Fluid Dynamics
Performance of an open-source image-based history matching framework for CO$_2$ storage
David Landa-Marbán, Tor Harald Sandve, Jakub Wiktor Both et al. (+2 more)
We present a history matching (HM) workflow applied to the International FluidFlower
benchmark study
dataset, which features high-resolution images of CO$_2$ storage in a meter-scale, geologically complex reservoir. The
dataset provides dense spatial and temporal observations of fluid displacement, offering a rare opportunity to validate and enhance HM techniques for geological carbon storage (GCS). The combination of detailed experimental data and direct visual observation of flow behavior at this scale is novel and valuable. This study explores the potential and limitations of using experimental data to calibrate standard models for GCS simulation. By leveraging high-resolution images and resulting interpretations of fluid phase distributions, we adjust uncertain parameters and reduce the mismatch between simulation results and observed data. Simulations are performed using the open-source OPM Flow simulator, while the open-source Everest decision-making tool is employed to conduct the HM. After the HM process, the final simulation results show good agreement with the experimental CO$_2$ storage data. This suggests that the system can be effectively described using standard flow equations, conventional saturation functions, and typical PVT properties for CO$_2$-brine mixtures. Our results demonstrate that the Wasserstein distance is a particularly effective metric for matching multi-phase, multi-component flow data. The entire workflow is implemented in a Python package named pofff (Python OPM Flow FluidFlower), which organizes all functionality through a single input file. This design ensures reproducibility and facilitates future extensions of the study.
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All authors David Landa-Marbán, Tor Harald Sandve, Jakub Wiktor Both, Jan Martin Nordbotten, Sarah Eileen Gasda
Applied Physics · Cross-listed from cond-mat.supr-con
Low-temperature electron dephasing rates indicate magnetic disorder in superconducting TiN films
A. I. Lomakin, E. M. Baeva, N. A. Titova et al. (+8 more)
We investigate electron transport and phase-breaking processes in thin titanium nitride (TiN) films of epitaxial quality. Previous studies show that a minute surface
magnetic disorder significantly reduces the critical temperature ($T_\mathrm{c}$) and broadens the superconducting transition as the film thickness and device size decrease. We measure electron dephasing rates via magnetoresistance from $T_\mathrm{c}$ to $\sim 4T_\mathrm{c}$ in various-thickness TiN films. Electron dephasing occurs on the picosecond timescale and is nearly independent of temperature, differing from the expected inelastic scattering due to the electron-
phonon and electron-electron interactions near $T_\mathrm{c}$, which occur over a nanosecond timescale. We propose
spin-flip scattering as a possible additional phase-breaking mechanism. The significant increase in the dephasing rate for the thinnest film indicates that
magnetic disorder resides near the surface of naturally oxidized films. Our research suggests that
magnetic disorder may be a significant contributor to RF dissipation in superconducting devices based on TiN.
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All authors A. I. Lomakin, E. M. Baeva, N. A. Titova, A. V. Semenov, A. V. Lubenchenko, M. A. Kirsanova, S. A. Evlashin, S. Saha, S. Bogdanov, A. I. Kolbatova, G. N. Goltsman
Optics · Cross-listed from quant-ph
Phase Transitions and Virtual Exceptional Points in Quantum Emitters Coupled to Dissipative Baths
Stefano Longhi
Controlling atom-
photon interactions in engineered environments is central to
quantum optics and emerging
quantum technologies. Non-Hermitian (NH) photonic baths, where dissipation fundamentally reshapes spectral and dynamical properties, provide versatile platforms for such control. Here we investigate the relaxation dynamics of a single two-level
quantum emitter coupled to the edge of a semi-infinite dissipative bosonic
lattice with uniform loss. Despite the simplicity of this bath, we uncover rich dynamical phase transitions, i.e. qualitative changes in spontaneous emission decay as system parameters are varied. In particular, we establish the existence of an optimal dissipative environment for accelerated spontaneous emission. The phase transitions are traced to spectral restructuring of the resolvent, in some cases governed by the coalescence of resonance states on the second Riemann sheet. We identify these coalescences as virtual exceptional points (EPs) of resonance origin, providing a conceptual bridge with EP physics while highlighting distinctive features of infinite-dimensional NH systems. More broadly, our results illustrate how the specific nature of dissipation – whether uniform losses, staggered losses, or dephasing – can profoundly impact emitter relaxation, pointing to dissipation engineering as a versatile tool for
quantum technologies.
