allocations attract nearby trajectories, although every stationary stock approaches the same background, with even its largest share tending to zero. These allocations continue to coexist under specified changes in transport that preserve the background. Redistribution can therefore spread the stock widely without spreading new allocations in the same way.
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Physics and Society · Cross-listed from cs.MA
A Large Language Model-Driven Agent-Based Modeling Framework with Multi-Round Communication for Simulating Vaccine Opinion Dynamics
Bo Zhang and Na Jiang
Recently, Large
Language Models (LLMs) have been utilized in various applications of computational social science and provide the possibility to integrate such models into
agent-based modeling to explore the cognitive processes. However, how specific cognitive modules drive individual decisions and macro-level opinion dynamics remains unclear. Therefore, this study introduces a framework that integrates an LLM (Qwen3-8B) into
agent-based modeling to investigate this problem, using vaccination opinion dynamics as a case study. We utilize this framework to simulate opinion dynamics among
agents with heterogeneous profiles and social networks, evaluating scenarios by enabling different cognitive modules: a
memory module and a
prompt diversity module. The simulation results reveal that different cognitive modules have opposite impacts on our emergent opinion. Furthermore, the framework reproduces the non-linear behavior patterns of social influence observed in existing research, demonstrating our framework's validity and potential to reach the level 3 validation of
agent-based models.
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Biological Physics
Stiffness by design in biological fibers: the influence of microstructure and temperature
Eli Yovel and Noy Cohen
Biological fibers exhibit exceptional mechanical properties such as high stiffness, toughness, and elasticity. The stiffness of bio-fibers is governed by a hierarchical microstructure that is highly sensitive to the extraction method, post-processing, and environmental factors such as temperature. Commonly, the microstructure features an amorphous
matrix comprising polypeptide chains that interact through weak intermolecular bonds and interconnect via crystalline domains. In this work, we develop a microscopically motivated energy-based model that sheds light on the underlying mechanisms governing the stiffness of bio-fibers. The initial deformation is driven by (1) the entropic extension of polypeptide chains, (2) the elastic stretching and rotation of rigid crystalline domains, and (3) the distortion of weak intermolecular interactions, leading to the relative sliding of polypeptide chains. The model captures the influence of key physical microstructural quantities such as chain
alignment, chain stretch, intermolecular bond strength, and crystallite size on the overall stiffness. The merit of the model is demonstrated through a comparison to spider silk and cocoon silk fibers. We also employ the model to show that the reeling speed during the extraction of spider silk fibers governs the microstructure and, therefore, leads to different stiffnesses. We follow with a parametric analysis that sheds light on how different microstructural quantities affect the stiffness. Lastly, the framework is extended to account for thermally-induced microstructural changes and the model predictions are compared to experimental data on the stiffness of cocoon silk fibers as a function of temperature. The findings from this work delineate the role of microstructure on the overall stiffness and offer a pathway for the efficient design of tunable and optimized biomimetic fibers for target applications.
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Instrumentation and Detectors
Ultra-trace analysis of 40K in organic liquid scintillators
A. Barresi, D. Chiesa, D. Merli et al. (+64 more)
Rare-event searches require exceptionally low background levels, motivating the development of increasingly sophisticated screening methods to push sensitivity limits.
Liquid scintillators are particularly attractive detector media due to their intrinsic radiopurity and the ability to scale to large target masses. In this work, we present a screening strategy capable of measuring ultra-trace concentrations of $^{40}\text{K}$ with sensitivities below $10^{-15}$g/g. The method combines
neutron activation analysis with a dedicated radiochemical treatment, followed by low-background HPGe gamma spectroscopy. Using this approach, we achieved a minimum detectable concentration of $2.9\cdot10^{-16}$g/g for $^{40}\text{K}$, placing this technique among the most sensitive currently available.
