Computational Physics
Addressing the ground state of the deuteron by physics-informed neural networks
Lorenzo Brevi, Antonio Mandarino, Carlo Barbieri et al. (+1 more)
Machine learning techniques have proven to be effective in addressing the structure of atomic
nuclei. Physics$-$Informed Neural Networks (PINNs) are a promising machine learning technique suitable for solving integro-differential problems such as the many-body Schrödinger problem. So far, there has been no demonstration of extracting
nuclear eigenstates using such method. Here, we tackle realistic nucleon-nucleon interaction in momentum space, including models with strong high-momentum correlations, and demonstrate highly accurate results for the deuteron. We further provide additional
benchmarks in coordinate space. We introduce an expression for the variational energy that enters the loss function, which can be evaluated efficiently within the PINNs framework. Results are in excellent agreement with proven numerical methods, with a relative error between the value of the predicted binding energy by the PINN and the numerical
benchmark of the order of $10^{-6}$. Our approach paves the way for the exploitation of PINNs to solve more complex atomic
nuclei.
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All authors Lorenzo Brevi, Antonio Mandarino, Carlo Barbieri, Enrico Prati
Computational Physics
Stochastic Point Kinetics Model of Circulating-Fuel Reactors under Perfect Mixing Approximation
Lubomír Bureš and Valeria Raffuzzi
We present a
stochastic framework for low-population dynamics in circulating-fuel reactors (CFRs) that captures delayed-
neutron precursor (DNP) transport without delay terms. Starting from a modified point-kinetics model with two perfectly-mixed volumes, we derive equivalent discrete-event dynamics and an Itô
stochastic differential equation (SDE) system. Two solvers are implemented: an analog Monte Carlo (AMC) engine and a semi-implicit Milstein SDE solver. Transient
benchmarks demonstrate perfect agreement of AMC/SDE means with deterministic solutions, while revealing that the SDE approach underestimates DNP variances in selected regimes, potentially due to the neglect of DNP noise. We further recast reactivity loss due to precursor drift in this
stochastic setting and show that its
estimator is negatively biased. Overall, the developed framework provides a minimal yet representative model for CFR low-population kinetics. Future work will re-derive and test SDE noise terms and apply the framework to selected transient applications such as start-up analyses of CFRs.
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Physics and Society
URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres
Haining Ding, Chenxi Wang, Simon Ladouce et al. (+1 more)
Cycling is reported by an average of 35% of adults at least once per week across 28 countries, and as vulnerable road users directly exposed to their surroundings, cyclists experience the street at an intensity unmatched by other modes. Yet the street-level features that shape this experience remain under-analysed, particularly in historical urban contexts where spatial constraints rule out large-scale infrastructural change and where typological context is often overlooked. This study develops a perception-led, typology-based, and data-integrated framework that explicitly models street typologies and their sub-classifications to evaluate how visual and spatial configurations shape cycling experience. Drawing on the Cambridge Cycling Experience
Video Dataset (CCEVD), a first-person and handlebar-mounted corpus developed in this study, we extract fine-grained streetscape indicators with computer
vision and pair them with built-environment variables and subjective ratings from a Balanced Incomplete Block Design (BIBD) survey, thereby constructing a typology-sensitive Bikeability Index that integrates subjective and perceived dimensions with physical metrics for segment-level comparison. Statistical analysis shows that perceived bikeability arises from cumulative, context-specific interactions among features. While greenness and openness consistently enhance comfort and pleasure, enclosure, imageability, and building continuity display threshold or divergent effects contingent on street type and subtype. AI-assisted visual redesigns further demonstrate that subtle, targeted changes can yield meaningful perceptual gains without large-scale structural interventions. The framework offers a transferable model for evaluating and improving cycling conditions in heritage cities through perceptually attuned, typology-aware design strategies.
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All authors Haining Ding, Chenxi Wang, Simon Ladouce, Michal Gath-Morad
Optics
Inverse Design of Three-Dimensional Microwave Cavities for Optimizing Electromagnetic Helicity
Emma Paterson, Jeremy Bourhill and Maxim Goryachev
We present a inverse-design framework framework for systematically engineering three-dimensional microwave cavity resonators that support modes with nonzero electromagnetic helicity. In contrast to heuristic approaches to cavity design, helicity maximisation is formulated as a boundary-shape optimisation problem, enabling systematic exploration of complex boundary-shape parameter spaces and the identification of high-helicity designs that are difficult to predict using heuristic design rules alone. We applied this framework to several cavity families composed of smooth, edge-free components, including globally twisted cavities with control-point-defined cross-sections realised in both linear and ring configurations, cavities defined by the intersection of orthogonal prisms, sphere-subtracted cylindrical cavities, and parametrised surface resonators. Two gradient-free optimisation strategies, a genetic
algorithm and
Bayesian optimisation, were independently employed to explore compact sets of design parameters for these geometries and to optimise a scaled-helicity figure of merit for the dominant helical mode, evaluated via finite-element eigenmode analysis. Robustness to manufacturing tolerances was quantified by applying Gaussian
geometric perturbations to the optimised cavities and evaluating statistical robustness metrics that penalise sensitivity to
geometric variation. The optimisation reveals clear physical design principles governing the generation of high electromagnetic helicity in three-dimensional microwave cavities.