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Optics
Super-robust telecommunications enabled by topological half-supermodes
Rui Zhou, Xintong Shi, Hai Lin et al. (+6 more)
Topological photonics offers transformative potential for robust integrated waveguide devices due to their backscattering-immune properties. However, their integration faces two fundamental challenges: mode
symmetry mismatch with conventional waveguides and prohibitive dimensions. We successfully overcome these two critical challenges by introducing a novel valley-ridge gap waveguide based on topological half-supermode engineering. By strategically hybridizing ridge waveguide modes and valley kink states, we create an exotic odd-symmetric supermode enabling robust propagation and ultra-compact operation. The further implementation of a perfect electric conductor boundary halves lateral dimensions while eliminating radiation loss. Crucially, our proposed valley-ridge interface achieves direct transverse electric mode matching with standard waveguides without transition structures, enabling seamless integration. Experimental results demonstrate reflection losses lower than -15 dB in realistic telecommunication scenarios with super-robust signal propagation through sharp bends. This work innovatively conceptualizes topological half-supermodes and pioneers their practical applications for integrated waveguide devices, establishing a completely new waveguide class that uniquely combines robust backscattering immunity with deep subwavelength compactness.
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All authors Rui Zhou, Xintong Shi, Hai Lin, Yan Ren, Hang Liu, Zihao Yu, Jing Jin, Zhihao Lan, Menglin L. N. Chen
Biological Physics
Dynamic principles of concentration buffering through liquid-liquid phase separation
Logan de Monchaux-Irons, T-Y Dora Tang, Christoph A. Weber et al. (+1 more)
Living systems must maintain robust biochemical function despite fluctuations that span a wide range of timescales. Biomolecular condensates formed by
liquid-
liquid phase separation (LLPS) have been shown to buffer concentration fluctuations, but the principles governing their dynamic regulation remain unclear. We address this by probing the response of LLPS to oscillatory perturbations that mimic fluctuations across different timescales, establishing the first systematic frequency-domain analysis of concentration buffering by condensates. We find that condensates act as frequency-selective filters: the perturbed dilute phase behaves as a high-pass filter, while the dense phase attenuates both low- and high-frequency perturbations. We establish quantitative links between LLPS parameters including interaction strength, droplet size, and molecular diffusivity, and the timescale range over which condensates effectively buffer concentration fluctuations. These findings establish the fundamental dynamical limits of concentration buffering by LLPS, with implications for how
cells may use LLPS to adapt to fluctuating environments and for the design of synthetic condensates with programmable control properties.
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All authors Logan de Monchaux-Irons, T-Y Dora Tang, Christoph A. Weber, Thomas C.T. Michaels
Fluid Dynamics
Are heuristic switches necessary to control dissipation in modern smoothed particle hydrodynamics ?
Domingo García-Senz and Rubén M. Cabezón
Artificial viscosity is commonly employed in smoothed particle hydrodynamics (SPH) to model dissipation in hydrodynamic simulations. However, its practical implementation today relies, in many cases, on complex numerical switches to restrict its application to regions where dissipation is physically warranted, such as shocks. These switches, while essential, are imperfect and can introduce additional numerical noise. In this work we develop and validate a more efficient shock capture scheme for SPH that does not rely on artificial viscosity switches. Recent studies have proposed that subtracting the linear component of the velocity field can suppress spurious dissipation in shear-dominated regions. Building on this idea, we implement a velocity-reconstruction technique that removes the bulk linear motion from the local velocity field and uses the Balsara correction to modulate the dissipation. The presented methodology yields a balanced dissipation scheme that performs well across a range of regimes, including subsonic instabilities, shear flows, and strong shocks. We demonstrate that this approach yields improved accuracy and lower spurious dissipation, compared to the reference viscosity switch used in this work.
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Optics · Cross-listed from quant-ph
Feasibility of entanglement-based QKD protocols with SPDC and QD sources
Mariia Gumberidze and Vladyslav C. Usenko
We theoretically analyze the feasibility of
entanglement-based
quantum key distribution (QKD) protocols considering widely used spontaneous parametric down-conversion (SPDC) and novel
quantum dot (QD) sources. We account for multiphoton emission in SPDC sources and fine-structure splitting (FSS) in QD. In addition, we incorporate imperfect detection, including dark counts and limited efficiency. For SPDC sources, we confirm that the presence of
vacuum and multiphoton pairs renders them unsuitable for secure device-independent (DI) QKD implementations under standard detection strategies. Conversely, in the case of QD sources, accounting for the effects of FSS, results in reduced performance of protocols. Our findings are crucial for the practical implementation of
entanglement-based QKD protocols using realistic sources and detectors.
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Optics
Soliton interaction and bound state formation in coupled Kerr resonators
Daria A. Dolinina, Dmitry V. Turaev and Andrei G. Vladimirov
Soliton dynamics in coupled Kerr microcavities is an important aspect of frequency comb technologies, with applications in optical communication and precision metrology. We investigate a minimal system consisting of two nearly identical coupled Kerr microresonators, each operating in the
soliton regime and driven by a separate coherent beam, and analyze the mechanisms that govern their
soliton interactions. In the weak-coupling regime, the system supports multiple
soliton clusters characterized by distinct
soliton separations and stability. Numerical simulations indicate that asymmetric perturbations can alter
soliton separations or destroy these states, while the imposed pump phase difference plays a key role in cluster selection. Together, these findings highlight previously unexplored regimes of dissipative
soliton organization and suggest new strategies for controlling
soliton ensembles in integrated photonic platforms.