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All authors A. Barresi, D. Chiesa, D. Merli, M. Nastasi, S. Nisi, E. Previtali, M. Sisti, M. Borghesi, A. Cammi, C. Coletta, G. Ferrante, L. Loi, G. Andronico, V. Antonelli, D. Basilico, M. Beretta, A. Bergnoli, A. Brigatti, R. Brugnera, R. Bruno, A. Budano, B. Caccianiga, A. Caslini, V. Cerrone, R. Caruso, C. Clementi, L.V. D'Auria, S. Dusini, A. Fabbri, G. Felici, A. Garfagnini, M.G. Giammarchi, N. Giudice, A. Gavrikov, M. Grassi, N. Guardone, F. Houria, A. Islam, C. Landini, L. Lastrucci, I. Lippi, P. Lombardi, F. Mantovani, S.M. Mari, A. Martini, L. Miramonti, M. Montuschi, D. Orestano, F. Ortica, A. Paoloni, L. Pelicci, E. Percalli, F. Petrucci, G. Ranucci, A.C. Re, B. Ricci, A. Romani, A. Serafini, C. Sirignano, L. Stanco, E. Stanescu Farilla, V. Strati, M.D.C Torri, C. Tuvé, C. Venettacci, G. Verde, L. Votano
Plasma Physics
Observation of Self-Similarity in the Magnetic Fields Generated by the Ablative Nonlinear Rayleigh-Taylor Instability
L. Gao, P. M. Nilson, I. V. Igumenschev et al. (+10 more)
Magnetic fields generated by the nonlinear Rayleigh-Taylor growth of
laser-seeded three-dimensional broadband perturbations were measured in
laser-accelerated planar targets using ultrafast
proton radiography. The experimental data show self-similar behavior in the growing cellular
magnetic field structures. These observations are consistent with a bubble competition and merger model that predicts the time
evolution of the number and size of the bubbles, linking the cellular
magnetic field structures with the Rayleigh-Taylor bubble and spike growth.
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All authors L. Gao, P. M. Nilson, I. V. Igumenschev, G. Fiksel, R. Yan, J. R. Davies, D. Martinez, V. Smalyuk, M. G. Haines, E. G. Blackman, D. H. Froula, R. Betti, D. D. Meyerhofer
Optics
Transmissibility, boundary-guided waves, and representative unit cell choice in finite-sized metamaterials
Plastiras Demetriou and Gianluca Rizzi
The implications of selecting different unit
cells are often overlooked in both direct studies of microstructured materials and their homogenized equivalents. Investigating the effects of unit
cell selection is crucial not only for understanding boundary phenomena but also for identifying which finite-sized metamaterial performs best for a given purpose (e.g., achieving zero or minimal transmissibility in the band-gap range). This study examines how the choice of a representative unit
cell in periodic metamaterials influences boundary effects and, consequently, the transmissibility of finite-size samples, while providing a strategy based on eigenfrequency calculations that enables a priori optimization.
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Optics
Time domain Stokes mechanism of pair correlated k gap solitons in nonlinear photonic time crystal slabs
Jiaxiang Sun, Guowei Chen, Liang Zhang et al. (+1 more)
Pair generation in time-varying media is commonly attributed to time reflection at temporal boundaries or to amplification inside momentum k gaps. Here we show that these two processes are connected by the time domain Stokes phenomenon. A finite duration photonic time
crystal (PTC) slab provides the necessary Stokes connection between the incident
vacuum mode, transient k gap amplification, and time boundary scattering. With Kerr nonlinearity, the otherwise unbounded amplification is arrested, spawning Kerr stabilized k gap
solitons. When these
solitons cross the exit boundary of the time slab, Stokes induced mode conversion produces a secondary pair generation process, yielding four spatially separated and entangled pulse branches. Detection of a backward propagating light pulse therefore heralds its forward propagating partner. We further propose combined Hanbury Brown Twiss and Hong Ou Mandel measurements to test their nonclassical correlations. These results reveal a link between asymptotic Stokes physics and
quantum temporal scattering in PTCs, and suggest a route toward ultrafast heralded
quantum light sources.