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Physics and Society
A Distinct Communication Strategies Model of the Double Empathy Problem
Enrique Calderoli, Tiago Figueiredo, Maria Cristina Varriale et al. (+1 more)
The double empathy problem reframes social difficulties between autistic and non-autistic people as reciprocal rather than as deficits located exclusively within the autistic individual. While there has been ample discussion about communicative compatibility, interaction cost, and affective resonance in cross-neurotype interactions, no mechanistic model of how these processes interact has been proposed. Here we build a feedback-loop mathematical model that would theoretically reproduce the empathy degradation observed during communication in neurotypical-autistic pairs solely due to differences in communication preferences of each neurotype. Numerical simulations of dyadic interactions show the model, whose mechanism is based solely on channel preferences, can illustrate the breakdown of empathic bonding observed clinically. This mathematical model presents testable predictions regarding the verbal dimension of dyadic empathic bonding and represents a promising step towards a formal account of a broader framework of social interaction dynamics of neurodivergent individuals and possible mitigation strategies for the double empathy problem.
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All authors Enrique Calderoli, Tiago Figueiredo, Maria Cristina Varriale, Flávio Kapczinski
Physics and Society
Indirect Reciprocity with Environmental Feedback
Yishen Jiang, Xin Wang, Ming Wei et al. (+4 more)
Indirect reciprocity maintains cooperation in stranger societies by mapping individual behaviors onto reputation signals via social norms. Existing theoretical frameworks assume static environments with constant resources and fixed payoff structures. However, in real-world systems, individuals' strategic behaviors not only shape their reputation but also induce collective-level resource changes in ecological, economic, or other external environments, which in turn reshape the incentives governing future individual actions. To overcome this limitation, we establish a co-evolutionary framework that couples moral assessment, strategy updating, and environmental dynamics, allowing the payoff structure to dynamically adjust in response to the ecological consequences of collective actions. We find that this environmental feedback mechanism helps lower the threshold for the emergence of cooperation, enabling the system to spontaneously transition from a low-cooperation state to a stable high-cooperation regime, thereby reducing the dependence on specific initial conditions. Furthermore, while lenient norms demonstrate adaptability in static environments, norms with strict discrimination are shown to be crucial for curbing opportunism and maintaining evolutionary resilience in dynamic settings. Our results reveal the evolutionary dynamics of coupled systems involving reputation institutions and environmental constraints, offering a new theoretical perspective for understanding collective cooperation and social governance in complex environments.
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All authors Yishen Jiang, Xin Wang, Ming Wei, Wenqiang Zhu, Longzhao Liu, Hongwei Zheng, Shaoting Tang
Applied Physics
Ultrasensitive, universal single-ion nanodetector
Namita Narendra and Tillmann Kubis
In this paper, a carbon nanotube (CNT) based single-ion detector is proposed and its performance is evaluated with atomistic
quantum transport models. The sensor can detect any ion type without molecule-specific functionalization and allows for continuous real-time ion monitoring. A single ion temporarily changes the operating principle of the sensor's CNT field-effect transistor into a resonant tunneling diode. The concrete device example of this paper showed a source-drain current increase of 5 orders of magnitude induced by a single ion.
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Atomic Physics
Wafer-Scale Micro-Knife Sealed Vacuum Cells for Quantum Devices
Megan Lauree Kelleher, Konrad Ziegler, Jeremy Robin et al. (+10 more)
Advanced integration technologies greatly enhance the prospects and reliability of practical
quantum sensors, atomic clocks, and
quantum information technologies. The performance and proliferation of these devices at chip-scale is contingent upon developing low leak and low gas permeation
vacuum cells using wafer-scale techniques. Here we demonstrate both evacuated atomic beam
cells and atomic vapor
cells using plastic deformation micro-knife bonding of selectively etched fused silica wafers. The
cells are characterized using saturated absorption spectroscopy and fluorescence measurements. Vapor
cells are mechanically robust exhibiting sheer-force strength ($\sim 15$MPa), demonstrate long lifetimes ($> 1$ year), low residual gas pressures $ (\ll 10^{-3} \, \text{mbar}) $, and leak rates below fine-leak testing sensitivity ($\ll 2.8 \times 10^{-10} \frac{\text{mBar} \cdot \text{L}}{\text{s}}$). Micro-knife bonding greatly simplifies the fabrication process for complex chip scale atom-beam devices and atomic vapor
cells while identifying a path to future chip-scale cold atom devices, improved chip scale atomic clocks, and fieldable dissipation-dilution-limited optomechanics.