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Physics and Society · Cross-listed from cond-mat.stat-mech
Asymptotic versus mesoscopic spectral dimensions in networks and inhomogeneous structures
Lorenzo Grimaldi, Pablo Villegas, Alessandro Vezzani et al. (+3 more)
"Every object that biology studies is a system of systems." (François Jacob, 1974). Most networks feature intricate architectures originating from tinkering, a repetitive use of existing components where structures are not invented but reshaped. Linking the properties of primitive components to the emergent behavior of composite networks remains a key open challenge. Here, we show that modular compositions generically decouple the spectral and Fiedler dimensions, so that networks with identical thermodynamic exponents can exhibit parametrically different equilibration dynamics.
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All authors Lorenzo Grimaldi, Pablo Villegas, Alessandro Vezzani, Raffaella Burioni, Davide Cassi, Andrea Gabrielli
Instrumentation and Detectors
Basic considerations in the design of an electrostatic electron monochromator
M.J. Adriaans, J.P. Hoogenboom and A. Mohammadi-Gheidari
Monochromators are an essential component in electron microscopy and spectroscopy for enhancing the spatial and energy resolution. However, its adoption in scanning electron microscopes remains limited because of its high cost and operational complexity. Through a thin-deflector analysis of an electrostatic homogeneous-field deflector, the extreme sensitivity of current monochromators to power supply drift and mechanical imperfections is demonstrated. These stringent
alignment requirements for achieving optimal energy resolution often necessitate the use of additional correcting elements, adding to both cost and complexity. We demonstrate that the fringe-field deflector is instead less sensitive to these issues. Hence, a cost effective and simple monochromator design approach based on pure fringe fields is proposed. This monochromator does not need extra correcting elements and its optimal energy resolution is achieved by including momentary deceleration lenses surrounding the main deflector. This fully electrostatic design could be realized using MEMS technology, offering a simpler and more accessible approach for filtering beam energies.
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Biological Physics · Cross-listed from q-bio.PE
Fitness inference tested by in silico population genetics
Hong-Li Zeng, Yu-Han Huang, John Barton et al. (+1 more)
We consider populations evolving according to natural selection, mutation, and recombination, and assume that the genomes of all or a representative selection of individuals are known. We pose the problem if it is possible to infer fitness parameters and genotype fitness order from such data. We tested this hypothesis in simulated populations. We delineate parameter ranges where this is possible and other ranges where it is not.Our work provides a framework for determining when fitness inference is feasible from population-wide, whole-genome, time-stratified data and highlights settings where it is not. We give a brief survey of biological model organisms and human pathogens that fit into this framework.
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All authors Hong-Li Zeng, Yu-Han Huang, John Barton, Erik Aurell
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Hurdle-RMIL: Addressing Zero Inflation and Long-Tailed Imbalance in Infrared Rainfall Retrieval
Fangjian Zhang, Xiaoyong Zhuge, Wenlan Wang et al. (+4 more)
Imbalanced labels can cause frequent samples to dominate AI-based quantitative remote sensing, degrading rare-event
retrieval. In rain-rate
retrieval based on satellite infrared brightness temperatures, this imbalance leads to systematic underestimation of rare high-intensity rainfall. In this study, Hurdle-
Retrieval Model Imbalanced Learning (RMIL) is proposed. Following a divide-and-conquer strategy, Hurdle-RMIL separates zero inflation from the long-tailed distribution of positive rain. A hurdle model handles zero inflation, whereas RMIL exploits invariance under fixed observation conditions of the rainfall-to-satellite forward process to derive a Bayes-based transformation linking conditional distributions under naturally long-tailed and hypothetical balanced rainfall. This transformation enables the balanced-distribution model to be learned from natural samples without constructing a balanced
dataset. Comparisons with conventional learning, classification-regression modeling, cost-sensitive learning, and generative learning using test data from multiple regions in China show that Hurdle-RMIL mitigates systematic underestimation and improves detection of rare high-intensity and extreme rainfall without markedly degrading lower-threshold accuracy. At 0.1-10 mm per hour, its root mean square error remains close to those of the best baselines, and it yields the highest equitable threat score (ETS) at most evaluated thresholds, with its advantage becoming more pronounced at high thresholds. At 30 mm per hour, its ETS is 0.051 versus 0.015 for the best baseline, and its mean error is -25.41 mm per hour versus -28.98 mm per hour. Case studies further show improved representations of rainfall intensity and spatial extent, demonstrating that Hurdle-RMIL effectively addresses rainfall-distribution imbalance and improves the
retrieval of rare high-intensity rainfall.
