Computational Physics · Cross-listed from gr-qc
Twist and higher modes of a complex scalar field at the threshold of collapse
Krinio Marouda, Daniela Cors, Hannes R. Rüter et al. (+2 more)
We investigate the threshold of collapse of a massless complex scalar field in axisymmetric spacetimes under the ansatz of Choptuik et al. 2004, in which a
symmetry depending on the azimuthal parameter $m$ is imposed on the scalar field. This allows for both non-vanishing twist and angular momentum. We extend earlier work to include higher angular modes. Using the pseudospectral code bamps with a new adapted
symmetry reduction method, which we call $m$-cartoon, and a generalized twist-compatible apparent horizon finder, we evolve near-critical initial data to the verge of black hole formation for the lowest nontrivial modes, $m=1$ and $m=2$. For $m=1$ we recover discrete self-similarity with echoing period $\Delta\simeq0.42$ and power-law scaling with exponent $\gamma\simeq0.11$, consistent with earlier work. For $m=2$ we find that universality is maintained within this nonzero fixed-$m$
symmetry class but with smaller period and critical exponents, $\Delta\simeq0.09$ and $\gamma\simeq0.035$, establishing an explicit dependence of the critical solution on the angular mode. Analysis of the relation between the angular momentum and the mass of apparent horizons at the instant of formation, $J_{\mathrm{AH}}{-}M_{\mathrm{AH}}$, shows that the effect of angular momentum is minimal at the threshold, with $\chi_{\mathrm{AH}}=J_{\mathrm{AH}}/M_{\mathrm{AH}}^2\to0$, and, therefore, excludes extremal black holes for the families under consideration. Our results demonstrate that while universality and DSS hold within each $m$-sector, the critical universal values vary with $m$, and neither extremality nor bifurcation occur in the complex scalar field model within the families considered here.
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All authors Krinio Marouda, Daniela Cors, Hannes R. Rüter, Alex Vaño-Viñuales, David Hilditch
Computational Physics · Cross-listed from cond-mat.mtrl-sci
The phase-field model of fracture incorporating Mohr-Coulomb, Mogi-Coulomb, and Hoek-Brown strength surfaces
S Chockalingam, Adrian Buganza Tepole and Aditya Kumar
Classical phase-field theories of brittle fracture capture toughness-controlled crack growth but do not account for the material's strength surface, which governs fracture nucleation in the absence of cracks. The phase-field formulation of Kumar et al. (2020) proposed a blueprint for incorporating the strength surface while preserving toughness-controlled propagation by introducing a nucleation driving force and presented results for the Drucker-Prager surface. Following this blueprint, Chockalingam (2025) recently derived a general driving-force expression that incorporates arbitrary strength surfaces. The present work implements this driving force within a finite-element framework and incorporates representative strength surfaces that span diverse mathematical and physical characteristics-the Mohr-Coulomb, 3D Hoek-Brown, and Mogi-Coulomb surfaces. Through simulations of canonical fracture problems, the formulation is comprehensively validated across fracture regimes, capturing (i) nucleation under uniform stress, (ii) crack growth from large pre-existing flaws, and (iii) fracture governed jointly by strength and toughness. While the strength surfaces examined here already encompass a broad range of brittle materials, the results demonstrate the generality and robustness of the proposed driving-force construction for materials governed by arbitrary strength surfaces.
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Fluid Dynamics
Dissecting coherent motions in extreme wall shear stress events within adverse pressure gradient turbulent boundary layers
Leandro J. O. Silva and William R. Wolf
Coherent motions associated with extreme wall shear stress events are investigated for adverse pressure gradient turbulent boundary layers (APG-TBLs). The analyses are performed using wall-resolved large eddy simulations of a NACA0012 airfoil at angles of attack of 9 and 12 deg. and Reynolds number 400000. The suction side exhibits attached TBLs which develop under progressively stronger APGs. A quadrant decomposition of Reynolds shear stress shows that sweeps and ejections dominate the momentum exchange between the mean and fluctuating fields, with the intensity of sweeps near the wall growing more rapidly with APG strength. Probability density functions of wall shear stress reveal a higher frequency of backflow events and an increased distribution
symmetry with stronger APGs. Extreme positive and backflow events are examined using space–time correlations and conditional statistics. Conditional averages show that backflow events originate from inner-layer sweep motions bringing high-momentum fluid toward the wall, followed by ejections that drive local deceleration. In such cases, the intensity of ejections is modulated by the APG strength. The dynamics of coherent turbulent structures and their interactions are examined using conditional flow field analyses. For extreme positive events, stronger APGs lead to shorter high-speed streaks, while the associated sweep motions generate spanwise velocities that increasingly influence the near-wall dynamics. In the case of backflows, stronger APGs shorten low-speed streaks and amplify high-speed structures associated with sweep motions, promoting spanwise
alignment of vortical structures. Overall, APGs modify the structure and dynamics of extreme near-wall events by reshaping the balance and spatial organization of sweep- and ejection-dominated motions.