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All authors Jiaxiang Sun, Guowei Chen, Liang Zhang, Yiming Pan
Fluid Dynamics · Cross-listed from quant-ph
Quantum simulation of real-world nonlinear dynamics via Koopman method
Baoyang Zhang, Dong An, Zhaoyuan Meng et al. (+4 more)
Nonlinear dynamics is ubiquitous in nature, ranging from chemical pattern formation to ocean circulation, yet its simulation on
quantum computers is fundamentally limited by the
unitary nature of
quantum evolution. We propose the
quantum Koopman method, a data-driven framework that embeds nonlinear dynamics into a learned linear representation and implements the resulting
evolution using shallow
quantum circuits. This method learns Koopman observables from trajectory data, projects the lifted dynamics onto a finite-dimensional subspace, and decomposes the corresponding non-
unitary propagator into parallel spectral channels. We utilize the Koopman method on a superconducting processor to simulate three distinct nonlinear systems, comprising reaction-
diffusion dynamics, fluid motion on a sphere, and satellite-derived observations of Gulf Stream currents, employing up to 32 parallel
circuits of 10
qubits. These
quantum simulations capture the dominant multiscale patterns and statistical signatures of the underlying dynamics, and reveal a transition from performance limited by hardware noise in weakly nonlinear systems to performance limited by finite-dimensional Koopman representations as nonlinear scale interactions increase. This transition identifies a practical boundary for
quantum-amenable nonlinear dynamics, establishing a hardware-validated route for simulating moderately nonlinear dynamics on near-term
quantum hardware.
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All authors Baoyang Zhang, Dong An, Zhaoyuan Meng, Yefei Yu, Xiaoxiao Xiao, Zhen Lu, Yue Yang
Fluid Dynamics · Cross-listed from cond-mat.stat-mech
Taming nonlinear energy diffusion: The case of time-crystal energy condensates
P.I. Hurtado and G. Cortés-Guillén
We study a bulk-driven nonlinear variant of the Kipnis-Marchioro-Presutti model of
stochastic energy
diffusion in which local collisions are biased to induce a net energy flow, resembling the effect of an external field. Starting from the microscopic master equation, we derive the hydrodynamic description of the driven system via a local
equilibrium approximation, obtaining explicit expressions for the energy current and the associated diffusivity and mobility transport coefficients, which are nonlinear functions of the local energy density. We test our findings in kinetic Monte Carlo simulations of the model and, as a proof of concept, we demonstrate the versatility of this driving mechanism to control nonlinear energy transport by inducing time-crystalline phases. In particular, we show that appropriately designed packing fields induce the spontaneous formation of traveling energy condensates, exhibiting robust long-range temporal order reminiscent of continuous time
crystals. Our results provide a simple yet powerful framework to study bulk-driven nonlinear energy
diffusion in
stochastic many-body systems, offering a bridge between microscopic dynamics, macroscopic transport, and controlled spatiotemporal order.
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Optics
Pulley coupler engineering for frequency comb generation based on the supermodal approach
Lise Morice, Baptiste Routier, Quentin Wilmart et al. (+3 more)
Light coupling into integrated microresonators is typically achieved using straight bus couplers that are optimized to achieve critical coupling at the pump wavelength but lack spectral engineering capabilities. Pulley couplers offer additional degrees of freedom and opportunities, but optimizing their spectral response requires intensive and time-consuming numerical simulations. To address this challenge, we developed a semi-analytical approach to quickly and efficiently calculate the coupling quality factor of pulley couplers. This enables the design of either broadband or wavelength-selective pulley couplers, depending on the target application. We illustrate the dispersion engineering capabilities offered by this new approach in the context of Kerr microcombs and experimentally validate it on the SiN photonic platform. We show that it is possible to achieve a non-dispersive broadband coupler, enabling the generation of frequency combs with similar spectral characteristics (span and intensity) irrespectively of the chosen pump wavelength.