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All authors Fangjian Zhang, Xiaoyong Zhuge, Wenlan Wang, Haixia Xiao, Yuying Zhu, Siyang Cheng, Ali Mamtimin
Chemical Physics
An interpretable molecular descriptor for machine learning predictions in atmospheric science
Linus Lind, Hilda Sandström and Patrick Rinke
The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are particularly affected because they often rely on dictionary-based descriptors tied to specific molecular substructures, which currently fail to capture the full range of organic atmospheric compounds, including large, highly oxidized molecules common in the atmosphere. We introduce ATMOMACCS, an interpretable descriptor combining the 166 binary keys of the MACCS fingerprint with motifs inspired by the SIMPOL method for estimating saturation vapor pressures. We show that ATMOMACCS based models improve predictions of saturation vapor pressures (7-8 % error reduction),
equilibrium partition coefficients (5 % and 9 % error reduction), glass transition temperatures (22 % error reduction), and enthalpy of vaporization (61 % error reduction) on four
datasets with atmospheric compounds. Feature analysis shows that saturation vapor pressure and partition coefficients are governed by carbon number and oxygen-related features, whereas other phase-transition properties (e.g., enthalpy of vaporization, glass transition temperature) depend on carbon-hydrogen bond types and the presence of heteroatoms other than oxygen. This highlights the generalizability of ATMOMACCS across different
datasets and properties as an interpretable molecular descriptor.
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Optics
Suspension-Free Integrated Cavity Brillouin Optomechanics on a Chip
Yuan-Hao Yang, Jia-Qi Wang, Zheng-Xu Zhu et al. (+6 more)
Cavity optomechanical systems enable coherent
photon-
phonon interactions essential for
quantum technologies, yet high-performance devices have been limited to suspended structures. Here, we overcome this limitation by demonstrating cavity Brillouin optomechanics in a suspension-free racetrack microring resonator on a lithium-niobate-on-sapphire chip, a platform that merits high stability and scalability. We demonstrate coherent coupling between telecom-band optical modes and a 9.6-GHz
phonon mode, achieving a maximum cooperativity of $0.41$ and a
phonon quality-factor-frequency product of $10^{13}\,\mathrm{Hz}$. The momentum-matching condition inherent to traveling-wave Brillouin interactions establishes a one-to-one mapping between optical wavelength and
phonon frequency, enabling multi-channel parallel operations across nearly $300\,\mathrm{MHz}$ in
phonon frequency and $40\,\mathrm{nm}$ in optical wavelength. Our suspension-free architecture provides a coherent
photon-
phonon interface compatible with wafer-scale integration, opening pathways toward hybrid
quantum circuits that unite photonic, phononic, and superconducting components on a single chip.
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All authors Yuan-Hao Yang, Jia-Qi Wang, Zheng-Xu Zhu, Xin-Biao Xu, Ming Li, Juanjuan Lu, Guang-Can Guo, Luyan Sun, Chang-Ling Zou
Computational Physics · Cross-listed from hep-ph
Symbolic Regression and Differentiable Fits in Beyond the Standard Model Physics
Shehu AbdusSalam, Steven Abel, Deaglan Bartlett et al. (+1 more)
We demonstrate the efficacy of symbolic regression (SR) to probe models of particle physics Beyond the Standard Model (BSM), by considering the so-called Constrained Minimal Supersymmetric Standard Model (CMSSM). Like many incarnations of BSM physics this model has a number (four) of arbitrary parameters, which determine the experimental signals, and cosmological observables such as the dark matter relic density. We show that analysis of the phenomenology can be greatly accelerated by using symbolic expressions derived for the observables in terms of the input parameters. Here we focus on the
Higgs mass, the cold dark matter relic density, and the contribution to the anomalous
magnetic moment of the
muon. We find that SR can produce remarkably accurate expressions. Using them we make global fits to derive the posterior probability densities of the CMSSM input parameters which are in good agreement with those performed using conventional methods. Moreover, we demonstrate a major advantage of SR which is the ability to make fits using differentiable methods rather than sampling methods. We also compare the method with neural network (NN) regression. SR produces more globally robust results, while NNs require data that is focussed on the promising regions in order to be equally performant.
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All authors Shehu AbdusSalam, Steven Abel, Deaglan Bartlett, Miguel Crispim Romão
Fluid Dynamics
The impact of pressure oscillations on bubble rising in shear-thinning fluids
Mario Riccio and Marco De Corato
We study the rising dynamics of a bubble driven into periodic volumetric oscillations by an external pressure driving within a highly viscous shear-thinning fluid. We perform axisymmetric direct numerical simulations employing the Carreau-Yasuda model to describe the rheological behavior of the fluid and the finite element method to discretize the equations. We carry out a parametric study of the bubble rising dynamics, changing the amplitude and the frequency of the external pressure driving, and the bubble radius. Due to the external pressure oscillations, the bubble undergoes volume changes that strain the
liquid at much larger rates than those due to natural rising, causing the surrounding fluid viscosity to thin. The numerical results show that the rising dynamics become highly nonlinear and unsteady due to the interplay of the shear-thinning rheology and the external driving. As a result, the period-averaged rising velocity of the bubble can increase by orders of magnitude compared to its natural rising velocity. These nonlinear effects become progressively more important as the amplitude and the frequency of the pressure driving or the bubble radius are increased. Qualitatively, the simulation model agrees with previous experimental findings in terms of average rising velocity. However, the experiments exhibit terminal velocities that are smaller than those predicted numerically, along with differences in bubble shape during the ascent. These discrepancies may be attributed to modeling the fluid rheology as a generalized Newtonian fluid rather than as a viscoelastic one.