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Optics
Probability Distribution for Coherent Transport of Random Waves
Yunrui Wang and Cheng Guo
We establish a comprehensive probability theory for coherent transport of random waves through arbitrary linear media. The transmissivity distribution for random coherent waves is a fundamental B-spline with
knots at the transmission
eigenvalues. We analyze the distribution's shape, bounds, moments, and asymptotic behaviors. In the large n limit, the distribution converges to a Gaussian whose mean and variance depend solely on those of the
eigenvalues. This result resolves the apparent paradox between bimodal
eigenvalue distribution and unimodal transmissivity distribution.
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Computational Physics
Combining Harmonic Sampling with the Worm Algorithm to Improve the Efficiency of Path Integral Monte Carlo
Sourav Karmakar, Sutirtha Paul, Adrian Del Maestro et al. (+1 more)
We propose an improved Path Integral Monte Carlo (PIMC)
algorithm called Harmonic PIMC (H-PIMC) and its generalization, Mixed PIMC (M-PIMC). PIMC is a powerful tool for studying
quantum condensed phases. However, it often suffers from a low acceptance ratio for solids and dense confined
liquids. We develop two sampling schemes especially suited for such problems by dividing the potential into its harmonic and anharmonic contributions. In H-PIMC, we generate the imaginary time paths for the harmonic part of the potential exactly and accept or reject it based on the anharmonic part. In M-PIMC, we restrict the harmonic sampling to the vicinity of local minimum and use standard PIMC otherwise, to optimize efficiency. We
benchmark H-PIMC on systems with increasing anharmonicity, improving the acceptance ratio and lowering the auto-correlation time. For weakly to moderately anharmonic systems, at $\beta \hbar \omega=16$, H-PIMC improves the acceptance ratio by a factor of 6-16 and reduces the autocorrelation time by a factor of 7-30. We also find that the method requires a smaller number of imaginary time slices for convergence, which leads to another two- to four-fold acceleration. For strongly anharmonic systems, M-PIMC converges with a similar number of imaginary time slices as standard PIMC, but allows the optimization of the auto-correlation time. We extend M-PIMC to periodic systems and apply it to a sinusoidal potential. Finally, we combine H- and M-PIMC with the worm
algorithm, allowing us to obtain similar efficiency gains for systems of indistinguishable particles.
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All authors Sourav Karmakar, Sutirtha Paul, Adrian Del Maestro, Barak Hirshberg
Fluid Dynamics
Towards extreme event prediction of turbulent flows with quantized local reduced-order models
Antonio Colanera and Luca Magri
This work develops quantized local reduced-order models (ql-ROMs) of the turbulent Minimal Flow Unit (MFU) for the analysis and interpretation of intermittent dissipative dynamics and extreme events. The ql-ROM combines data-driven clustering of the flow state space with intrusive Galerkin projection on locally defined Proper Orthogonal Decomposition (POD) bases. This construction enables an accurate and stable low-dimensional representation of nonlinear flow dynamics whilst preserving the structure of the governing equations. The model is trained on direct numerical simulation data of the MFU. When deployed, the ql-ROM is numerically stable for long-term integration, and correctly infers the statistical behavior of the kinetic energy and dissipation observed of the full-order system. A local modal energy-budget formulation is employed to quantify intermodal energy transfer and viscous dissipation within each region of the attractor. The analysis reveals that dissipation bursts correspond to localized energy transfer from streamwise streaks and travelling-wave modes toward highly dissipative vortical structures, consistent with the self-sustaining process of near-wall
turbulence. Beyond reduced modeling, the ql-ROM framework provides a pathway for the reduced-space characterization and potential prediction of extreme events. ql-ROM offer an interpretable and computationally efficient framework for the analysis and prediction of extreme events in turbulent flows.
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Computational Physics · Cross-listed from math.OC
Computational Modeling and Learning-Based Adaptive Control of Solid-Fuel Ramjets
Gohar T. Khokhar, Kyle Hanquist, Parham Oveissi et al. (+2 more)
Solid-fuel ramjets offer a compact, energy-dense propulsion option for long-range, high-speed flight but pose significant challenges for thrust regulation due to strong nonlinearities, limited actuation authority, and complex multi-physics coupling between fuel regression, combustion, and compressible flow. This paper presents a computational and control framework that combines a computational fluid dynamics model of an SFRJ with a learning-based adaptive control approach. A CFD model incorporating heat addition was developed to characterize thrust response, establish the operational envelope, and identify the onset of inlet unstart. An adaptive proportional-integral controller, updated online using the retrospective cost adaptive control (RCAC)
algorithm, was then applied to regulate thrust. Closed-loop simulations demonstrate that the RCAC-based controller achieves accurate thrust regulation under both static and dynamic operating conditions, while remaining robust to variations in commands, hyperparameters, and inlet states. The results highlight the suitability of RCAC for SFRJ control, where accurate reduced-order models are challenging to obtain, and underscore the potential of learning-based adaptive control to enable robust and reliable operation of SFRJs in future air-breathing propulsion applications.