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All authors Lise Morice, Baptiste Routier, Quentin Wilmart, Christian Grillet, Christelle Monat, Yoan Léger
Physics and Society · Cross-listed from cond-mat.stat-mech
Thermodynamic description of worldwide distribution of energy and carbon emission
Klaus M. Frahm and Dima L. Shepelyansky
Based on public data, we analyze the distributions of energy and carbon emission over world countries on a scale of the last 40-50 years using their presentation via Lorenz and Pareto curves. These curves in rescaled format remain remarkably stable on this time period being characterized by high values of the Gini coefficient indicating a strong inequality of energy distribution. To explain these distributions, we introduce the ENergy Thermalization Hypothesis (ENTH) according to which these distributions result from the Rayleigh-Jeans (RJ) thermalization and condensation of
agents representing different countries. We show that this hypothesis provides an excellent description of Lorenz and Pareto curves obtained from data on the above time period. It also gives natural grounds for inequality relating it to the RJ condensation at low energy states. We additionally trace parallels with the wealth inequality in the world.
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Atomic Physics
Collision and static widths of self-broadened line shapes in optically saturated high-density atomic vapor
Vladimir Sautenkov, Sergey Saakyan, Andrei Bobrov et al. (+2 more)
We study the frequency derivatives of selective reflection from high-density rubidium vapor using a hole-burning technique. Saturation dips are observed inside the self-broadened line shapes. The line self-broadening is a combination of static width and collision width. By analyzing saturation dips, we can separate power broadening and collision width. Our experimental results support the theory of inhomogeneous dipole-dipole induced broadening of transitions in a dense atomic gas, published by J. A. Leegwater and S. Mukamel [Phys. Rev. A 49 (1994) 146].
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All authors Vladimir Sautenkov, Sergey Saakyan, Andrei Bobrov, Eugenia Vilshanskaya, Boris B. Zelener
Fluid Dynamics
Three-dimensional global stability analysis of turbulent screeching jets
Alessandro Franchini, Nicolas Alferez and Jean-Christophe Robinet
A three dimensional global stability analysis is performed to investigate the problem of screeching jets under turbulent conditions. The study employs an Unsteady Reynolds-Averaged Navier-Stokes (URANS) framework, in which the compressible flow equations are discretised using the high-fidelity solver dNami, the linearised discrete system is obtained through the automatic differentiation tool Tapenade, and the global stability problem is solved in a time-stepping framework. The fixed-point solutions of the URANS equations are first validated against experimental and numerical data, then a three dimensional global stability analysis is performed around fixed points solutions at different levels of under-expanded regimes. The extracted modes are spatially analysed and examined in terms of acustic radiation and validated against experimental data. Comparison with experimental POD data shows that the linear modes reproduce the main wavenumber content and spatial organisation of the screech resonance loop, even at high levels of under-expansion. The staging behaviour is also recovered from the interaction between the Kelvin–Helmholtz wave and the dominant wavenumbers of the shock-
cell structure. Finally, a Helmholtz decomposition is applied to the velocity perturbation in order to separate the vortical and irrotational parts of the modes. An energy budget of the wave components is then used to quantify the repartition of the relative feedback-loop energy perturbation. Notably, the Mach-number effects on energy partition vary depending on the type of staged mode. This insight could prove valuable for interpreting receptivity mechanisms at nozzle lips and shocks in future research.
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Atmospheric and Oceanic Physics
Super-Resolution of Radar/Raingauge-Analyzed Precipitation Using Gaussian Process Regression with a Steering Kernel
Shoichi Akami, Tsuyoshi T. Sekiyama and Mizuo Kajino
Super-resolution (SR), a technique for estimating a high-resolution (HR) image from a low-resolution image, has been used in meteorology for downscaling and resolution enhancement of observations. SR Gaussian process regression with a steering kernel (SRGP-SK) generates more accurate HR images than the original SR Gaussian process regression (SRGP), but it has not yet been applied to meteorological data. This study applied SRGP-SK to radar/rain gauge-analyzed precipitation for convective and stratiform cases and evaluated the results using the structural similarity index (SSIM) and radially averaged power spectral density (PSD). SRGP-SK achieved SSIM values comparable to those of bicubic interpolation and higher than those of SRGP while reconstructing finer precipitation structures; it recovered variations down to a wavelength of 6 km, compared with 8 km for bicubic interpolation. This difference may correspond to an approximately threefold increase in the number of convective
cells. Among the kernel functions compared, the Matern 5/2 kernel yielded the highest
geometric mean PSD ratio. Further investigation of this point could identify the statistical scaling characteristics of the precipitation field. This study evaluates only two precipitation cases; examining more cases is necessary before SRGP-SK can be applied more broadly.