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Optics
Active control of the peak value of the Hanbury Brown-Twiss effect using coherent light by lensless holographic projection
Liming Li, Yuhan Guo, Chunguang Meng et al. (+4 more)
Computer-generated holography enables projection of target patterns onto designated planes, providing deterministic control over the probability density function of the projected light intensity. Here, we introduce an active control scheme for the peak value of the Hanbury Brown–Twiss effect, $g^{(2)}(0)$, utilizing lensless
holographic projection with coherent light. Notably, single-frame
holographic projection yields a markedly different $g^{(2)}(0)$ from its multiframe-averaged counterpart due to the presence of coherent speckle noise. With the coherent speckle noise suppression, we derive an analytical expression $g^{(2)}(0)$ on
holographic projection plane, revealing that it is determined by the target coherence length, its statistics, and the numerical aperture of projection system. Our experimental results show good agreement with the theoretical analysis, confirming the joint influence of these factors. By employing dynamic sparse target patterns, we achieve a maximum $g^{(2)}(0)$ of $39.77$. Numerical simulations, benchmarked against experimental measurements, reveal that coherent speckle noise enhances $g^{(2)}(0)$ through mutual superposition with the target pattern, leading to a joint modulation of intensity fluctuations. In summary, by manipulating multiple controllable parameters, we establish a robust strategy for tailoring $g^{(2)}(0)$, paving the way for advanced applications in speckle imaging and optical metrology.
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All authors Liming Li, Yuhan Guo, Chunguang Meng, Wenfei Zhang, Gongxiang Wei, Huiqiang Liu, Wei Li
Atmospheric and Oceanic Physics
Learning Coupled Earth System Dynamics with GraphDOP
Eulalie Boucher, Mihai Alexe, Peter Lean et al. (+7 more)
Interactions between different components of the Earth System (e.g. ocean, atmosphere, land and cryosphere) are a crucial driver of global weather patterns. Modern Numerical Weather Prediction (NWP) systems typically run separate models of the different components, explicitly coupled across their interfaces to additionally model exchanges between the different components. Accurately representing these coupled interactions remains a major scientific and technical challenge of weather
forecasting. GraphDOP is a graph-based machine learning model that learns to forecast weather directly from raw satellite and in-situ observations, without reliance on reanalysis products or traditional physics-based NWP models. GraphDOP simultaneously embeds information from diverse observation sources spanning the full Earth system into a shared latent space. This enables predictions that implicitly capture cross-domain interactions in a single model without the need for any explicit coupling. Here we present a selection of case studies which illustrate the capability of GraphDOP to forecast events where coupled processes play a particularly key role. These include rapid sea-ice freezing in the Arctic, mixing-induced ocean surface cooling during Hurricane Ian and the severe European heat wave of 2022. The results suggest that learning directly from Earth System observations can successfully characterise and propagate cross-component interactions, offering a promising path towards physically consistent end-to-end data-driven Earth System prediction with a single model.
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All authors Eulalie Boucher, Mihai Alexe, Peter Lean, Ewan Pinnington, Simon Lang, Patrick Laloyaux, Lorenzo Zampieri, Patricia de Rosnay, Niels Bormann, Anthony McNally
Medical Physics
Contrast-enhanced X-ray imaging of articular cartilage: reliability of a cationic contrast agent in combination with high-resolution peripheral quantitative computed tomography system
S. Fantoni, M. Berni, R. Fognani et al. (+4 more)
Articular cartilage showcases distinctive mechanical behaviour, attributable to its biphasic composition and hierarchical organization. Proteoglycans, essential constituents of the extracellular
matrix, contribute to tissue swelling, stiffness, and viscoelasticity, thanks to the fixed charge density. Degenerative alterations in proteoglycan content and collagen structure - key indicators of diseases such as osteoarthritis - compromise the mechanical integrity of articular cartilage, underscoring the importance of early detection. Although
magnetic resonance imaging provides valuable compositional information, its limited spatial resolution restricts its effectiveness in evaluating thin soft tissues such as articular cartilage. As an alternative, contrast-enhanced X-ray imaging circumvents such limitation, resorting to the use of radiopaque contrast
agents. In this context, cationic contrast
agents, like CA4+, enable the quantitative assessment of proteoglycan content via electrostatic attraction. High-resolution peripheral quantitative computed
tomography offers an optimal compromise between spatial resolution and radiation exposure, making it a promising tool for clinical use. This study aimed to explore the relationship between proteoglycan content - quantified through contrast-enhanced high-resolution peripheral quantitative computed
tomography combined with the use of CA4+ - and the viscoelastic properties of healthy bovine articular cartilage, assessed via indentation testing. Results highlighted significant correlations between the enhanced radiopacity of articular cartilage, ascribable to the PG content, with parameters representing the mechanical response of the tissue.