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All authors Gohar T. Khokhar, Kyle Hanquist, Parham Oveissi, Alex Dorsey, Ankit Goel
Atomic Physics · Cross-listed from cond-mat.quant-gas
Superfluid Fraction of a 2D Bose-Einstein Condensate in a Triangular Lattice
F. Rabec, G. Brochier, S. Wattellier et al. (+5 more)
We experimentally investigate the superfluid properties of a two-dimensional, weakly interacting Bose-Einstein condensate in the zero-temperature regime, when it is subjected to a triangular optical
lattice potential. We implement an original method, which involves solving the hydrodynamic continuity equation to extract the superfluid fraction tensor from the measured in situ density distribution of the fluid at rest. In parallel, we apply an independent dynamical approach that combines compressibility and sound velocity measurements to determine the superfluid fraction. Both methods yield consistent results in good agreement with simulations of the Gross-Pitaevskii equation as well as with the Leggett bounds determined from the measured density profiles.
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All authors F. Rabec, G. Brochier, S. Wattellier, G. Chauveau, Y. Li, S. Nascimbene, J. Dalibard, J. Beugnon
Plasma Physics
Machine Learning for Electron-Scale Turbulence Modeling in W7-X
Ionut-Gabriel Farcas, Don Lawrence Carl Agapito Fernando, Alejandro Banon Navarro et al. (+2 more)
Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as parameter exploration and design optimization. This work investigates machine-learning-driven reduced models for Electron Temperature Gradient (ETG)
turbulence in the Wendelstein 7-X (W7-X) stellarator. We develop physics-guided scaling laws to predict the ETG heat flux at seven radial locations as functions of three key
plasma parameters: the normalized electron temperature gradient ($\omega_{T_e}$), the ratio of normalized electron temperature and density gradients ($\eta_e$), and the electron-to-ion temperature ratio ($\tau$). The model coefficients are determined through regression combined with an active learning strategy. The procedure initializes the scaling laws using low-cardinality sparse-grid training data and iteratively enriches the training set by selecting maximally informative samples from an existing simulation database. The predictive performance of the models is assessed using out-of-sample
datasets comprising more than $393$ points per radial location. Using the coefficients identified at the seven training radial locations, we further derive regression-based parameterizations for the scaling-law coefficients as functions of radial position. The resulting models are then evaluated at three additional radial locations not used during training, including both interpolation and moderate extrapolation cases. Overall, our reduced models demonstrate good predictive performance and achieve accuracy comparable to the original reference simulations, including in interpolation and moderate extrapolation regimes. An important finding is that a single radius-independent model cannot adequately describe ETG transport across the W7-X core, suggesting the presence of geometry-dependent physics not captured by the present formulation.
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All authors Ionut-Gabriel Farcas, Don Lawrence Carl Agapito Fernando, Alejandro Banon Navarro, Gabriele Merlo, Frank Jenko
Computational Physics
Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI
Yoh-ichi Mototake and Makoto Sasaki
Physics-informed machine learning (PIML) integrates partial differential equations (PDEs) into machine learning models to solve inverse problems, such as estimating coefficient functions (e.g., the
Hamiltonian function) that characterize physical systems. This framework enables data-driven understanding and prediction of complex physical phenomena. While coefficient functions in PIML are typically estimated on the basis of predictive performance, physics as a discipline does not rely solely on prediction accuracy to evaluate models. For example, Kepler's heliocentric model was favored owing to small discrepancies in planetary motion, despite its similar predictive accuracy to the geocentric model. This highlights the inherent uncertainties in data-driven model inference and the scientific importance of selecting physically meaningful solutions. In this paper, we propose a framework to quantify and analyze such uncertainties in the estimation of coefficient functions in PIML. We apply our framework to reduced model of magnetohydrodynamics and our framework shows that there are uncertainties, and unique identification is possible with
geometric constraints. Finally, we confirm that we can estimate the reduced model uniquely by incorporating these constraints.
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Chemical Physics · Cross-listed from quant-ph
Preferred Basis in Coupled Electron-Nuclear Dynamics
Junhyeok Bang
Beyond the adiabatic regime, our understanding of
quantum dynamics in coupled systems remains limited, and the choice of representation continues to obscure physical interpretation and simulation accuracy. Here we propose a natural and efficient basis for electron
nuclear dynamics by drawing on the concepts of pointer and preferred states from decoherence theory, adapted to systems where electrons and
nuclei interact strongly. Within this framework, we show that 1) the independent dynamics exploited by mixed
quantum classical (MQC) methods is best understood as a manifestation of
entanglement viewed in a preferred basis, rather than a consequence of decoherence, and 2) the adiabatic Born Oppenheimer states satisfy the conditions of an approximate preferred basis. This perspective reconciles widely used approximations with a more fundamental structure of the theory and provides a systematic route to more reliable MQC strategies. In effect, we revisit MQC methods through the lens of preferred states, clarifying when they succeed and how they can be improved.