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Applied Physics · Cross-listed from cs.RO
Programmable Synchronization Graphs for Adaptive and Fault-Tolerant Modular Miniature Robots
Okan Kulekcioglu, Arqam Bin Ahmad, Ines Garcia et al. (+3 more)
Modular miniature
robots could provide scalable function in constrained environments, but coordinating many imperfect modules remains difficult when computation, communication and reliability are limited. A central robotics challenge is to coordinate many actuator-sensor modules without assigning a privileged leader, prescribing a fixed gait template, or relying on dense communication. Here we introduce a programmable synchronization-graph framework for modular miniature
robots in which each actuator-sensor pair is represented as a network node and locomotor coordination is encoded through graph coupling. Fixed intra-subgraph links synchronize heterogeneous actuator groups, whereas a small number of signed inter-subgraph links program phase relationships between groups. In physical
robot collectives with up to nine modules, graph coupling drives the emergence of synchronization, signed links tune the phase difference from in-phase to out-of-phase motion, and floor experiments produce gallop-like and trot-like contact patterns in a five-module
robot assembly. Replacing dense all-to-all coupling with sparse d-regular
topologies preserves synchronization while reducing the coupling burden. The same graph representation also captures fault tolerance: increasing graph degree increases the number of module deactivations tolerated before desynchronization. Finally, an upper-confidence-bound edge-selection
algorithm learns inter-subgraph links that drive the system toward target phase states. In a separate deactivation
benchmark, the graph-based controller avoids the leader-specific failure mode observed in centralized leader-follower control and reduces worst-case phase error by about threefold. These results establish programmable network
topology as a compact control layer for gait phase programming, online adaptation and robustness to unit loss in modular miniature
robots.
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All authors Okan Kulekcioglu, Arqam Bin Ahmad, Ines Garcia, Filipe Serra Alves, Onur Ozcan, M. Selim Hanay
Fluid Dynamics · Cross-listed from cs.LG
BubbleSH: A Dataset of Rising Bubbles with Deformable Interfaces
Rachna Ramesh, Kiet Bennema ten Brinke, Douwe Orij et al. (+2 more)
Bubbly flows exhibit complex multiscale dynamics, with deformable bubbles interacting through the surrounding
liquid and giving rise to strongly coupled kinematic and morphological behavior. We present BubbleSH, a bubbly flows
dataset consisting of transient, three-dimensional bubble-swarm dynamics obtained from high-fidelity direct numerical simulations of bubbles rising in a periodic domain. The
dataset provides time-resolved bubble trajectories, velocities, and shape
evolution, with bubble morphology compactly represented using spherical harmonics. Designed to be lightweight yet physically expressive, the
dataset enables data-driven modeling of bubbly flow simulators where shape deformation and bubble-bubble interactions play a central role. We characterize the
dataset with bubble kinematics, morphology, and interaction patterns, and introduce evaluation metrics for both trajectory and shape prediction. The sensitivity of bubble-swarm dynamics to local perturbations makes BubbleSH particularly well suited to generative models that learn distributions over possible future trajectories. We evaluate a permutationally and translationally equivariant probabilistic emulator on BubbleSH given the proposed metrics. Therefore, we establish a compact, high-fidelity
dataset and a
benchmark for developing and evaluating data-driven models of deformable, chaotic multiphase systems.