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All authors S. Fantoni, M. Berni, R. Fognani, G. Fraterrigo, P. Cardarelli, F. Baruffaldi, M. Baleani
Optics
Elliptical-rod geometries enhance photonic band gaps in disordered stealthy hyperuniform photonic crystals
Kota Asakura, Kazuki Yamamoto and Akihisa Koga
We study two-dimensional photonic
crystals composed of elliptical dielectric rods arranged according to stealthy hyperuniform point patterns. These patterns are characterized by the structure factor, which vanishes for 0 < |k| <= K, where k is the wave number and K denotes the cutoff wave number specifying the stealthiness of the pattern. The optical properties of the photonic
crystals are analyzed by applying the plane-wave expansion method to Maxwell's equations. We demonstrate that photonic
crystals composed of elliptical dielectric rods can exhibit larger photonic band gaps than those with cylindrical rods when both the rod orientation and aspect ratio are properly optimized. This behavior contrasts with that of periodic
lattices such as triangular or square arrays. These findings shed light on the crucial role of structural anisotropy and aperiodic structure in enhancing photonic band-gap formation.
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Fluid Dynamics · Cross-listed from math.NA
Improving the accuracy of meshless methods via resolving power optimisation using multiple kernels
H. Broadley, J. R. C. King and S. J. Lind
Meshless methods are commonly used to determine numerical solutions to partial differential equations (PDEs) for problems involving free surfaces and/or complex geometries, approximating spatial derivatives at collocation points via local kernels with a finite size. Despite their common use in turbulent flow simulations, the accuracy of meshless methods has typically been assessed using their convergence characteristics resulting from the
polynomial consistency of approximations to operators, with little to no
attention paid to the resolving power of the approximation. Here we provide a framework for the optimisation of resolving power by exploiting the non-uniqueness of kernels to provide improvements to numerical approximations of spatial derivatives. We first demonstrate that, unlike in finite-difference approximations, the resolving power of meshless methods is dependent not only on the magnitude of the wavenumber, but also its orientation, before using linear combinations of kernels to maximise resolving power over a range of wavenumbers. The new approach shows improved accuracy in convergence tests and has little impact on stability of time-dependent problems for a range of Eulerian meshless methods. Solutions to a variety of PDE systems are computed, with significant gains in accuracy for no extra computational cost per timestep in Eulerian frameworks. The improved resolution characteristics provided by the optimisation procedure presented herein enable accurate simulation of systems of PDEs whose solution contains short spatial scales such as flow fields with homogeneous isotropic
turbulence.
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Computational Physics
ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature
Aritra Roy, Enrico Grisan, John Buckeridge et al. (+1 more)
Since the advent of various pre-trained large
language models, extracting structured knowledge from scientific text has experienced a revolutionary change compared with traditional machine learning or natural
language processing techniques. Despite these advances, accessible automated tools that allow users to construct, validate, and visualise
datasets from scientific literature extraction remain scarce. We therefore developed ComProScanner, an autonomous multi-
agent platform that facilitates the extraction, validation, classification, and visualisation of machine-readable chemical compositions and properties, integrated with synthesis data from journal articles for comprehensive database creation. We evaluated our framework using 100 journal articles against 10 different LLMs, including both open-source and proprietary models, to extract highly complex compositions associated with ceramic piezoelectric materials and corresponding piezoelectric strain coefficients (d33), motivated by the lack of a large
dataset for such materials. DeepSeek-V3-0324 outperformed all models with a significant overall accuracy of 0.82. This framework provides a simple, user-friendly, readily-usable package for extracting highly complex experimental data buried in the literature to build machine learning or deep learning
datasets.
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All authors Aritra Roy, Enrico Grisan, John Buckeridge, Chiara Gattinoni
Optics · Cross-listed from cond-mat.dis-nn
Nonergodic extended phase for waves in three dimensions
Marcus Prado, Romain Bachelard, Robin Kaiser et al. (+1 more)
Wave transport in complex media is determined by the nature of quasimodes at the microscopic level. In three dimensional disordered media, waves generally undergo a phase transition from
diffusion to Anderson localization, characterized by exponentially localized modes. A remarkable exception are electromagnetic waves, whose vector-like nature prevents Anderson localization to occur. Here we demonstrate that both scalar and vector (electromagnetic) waves exhibit a non-ergodic extended phase characterized by fractal quasimodes, for a broad range of disorder strengths. While electromagnetic waves remain in the non-ergodic extended phase at high disorder strength, scalar waves eventually enter a localized regime. These results pave the way for the engineering of anomalous wave transport phenomena in disordered media without spatial correlations.