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Plasma Physics
Electromagnetic plasma wave modes propagating along light-cone coordinates
Felipe A. Asenjo and Swadesh M. Mahajan
We present new electromagnetic
plasma wavepacket solutions that propagates in one time and one space coordinates. Differently to the usual plane wave solution, which is written in terms of separation of variables, all our solutions are along the light-cone coordinates. This allow us to find several new solutions whose functionality properties rely on the conditions imposed on the choice for their light-cone coordinates dependence. The presented wavepacket solutions are constructed in terms of multiplications of Airy functions,
Parabolic cylinder functions, Mathieu functions, or Bessel functions. We thoroughly analyze the case of a double Airy solution, which have new electromagnetic properties, as a defined wavefront, and velocity faster than the electromagnetic plane wave counterpart solution. It is also mentioned how more general structured wavepackets can be constructed from these new solutions.
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Atmospheric and Oceanic Physics
Annual net community production and carbon exports in the central Sargasso Sea from autonomous underwater glider observations
Ruth G. Curry, Michael W. Lomas, Megan R. Sullivan et al. (+1 more)
Despite decades of ship-based observations at the Bermuda Atlantic Timeseries Study (BATS) site, ambiguities linger in our understanding of the region's annual carbon cycle. Difficulties reconciling geochemical estimates of annual net community production (ANCP) with direct measurements of nutrient delivery and carbon exports (EP) have implied either an insufficient understanding of these processes, and/or that they are playing out on shorter time and spatial scales than resolved by monthly sampling. We address the latter concern using autonomous
underwater gliders equipped with biogeochemical sensors to quantify ANCP from mass balances of oxygen (O2) and nitrate (NO3) over a full annual cycle. The timing, amplitude and distribution of O2 production, consumption, and NO3 fluxes reaffirm ideas about strong seasonality in physical forcing and trophic structure creating a dual system: i.e. production fueled by NO3 supplied to the photic zone from deeper layers in the first half of the year, versus being recycled within the upper ocean during the second half. The evidence also supports recently proposed hypotheses regarding the production and recycling of carbon with non-Redfield characteristics, deplete in nitrogen and phosphorus, to explain observed patterns of high NCP in the absence of significant NO3 supply. It further identifies significant contributions to ANCP and EP potentially linked to vertically migrating communities of salps in spring after all convective activity has ceased. The improved resolution of the
datasets, combined with more precise definitions of photic and subphotic integration depths, brings the estimates of ANCP and EP into better
alignment with each other.
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All authors Ruth G. Curry, Michael W. Lomas, Megan R. Sullivan, Damian Grundle
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Uncertainty Quantification for Reduced-Order Surrogate Models Applied to Cloud Microphysics
Jonas E. Katona, Emily K. de Jong and Nipun Gunawardena
Reduced-order models (ROMs) can efficiently simulate high-dimensional physical systems but lack robust uncertainty quantification methods. Existing approaches are frequently architecture- or training-specific, which limits flexibility and generalization. We introduce a post hoc, model-agnostic framework for predictive uncertainty quantification in latent space ROMs that requires no modification to the underlying architecture or training procedure. Using conformal prediction, our approach estimates statistical prediction intervals for multiple components of the ROM pipeline: latent dynamics, reconstruction, and end-to-end predictions. We demonstrate the method on a latent space dynamical model for cloud microphysics, where it accurately predicts the
evolution of droplet-size distributions and quantifies uncertainty across the ROM pipeline.
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Applied Physics
Self-mixing-based photoacoustic sensing
Tecla Gabbrielli, Jacopo Pelini, Chenhong Zhang et al. (+7 more)
Versatile, ultracompact, easy-to-handle, high-sensitivity sensors are compelling tools for in situ pivotal applications, such as medical diagnostics, security and safety assessments, and environmental control. In this work, we combine photoacoustic spectroscopy and feedback interferometry, proposing a novel trace-gas sensor equipped with a self-mixing readout. This scheme demonstrates a readout sensitivity comparable to that of bulkier state-of-the-art balanced Michelson-interferometric schemes, achieving the same spectroscopic performance in terms of signal-to-noise ratio (SNR) and minimum detection limit (MDL). At the same time, the self-mixing readout benefits from a reduced size and a lower baseline, paving the way for future system downsizing and integration while offering a higher detectability for lower gas concentrations. Moreover, the intrinsic wavelength independence of both self-mixing and photoacoustic techniques allows the applicability and tailorability of the sensor to any desired spectral range.
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All authors Tecla Gabbrielli, Jacopo Pelini, Chenhong Zhang, Francesco Cappelli, Mario Siciliani de Cumis, Stefano Dello Russo, Maria Concetta Canino, Alberto Roncaglia, Paolo De Natale, Simone Borri
Fluid Dynamics · Cross-listed from cond-mat.soft
Hysteresis in the freeze-thaw cycle of emulsions and suspensions
Wilfried Raffi, Jochem G. Meijer and Detlef Lohse
Freeze-thaw cycles can be regularly observed in nature in water and are essential in industry and science. Objects present in the medium will interact with either an advancing solidification front during freezing or a retracting solidification front, i.e., an advancing melting front, during thawing. It is well known that objects show complex behaviours when interacting with the advancing solidification front, but the extent to which they are displaced during the retraction of the solid-
liquid interface is less well understood. To study potential hysteresis effects during freeze-thaw cycles, we exploit experimental model systems of oil-in-water emulsions and polystyrene (PS) particle suspensions, in which a water-ice solidification front advances and retracts over an individual immiscible (and deformable) oil droplet or over a solid PS particle. We record several interesting hysteresis effects, resulting in non-zero relative displacements of the objects between freezing and thawing. PS particles tend to migrate further and further away from their initial position, whereas oil droplets tend to return to their starting positions during thawing. We rationalize our experimental findings by comparing them to our prior theoretical model of Meijer, Bertin & Lohse, Phys. Rev. Fluids (2025), yielding a qualitatively good agreement. Additionally, we look into the reversibility of how the droplet deforms and re-shapes throughout one freeze-thaw cycle, which will turn out to be remarkably robust.