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All authors Rachna Ramesh, Kiet Bennema ten Brinke, Douwe Orij, Ivo Roghair, Vlado Menkovski
Optics
Beyond white- and black-box modeling tools in optical communications and optical computing: physics-informed data-driven modeling
Isidora Teofilovic, Sergio Hernandez Fernandez, Metodi P. Yankov et al. (+3 more)
Efficient optimization and control of photonic computing and communication systems increasingly rely on accurate surrogate models/digital twins. While data-driven models may achieve faster inference than traditional physics-based methods, they typically suffer from poor training data efficiency and limited generalizability. To address this trade-off, physics-informed data-driven modeling has emerged as a powerful hybrid paradigm. This paper presents a comparative analysis of these three modeling paradigms across three test use cases: optical amplifiers, directly modulated
lasers, and interferometer meshes. By evaluating model complexity, data efficiency, generalizability, and modularity, this work provides a detailed analysis of the respective trade-offs and highlights the advantages of combining physical insight with data-driven learning.
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All authors Isidora Teofilovic, Sergio Hernandez Fernandez, Metodi P. Yankov, Christophe Peucheret, Darko Zibar, Francesco Da Ros
Physics and Society · Cross-listed from cs.CY
Billions of Sketches Reveal Hidden Cultural Variation in Human Concepts
Arianna Pera, Mauro Martino, Nima Dehmamy et al. (+3 more)
Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across
languages and cultures. However, words are effective as communication devices because they compress rich experiential variation into shared conventions, potentially obscuring hidden individual and cultural differences in how concepts are mentally represented. Here, we analyse 2.6 billion human-made sketches of common concepts from 236 countries and territories to examine conceptual structure through people's visual imagination. Consistent with recent work on image-based cognition, we find that single concepts unfold into multiple distinct visual exemplars, revealing latent information about similarities and differences in conceptual structure across cultures. This variation is strongest for concepts involving haptic interaction, suggesting that visual imagery reflects variation in
embodied experience as much as conventional definitions. Comparing
embedding models of sketches with word
embedding models across
languages, we find that their geometries diverge, with visual representations preserving rich semantic and cultural structure that
language models compress. Cross-cultural similarities derived from sketches align 32% more closely with established cultural distances than do text-based measures. Together, these results suggest that patterns of human conceptual universality may depend critically on the modality through which concepts are measured, with large-scale sketching providing a direct, high-resolution probe of conceptual diversity across
embodied and cultural dimensions of thought.
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All authors Arianna Pera, Mauro Martino, Nima Dehmamy, Douglas Guilbeault, Luca Maria Aiello, Andrea Baronchelli
Fluid Dynamics
Skin friction prediction for attached flows based on two-dimensional inviscid solutions
Mingkun Xia, Shule Zhao and Weiwei Zhang
Boundary layer theory and its analytical methods for skin friction coefficients provide an important basis for aerodynamic analysis. However, classical analytical formulas are mostly limited to flat-plate flows. High-fidelity numerical simulations are not only computationally expensive but also yield predictions that are highly sensitive to physical models, numerical schemes, and grid resolution. To overcome these limitations, symbolic AI opens a new pathway to discover novel laws of complex physical systems from data. Using limited data from surface solutions of the Euler equations and the skin friction coefficient from viscous flows over airfoils, we employ symbolic regression to progressively discover a generalizable, interpretable analytical formula chain for fast skin friction prediction in subsonic and supersonic attached flows. From the perspective of physical mechanisms, the discovered analytical expression chain reveals scaling laws for skin friction at different Mach numbers: the basic form captures the logarithmic decay of skin friction along the streamwise direction in the turbulent boundary layer; the inclusion of a pressure coefficient correction term quantifies the effect of surface pressure variation; and the Mach number correction term evolves with flow regimes, transitioning from the compressibility correction term in subsonic regimes to the thermodynamic effects term in supersonic and hypersonic regimes. This knowledge chain exhibits a unified structure across different Mach numbers, and omitting the correction terms under certain conditions recovers classical theoretical forms, further demonstrating its physical consistency. Validation against typical geometries shows that this analytical formula chain achieves a low average integrated skin friction drag prediction error, with good generalization capability across different freestream conditions and
geometric shapes.
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