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All authors Marcus Prado, Romain Bachelard, Robin Kaiser, Felipe A. Pinheiro
Applied Physics
Multi-Task Deep Learning for Surface Metrology
D. Kucharski, A. Gaska, T. Kowaluk et al. (+9 more)
A reproducible deep learning framework is presented for surface metrology to predict surface texture parameters together with their reported standard uncertainties. Using a multi-instrument
dataset spanning tactile and optical systems, measurement system type classification is addressed alongside coordinated regression of Ra, Rz, RONt and their uncertainty targets (Ra_uncert, Rz_uncert, RONt_uncert). Uncertainty is modelled via
quantile and heteroscedastic heads with post-hoc conformal calibration to yield calibrated intervals. On a held-out set, high fidelity was achieved by single-target regressors (R2: Ra 0.9824, Rz 0.9847, RONt 0.9918), with two uncertainty targets also well modelled (Ra_uncert 0.9899, Rz_uncert 0.9955); RONt_uncert remained difficult (R2 0.4934). The classifier reached 92.85% accuracy and probability calibration was essentially unchanged after temperature scaling (ECE 0.00504 -> 0.00503 on the test split). Negative transfer was observed for naive multi-output trunks, with single-target models performing better. These results provide calibrated predictions suitable to inform instrument selection and acceptance decisions in metrological workflows.
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All authors D. Kucharski, A. Gaska, T. Kowaluk, K. Stepien, M. Repalska, B. Gapinski, M. Wieczorowski, M. Nawotka, P. Sobecki, P. Sosinowski, J. Tomasik, A. Wojtowicz
Applied Physics
Comparative Analysis of Thermal Models for Test Masses in Next-Generation Gravitational Wave Interferometers
Vincenzo Pierro, Vincenzo Fiumara, Guerino Avallone et al. (+8 more)
Accurate thermal modeling of Terminal Test Masses (TTMs) is crucial for optimizing the sensitivity of gravitational wave interferometers like Virgo. In fact, in such gravitational wave detectors even minimal
laser power absorption can induce performance-limiting thermal effects. This paper presents a detailed investigation into the steady-state thermal behavior of TTMs. In particular, future scenarios of increased intracavity
laser beam power and optical coating absorption are considered. We develop and compare two numerical models: a comprehensive model incorporating volumetric heat absorption in both the multilayer coating and the bulk substrate, and a simplified reduced model where the coating's thermal impact is represented as an effective surface boundary condition on the substrate. Our simulations were focused on a ternary coating design, which is a candidate for use in next-generation detectors. Results reveal that higher coating absorption localizes peak temperatures near the coating–
vacuum interface. Importantly, the comparative analysis demonstrates that temperature predictions from the reduced model differ from the detailed model by only milli-Kelvins, a discrepancy often within the experimental uncertainties of the system's thermo-physical parameters. This finding suggests that computationally efficient reduced models can provide sufficiently accurate results for thermal management and first-order distortion analyses. Moreover, the critical role of accurately characterizing the total power absorbed by the coating is emphasized.
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All authors Vincenzo Pierro, Vincenzo Fiumara, Guerino Avallone, Giovanni Carapella, Francesco Chiadini, Roberta De Simone, Rosalba Fittipaldi, Gerardo Iannone, Alessandro Magalotti, Enrico Silva, Veronica Granata
Fluid Dynamics
Microfluidic Study of Evaporation-Driven Crystallization of Saline and Ammonia Brines under Hydrogen Flow
Karol M. Dąbrowski, Mohammad Nooraiepour and Mohammad Masoudi
Underground storage of hydrogen and ammonia in geological formations is essential for renewable energy integration, but salt precipitation during gas injection may threaten storage performance. While extensively studied for CO2 systems, precipitation mechanisms in hydrogen-brine and ammonia-brine systems remain poorly understood. This study presents a comprehensive microfluidic investigation of salt crystallization during hydrogen injection into saline and ammonia-containing brines using high-pressure microfluidics. We conducted 81 high-pressure experiments systematically varying brine composition (1-5 mol/kg NaCl), chemical additives (surfactants, alcohols, ammonia), and hydrogen flow rates (200-1300 mL/min). Quantitative image analysis reveals that hydrogen-induced precipitation differs fundamentally from CO2 systems. Hydrogen drives physical precipitation via evaporation and capillary trapping, producing discrete, localized deposits. In contrast, CO2-ammonia systems generate extensive reactive precipitation of ammonium bicarbonate with interconnected
crystal networks. Interfacial tension (IFT) controls both residual brine distribution and final
crystal coverage: high-IFT fluids form large, interconnected brine pools promoting extensive crystallization, while low-IFT fluids create isolated pools reducing
crystal coverage by 50%. Alcohol and surfactant additives suppress precipitation by enhancing brine mobility, whereas ammonia paradoxically increases
crystal fractions despite lower IFT. Higher flow rates accelerate crystallization across all compositions, enabling operational mitigation strategies. and demonstrate that gas-specific, rather than CO2-analog, risk assessments are essential for underground hydrogen storage design. The effectiveness of chemical additives offers promising pathways for near-wellbore protection in underground hydrogen storage operations.