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Geophysics
SeismoStats: A Python Package for Statistical Seismology
Aron Mirwald, Nicolas Schmid, Leila Mizrahi et al. (+4 more)
We introduce SeismoStats, a Python package that enables essential statistical seismology analyses. The package provides user-friendly tools to download and manipulate earthquake catalogs, to visualize them, and to estimate the a- and b-value of the Gutenberg-Richter law, or the magnitude of completeness. This comprehensively tested, well-documented, and openly accessible Python package is intended to serve as the foundation of a long-term community effort, continually expanding in functionality through shared contributions. We invite seismologists and developers to contribute ideas and code to support and shape its future development.
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All authors Aron Mirwald, Nicolas Schmid, Leila Mizrahi, Marta Han, Alicia Rohnacher, Vanille A. Ritz, Stefan Wiemer
Plasma Physics
Approaching the thermodynamic limit of a bounded one-component plasma
D. I. Zhukhovitskii and E. E. Perevoshchikov
The classical one-component
plasma (OCP) bounded by a spherical surface reflecting ions (BOCP) is studied using molecular dynamics (MD). Simulations performed for a series of sufficiently large BOCP's make it possible to establish the size dependencies for the investigated quantities and extrapolate them to the thermodynamic limit. In particular, the total electrostatic energy per ion is estimated in the limit of infinite BOCP in a wide range of the Coulomb coupling parameter $\Gamma$ from 0.03 to 1000 with the relative error of the order 0.1%. Calculated energies are by about 0.5% lower as compared to the modern Monte Carlo (MC) simulation data obtained by different authors at $\Gamma<30$ and almost coincide with the MC results at $\Gamma>175$. We introduce two more converging characteristic energies, the excess interatomic electrostatic energy and the excess ion-background electrostatic energy, which enable us to calculate the ionic compressibility factor inaccessible in conventional MC and MD simulation of the OCP with periodic boundary conditions. The derived wide-range ionic equation of state can be recommended for testing OCP simulations with various effective interaction potentials. Based on this equation, we propose an improved cutoff radius for the interionic forces implemented in LAMMPS and perform MD simulation of the OCP to demonstrate that location of the metastable region of the fluid-solid phase transition depends sensitively on this radius.
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Optics · Cross-listed from eess.IV
$\mu$NeuFMT: Optical-Property-Adaptive Fluorescence Molecular Tomography via Implicit Neural Representation
Shihan Zhao, Jianru Zhang, Yanan Wu et al. (+6 more)
Fluorescence Molecular
Tomography (FMT) is a promising technique for non-invasive 3D visualization of fluorescent probes, but its reconstruction remains challenging due to the inherent ill-posedness and reliance on inaccurate or often-unknown tissue optical properties. While deep learning methods have shown promise, their supervised nature limits generalization beyond training data. To address these problems, we propose $\mu$NeuFMT, a self-supervised FMT reconstruction framework that integrates implicit neural-based scene representation with explicit physical modeling of
photon propagation. Its key innovation lies in jointly optimize both the fluorescence distribution and the optical properties ($\mu$) during reconstruction, eliminating the need for precise prior knowledge of tissue optics or pre-conditioned training data. We demonstrate that $\mu$NeuFMT robustly recovers accurate fluorophore distributions and optical coefficients even with severely erroneous initial values (0.5$\times$ to 2$\times$ of ground truth). Extensive numerical, phantom, and in vivo validations show that $\mu$NeuFMT outperforms conventional and supervised deep learning approaches across diverse heterogeneous scenarios. Our work establishes a new paradigm for robust and accurate FMT reconstruction, paving the way for more reliable molecular imaging in complex clinically related scenarios, such as fluorescence guided surgery.
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All authors Shihan Zhao, Jianru Zhang, Yanan Wu, Linlin Li, Siyuan Shen, Xingjun Zhu, Guoyan Zheng, Jiahua Jiang, Wuwei Ren
Optics
Portable, cost_effective UV_vis_NIR microspectrophotometer for absorption and fluorescence microscopy and spectroscopy
Negar Karpourazar, Keyvan Khosh Abady and Peter M. Rentzepis
This article describes the design and construction of a portable, compact, and cost-effective microspectrophotometer (MSP) that operates in the range of (200_800 nm). This microscope spectrophotometer records highresolution absorption and emission spectra in situ. The dual head design of this MSP enables simultaneous real time imaging and spectral recording of heterogeneous samples with high selectivity and micrometer spatial resolution. Our compact, portable MSP design reduces construction costs by more than 20 times compared to commercial benchtop alternatives, primarily due to its innovative illumination system and microscope objective design. The performance of the UV_vis_NIR MSP was confirmed by comparing the absorption and fluorescence spectra of an aqueous solution of Ru(bpy) obtained with our system to those measured by commercial spectroscopic systems. The high accuracy and reliability of our system in measuring absorbance and fluorescence were confirmed by R squared values of 0.998 and 0.990, respectively, from colorimetric and fluorometric tests. The MSP was further used to record absorption and fluorescence spectra from a variety of samples, including dyes and
protein crystals, in both the solution and solid state, as well as individual living
cells. This compact instrument is ideal for rapid, in situ spectroscopic measurements and is expected to find on site applications across various fields, such as environmental monitoring, biological research, forensic analysis, and materials characterization.
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Fluid Dynamics
Implementation and verification of the resolved Reynolds stress transport equations in OpenFOAM
Mario J. Rincón, Christoffer Hansen, Martino Reclari et al. (+1 more)
The analysis of the Reynolds Stress Transport Equation (RSTE) provides fundamental physical insights that are essential for the development and validation of advanced
turbulence models. However, a comprehensive and validated tool for computing the complete RSTE budget is absent in the widely-used open-source Computational Fluid Dynamics (CFD) framework, OpenFOAM. This work addresses this gap by presenting the implementation and a posteriori validation of a function object library for calculating all terms of the resolved RSTE budget in Large-Eddy Simulations (LES). The library is applied to simulate two canonical wall-bounded turbulent flows: a channel flow and a pipe flow, both at a friction Reynolds number of Re$_{\tau}=180$. The implementation is validated through a mesh refinement study where the results from the LES simulations are systematically compared against high-fidelity Direct Numerical Simulation (DNS) data. The computed budget terms are observed to converge systematically towards the DNS reference data. This validation demonstrates that the implemented library accurately captures the intricate balance of all budget terms. This contribution provides the open-source CFD community with a powerful utility for detailed
turbulence analysis, thereby facilitating deeper physical understanding and accelerating the development of next-generation
turbulence models.
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All authors Mario J. Rincón, Christoffer Hansen, Martino Reclari, Mahdi Abkar
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
A copper sulfide-hydroxypropyl $\beta$-Cyclodextrin-reduced graphene oxide composite for highly sensitive electrochemical detection of 5-hydroxytryptamine in biological samples
Aravindan Santhan, Kuo Yuan Hwa, Slava V. Rotkin et al. (+2 more)
The precise identification of neurotransmitters is essential for comprehending cerebral function, detecting neurological conditions, and formulating successful therapeutic approaches. The present work investigates the electrochemical detection of serotonin with the excellent hybrid electrocatalyst $Cu_2S/H{\beta}cd-rGO$. $Cu_2S$, with its significant features as improved catalytic activity and enhanced charge transfer when combined with $H{\beta}cd-rGO$, will enhance the performance. The integration of $Cu_2S$ with $H{\beta}cd-rGO$, regulated by the van der Waals force and the electrostatic interaction, makes it a stable catalyst without disrupting the composite structure. Also, the aggregation of the $Cu_2S/H{\beta}cd$ with the layered sheets of rGO can be highly reduced and resulting in the improvement of the conductivity. Thus, the above features resulted in the improved oxidation response current when fabricated over the glassy carbon electrode (GCE). The SR showed sensitive response at a broad linear range of 0.019 to 0.299 $\mu$M and 4.28 to 403.14 $\mu$M, resulting in a lower limit of detection (LOD) of 1.2 nM or 0.0012 $\mu$M and a sensitivity of about 15.9 $\mu$A ${\mu}M^{-1}$ $cm^{-2}$. The sensor demonstrated excellent selectivity against common interferents, including aminophenol, dopamine, epinephrine, hydroquinone, melatonin, and chlorine. The real sample studies in the biological samples show good recovery values, showing the effectiveness of the as-fabricated sensor. Thus, the cost-efficient and straightforward integration of $Cu_2S/H{\beta}cd-rGO$ will be an outstanding electrocatalyst for detecting SR.
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All authors Aravindan Santhan, Kuo Yuan Hwa, Slava V. Rotkin, Cheng-Han Wang, Chun-Wei Ou
Computational Physics
Scalable Domain-decomposed Monte Carlo Neutral Transport for Nuclear Fusion
Oskar Lappi, Huw Leggate, Yannick Marandet et al. (+3 more)
EIRENE [1] is a Monte Carlo neutral transport solver heavily used in the fusion community. EIRENE does not implement domain decomposition, making it impossible to use for simulations where the grid data does not fit on one compute node (see e.g. [2]). This paper presents a domain-decomposed Monte Carlo (DDMC)
algorithm implemented in a new open source Monte Carlo code, Eiron. Two parallel
algorithms currently used in EIRENE are also implemented in Eiron, and the three
algorithms are compared by running strong scaling tests, with DDMC performing better than the other two
algorithms in nearly all cases. On the supercomputer Mahti [3], DDMC strong scaling is superlinear for grids that do not fit into an L3 cache slice (4 MiB). The DDMC
algorithm is also scaled up to 16384 cores in weak scaling tests, with a weak scaling efficiency of 45% in a high-collisional (heavier compute load) case, and 26% in a low-collisional (lighter compute load) case. We conclude that implementing this domain decomposition
algorithm in EIRENE would improve performance and enable simulations that are currently impossible due to
memory constraints.