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Fluid Dynamics
Vortex ring-cylinder interactions: regimes, reconnection, and the role of topology
Andres Herrera-Gómez, Juan Ángel Tendero-Ventanas and Rodolfo Ostilla-Mónico
We investigate the interaction between
vortex rings and cylindrical obstacles using direct numerical simulations across a wide range of
geometric and dynamical parameters. The flow is characterized in terms of the diameter ratio between ring and object $T_D = d/D$, the Reynolds number based on circulation $Re_\Gamma$, and the slenderness ratio $\Lambda$. By systematically varying $T_D$ and $Re_\Gamma$, we identify three distinct interaction regimes: the wire, cutting, and wall regimes. In the wire regime ($T_D \lesssim 0.05$), the primary
vortex ring survives the interaction with limited deformation and carries a weak lobe of secondary vorticity generated by the object's boundary layer. As $T_D$ increases, the interaction transitions to the cutting regime, where the ring is split into two secondary structures formed through the reconnection between boundary-layer and ring vorticity. For sufficiently large obstacles ($T_D \gtrsim 0.8$), the wall regime emerges, in which boundary-layer vorticity dominates and the primary ring is deflected and stretched along the obstacle surface. The transition between regimes depends primarily on $T_D$, while increasing $Re_\Gamma$ enhances vortical dynamics producing additional small-scale and tertiary structures. Finally, by modifying the
topology of the obstacle, we demonstrate that reconnection and recovery of the primary ring depend critically on the
topology of the secondary vorticity. These results provide a unified framework for interpreting
vortex-body interactions, bridging the gap between
vortex ring, tube, and wall collision dynamics.
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Optics
From Light Diffusion to Photocatalytic Rates: Compact Scaling Laws for Strongly Scattering Porous Slabs
Renaud A. L. Vallée and Rénal Backov
Light transport in strongly scattering porous photocatalytic materials governs the spatial distribution of absorbed
photons and therefore the generation of charge carriers driving photocatalytic reactions. Yet translating measured optical properties of such media into intrinsic reaction rate constants remains challenging, as it requires simultaneously accounting for multiple scattering, boundary losses, photochemical efficiency, and surface kinetics. Here we develop a compact analytical framework that unifies these processes for nanoparticle-loaded photocatalytic slabs. Using a finite-slab
diffusion model with extrapolated boundaries, we derive closed-form expressions for the fluence field and couple them to a photochemical
quantum efficiency and first-order surface kinetics. The resulting predictors yield intrinsic volumetric and areal rate constants whose dependence on the transport mean free path, optical thickness, and surface-to-volume ratio emerges transparently. Validation against Monte-Carlo
photon-migration simulations shows that the
diffusion approximation reproduces the fluence and generation-rate profiles with a modest multiplicative mismatch, typically within a factor of about 1.20-1.39, depending on the anisotropy and scattering phase function. This level of agreement is consistent with the known limits of the
diffusion approximation and is sufficient to enable reliable, design-oriented predictions. The analytical descriptors introduced here, such as l*, S/V and the extrapolation length zb are general, physically interpretable, and directly integrable into data-driven optimisation and geometry-engineered reactor design. The framework thus provides a versatile and physically grounded tool for photocatalytic systems across diverse applications, including VOC photo-degradation, indoor air purification, and
solar-fuel production.
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Fluid Dynamics · Cross-listed from math.AP
On an Analytical Criterion for Detecting Intermittent Turbulent Behaviour of Solutions of Partial Differential Equations
Michele V Bartuccelli and Guido Gentile
A main question in the study of partial differential equations is the following: how do we understand the nature of the solutions and, in particular, how do we determine if a given solution shows turbulent or non-turbulent behaviour? Being able to answer such a question would be a major advance in the comprehension of the nature of
turbulence. In this paper we focus on the case of intermittent
turbulence and provide an analytical criterion, based on the crest factor, which captures the essential feature of the solutions. By computing the crest factor for the solutions of some classical equations, both linear and nonlinear, we illustrate the capability of the criterion for discerning between solutions exhibiting time-intermittent
turbulence behaviour and solutions which either are not turbulent or show statistically stationary
turbulence, like, for example, in the case described by Kolmogorov's theory.
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Optics
Casimir effect in twisted photonic gratings with in-plane chirality
Natalia S. Salakhova, Sergey A. Dyakov, Ilia M. Fradkin et al. (+1 more)
We investigate the Casimir effect in a system of two twisted photonic gratings made of uniaxially anisotropic materials. Two distinct configuretions are explored: a stack of symmetric gratings and a stack of in-plane
chiral gratings, with the latter realized by choosing specific orientaton of anisotropy axis relative to stripes. We apply the reflection-
matrix-based Casimir Lifshitzformalism to explore hoe twiat angle, material anisotropy, and the separation between gratings influence Casimir energy, force and torque. Our calculations reveal that the
equilibrium orientation of the gratings is governed by the anisotropy rotation angles, leading to a
chiral configuration where the anisotropy axes of the upper and lower gratings are mutually parallel. These findings demonstrate that material anisotropy provids a pwerful mechanism for controlling rotational
alignment forces in nanophotonic system.
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All authors Natalia S. Salakhova, Sergey A. Dyakov, Ilia M. Fradkin, Nikolay A. Gippius