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All authors Oskar Lappi, Huw Leggate, Yannick Marandet, Jan Åström, Keijo Heljanko, Dmitriy V. Borodin
Computational Physics
Unveiling the Adsorption and Electronic Interactions of Drugs on 2D Graphsene: Insights from DFT and Machine Learning Approach
Chaithanya Purushottam Bhat, Pranav Suryawanshi, Aditya Guneja et al. (+1 more)
Efficient identification of promising drug candidates for nanomaterial-based delivery systems is essential for advancing next-generation therapeutics. In this work, we present a synergistic framework combining density functional theory (DFT) and machine learning (ML) to explore the adsorption behavior and electronic interactions of drugs on a novel 2D
graphene allotrope, termed Graphsene (GrS). Graphsene, characterized by its porous ring
topology and large surface area, offers an excellent platform for efficient adsorption and strong electronic coupling with drug molecules. A
dataset comprising 67 drugs adsorbed on various 2D substrates was employed to train the ML model, which was subsequently applied to predict suitable drug candidates for GrS based on molecular size and adsorption energy criteria (database link provided in a later section). The ML model exhibited robust predictive accuracy, achieving a mean absolute error of 0.075 eV upon DFT validation, though its sensitivity to initialization highlighted the need for larger and more diverse
datasets. DFT-based analyses, including adsorption energetics, projected density of states (PDOS), and Bader charge calculations, revealed pronounced charge transfer and electronic coupling between the drug molecules and the GrS surface, elucidating the fundamental nature of drug-substrate interactions. The study reveals that the integrated DFT-ML strategy offers a rapid, cost-efficient approach for screening and understanding drug-nanomaterial interactions, paving the way for data-driven design of advanced nanomaterial-enabled drug delivery systems.
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All authors Chaithanya Purushottam Bhat, Pranav Suryawanshi, Aditya Guneja, Debashis Bandyopadhyay
Data Analysis and Statistics · Cross-listed from cs.LG
Towards Causal Market Simulators
Dennis Thumm and Luis Ontaneda Mijares
Market generators using deep generative models have shown promise for synthetic financial data generation, but existing approaches lack causal
reasoning capabilities essential for counterfactual analysis and risk assessment. We propose a Time-series Neural Causal Model VAE (TNCM-VAE) that combines variational autoencoders with structural causal models to generate counterfactual financial time series while preserving both temporal dependencies and causal relationships. Our approach enforces causal constraints through directed acyclic graphs in the decoder architecture and employs the causal Wasserstein distance for training. We validate our method on synthetic autoregressive models inspired by the Ornstein-Uhlenbeck process, demonstrating superior performance in counterfactual probability estimation with L1 distances as low as 0.03-0.10 compared to ground truth. The model enables financial stress testing, scenario analysis, and enhanced backtesting by generating plausible counterfactual
market trajectories that respect underlying causal mechanisms.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks
Yujie Liu, Zhenyu Wang, Hang Lei et al. (+6 more)
Inorganic
crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for predicting materials' electrical conductivity, thermal conductivity and mechanical properties. Traditional experimental measurement suffers from high cost and low efficiency, while theoretical simulation and graph neural network-based machine learning methods–especially
crystal graph convolutional neural networks (CGCNNs)–have become effective alternatives, achieving remarkable results in predicting material elastic properties. This study trained two CGCNN models using shear modulus and bulk modulus data of 10987 materials from the Matbench v0.1
dataset, which exhibit high accuracy (mean absolute error <13, coefficient of determination R-squared close to 1) and good generalization ability. Materials were screened to retain those with band gaps between 0.1-3.0 eV and exclude radioactive element-containing compounds. The final predicted
dataset comprises two parts: 54359
crystal structures from the Materials Project database and 26305
crystal structures discovered by Merchant et al. (2023 Nature 624 80). Ultimately, this study completed the prediction of shear modulus and bulk modulus for 80664 inorganic
crystals. This work enriches existing material elastic data resources and provides robust support for material design, with all data openly available at https://doi.org/10.57760/sciencedb.j00213.00104.
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All authors Yujie Liu, Zhenyu Wang, Hang Lei, Guoyu Zhang, Jiawei Xian, Zhibin Gao, Jun Sun, Haifeng Song, Xiangdong Ding
Data Analysis and Statistics · Cross-listed from cond-mat.mtrl-sci
The Moving Beam Diffraction Geometry: the DIAD Application of a Diffraction Scanning-Probe
Alberto Leonardi, Andrew James, Christina Reinhard et al. (+6 more)
Understanding the interactions between microstructure, strain, phase, and material behavior is crucial in many scientific fields. However, quantifying these correlations is challenging, as it requires the use of multiple instruments and techniques, often separated by space and time. The Dual Imaging And Diffraction (DIAD) beamline at Diamond is designed to address this challenge. DIAD allows its users to visualize internal structures, identify compositional/phase changes, and measure strain. DIAD provides two independent beams combined at one sample position, allowing quasi-simultaneous X-ray Computed
Tomography and X-ray Powder Diffraction. A unique functionality of the DIAD configuration is the ability to perform image-guided diffraction, where the micron-sized diffraction beam is scanned over the complete area of the imaging field of view without moving the specimen. This moving beam diffraction geometry enables the study of fast-evolving and motion-susceptible processes and samples. Here, we discuss the novel moving beam diffraction geometry presenting the latest findings on the reliability of both geometry calibration and data reduction routines used. Our measures confirm diffraction is most sensitive to the moving geometry for the detector position downstream normal to the incident beam. The observed data confirm that the motion of the KB mirror coupled with a fixed aperture slit results in a rigid translation of the beam probe, without affecting the angle of the incident beam path to the sample. Our measures demonstrate a nearest-neighbour calibration can achieve the same accuracy as a self-calibrated geometry when the distance between calibrated and probed sample region is smaller or equal to the beam spot size. We show the absolute error of the moving beam diffraction geometry remains below 0.0001, which is the accuracy we observe for the beamline with stable beam operation.
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All authors Alberto Leonardi, Andrew James, Christina Reinhard, Michael Drakopoulos, Ben Williams, Hans Dehyle, Jacob Filik, Liam Perera, Sharif Ahmed
Physics Education
A quaternionic approach to teaching 3D rotations and the resolution of gimbal lock
Fernando Ricardo González Díaz, Vicent Martinez Badenes, Teodoro Rivera Montalvo et al. (+1 more)
Quaternions provide a unified algebraic and
geometric framework for representing three-dimensional rotations without the singularities that afflict Euler-angle parametrisations. This article develops a pedagogical and conceptual analysis of the Gimbal lock phenomenon and demonstrates, step by step, how quaternion
algebra resolves it. Beginning with the limitations of Euler representations, the work introduces the quaternionic rotation operator $v' = q\,v\,q^{*}$, derives the Rodrigues formula, and establishes the continuous, singularity-free mapping between unit quaternions and the rotation group $SO(3)$. The approach combines historical motivation, formal derivation, and illustrative examples designed for advanced undergraduate and graduate students. As an extension, Appendix~A presents the
geometric and topological interpretations of quaternions, including their relation to the groups $\mathbb{Q}_8$ and $SU(2)$, and the Dirac belt trick, offering a visual analogy that reinforces the connection between
algebra and spatial rotation. Overall, this work highlights the educational value of quaternions as a coherent and elegant framework for understanding rotational dynamics in physics.
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All authors Fernando Ricardo González Díaz, Vicent Martinez Badenes, Teodoro Rivera Montalvo, Ricardo García-Salcedo
Optics
Compact and high-resolution spectrometer via Brillouin integrated circuits
Jia-Qi Wang, Yuan-Hao Yang, Zheng-Xu Zhu et al. (+12 more)
Optical spectrometers are indispensable tools across various fields, from chemical and biological sensing to astronomical observations and
quantum technologies. However, the integration of spectrometers onto photonic chips has been hindered by the low spectral resolution or large device footprint with complex multiple channel operations. Here, we introduce a novel chip-integrated spectrometer by leveraging the acoustically-stimulated Brillouin scattering in a hybrid photonic-phononic chip. The Brillouin interaction provides a dynamic reflection grating with a high reflectivity up to 50% and a fast switching time on the microsecond scale, achieving an unprecedented spectral resolution of 0.56 nm over a 110 nm bandwidth using just a single 1 mm-long straight waveguide. This remarkable performance approaches the fundamental limit of resolution for a given device size, validating the potential of the hybrid photonic-phononic device for efficient and dynamically-reconfigurable spectral analysis, and thus opens up new avenues for advanced optical signal processing and sensing applications.
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All authors Jia-Qi Wang, Yuan-Hao Yang, Zheng-Xu Zhu, Juan-Juan Lu, Ming Li, Xiaoxuan Pan, Chuanlong Ma, Lintao Xiao, Bo Zhang, Weiting Wang, Chun-Hua Dong, Xin-Biao Xu, Guang-Can Guo, Luyan Sun, Chang-Ling Zou
Instrumentation and Detectors
First order statistic of afterpulsing and crosstalk events in SiPMs
Sergey Vinogradov
This paper briefly presents an order statistic approach to the time distribution of the first detected event after a primary avalanche breakdown from a mixture of correlated and dark counting processes. The well-known order statistic method, commonly used to describe the time resolution of scintillation detectors, is applied to the arrival times of correlated events. The established model of crosstalk as a branching Poisson process is extended to afterpulsing, and correlated events are considered starting from their seeds – free (de-trapped or diffused) charge carriers capable of triggering secondary avalanche breakdowns. The proposed approach enables the extraction of timing information for delayed crosstalk and afterpulsing events mixed with dark counts and predicts that the distribution of the first arrival time narrows as the number of seeds increases, corresponding to a higher probability of correlated events.
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