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All authors Megan Lauree Kelleher, Konrad Ziegler, Jeremy Robin, Lianxin Huang, Mitchel Button, Liam Mauck, Judith Olson, Peter Brewer, Danny Kim, John Kitching, Ruwan Senaratne, William R. McGehee, Travis M. Autry
Fluid Dynamics
Parametrization of subgrid scales in long-term simulations of the shallow-water equations using machine learning and convex limiting
Md Amran Hossan Mojamder, Zhihang Xu, Min Wang et al. (+1 more)
We present a method for parametrizing sub-grid processes in the Shallow Water equations. We define coarse variables and local spatial averages and use a feed-forward neural network to learn sub-grid fluxes. Our method results in a local parametrization that uses a four-point computational stencil, which has several advantages over globally coupled parametrizations. We demonstrate numerically that our method improves energy balance in long-term turbulent simulations and also accurately reproduces individual solutions. The long-term simulations refer to numerical studies where a fluid flow is simulated over a duration long enough to reach a statistical steady state. The neural network parametrization can be easily combined with flux limiting to reduce oscillations near shocks. More importantly, our method provides reliable parametrizations, even in dynamical regimes that are not included in the training data.
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All authors Md Amran Hossan Mojamder, Zhihang Xu, Min Wang, Ilya Timofeyev
Optics
Enhancing Imaging Depth and Sensitivity in Reflectance Mode Near Infrared Optical Imaging with Scatter Reducing Agents
Mannu Bardhan Paul, Kaiser Niknam and Mini Das
We investigate the role of scatter reducing
agents in a continuous wave (CW) near infrared (NIR)reflectance mode imaging setting. We use food-grade dye Tartrazine as a scatter reducing
agent to enhance depth sensitivity and weak-absorber detectability in CW diffuse reflectance measurements. We found that reflectance signal was enhanced when the dye was applied on chicken breast phantom. However, we saw reduced reflectance sensitivity when the dye was uniformly dissolved in intralipid phantom which is a commonly used for NIR imaging studies. This shows that the gradient of refractive index modulation created as the dye diffuses from the top layer allows increased reflectance signal sensitivity of optical
photons. However, when the scatter reduction is uniform throughout the phantom (like in intralipid phantom), the improved reflectance sensitivity was not observed. Our study points to significant redistribution of
photons with scatter modulation with Tartrazine dye. We show significant improvement in sensitivity to signals with reflectance imaging. To elucidate the underlying mechanism of dye induced scatter reduction in tissue, analytical
diffusion models and Monte Carlo simulations were employed. Modeling results show the impact of refractive index gradient created due to dye
diffusion in enhancing reflectance sensitivity. These findings demonstrate that dye induced scatter reduction provides a practical, low-complexity approach to improving depth sensitivity in CW diffuse reflectance measurements and extend the functional capabilities of CW-NIRS systems for deep-tissue sensing applications. Our preliminary studies shows up to five fold enhancement in signal sensitivity for signals between two and three cm depth.
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Atomic Physics
Fundamental Limits of Large Momentum Transfer in Optical Lattices
Ashkan Alibabaei, Patrik Mönkeberg, Florian Fitzek et al. (+5 more)
Large-momentum-transfer techniques are instrumental for the next generation of atom interferometers as they significantly improve their sensitivity. State-of-the-art implementations rely on elastic scattering processes from optical
lattices such as Bloch oscillations or sequential Bragg diffraction, but their performance is constrained by imperfect pulse efficiencies. Here we develop a Floquet-based theoretical framework that provides a unified description of elastic light-atom scattering across all relevant regimes. Within this formalism, we identify practical regimes that exhibit orders of magnitude reduced losses and improved phase accuracy compared to previous implementations. The model's validity is established through direct comparison with numerical solutions of the Schrödinger equation and through quantitative agreement with recent experimental
benchmark results. These findings delineate previously unexplored operating regimes for large momentum transfer beam splitters and open new perspectives for precision atom-interferometric measurements in fundamental physics,
gravity gradiometry or gravitational wave detection.
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All authors Ashkan Alibabaei, Patrik Mönkeberg, Florian Fitzek, Michael Werner, Alexandre Gauguet, Baptiste Allard, Klemens Hammerer, Naceur Gaaloul
Optics
Microscopy of Bioelectric Potentials using Electrochromism
Burhan Ahmed, Erica Liu, Lothar Maisenbacher et al. (+7 more)
Studying the electrical signals generated by living
cells is key to understanding numerous biological phenomena. Electrochromic optical recording (ECORE) uses the electrochromism exhibited by certain materials to noninvasively measure these signals in real time. In this work, we report on the development of ECORE based on a high-NA microscope objective. We demonstrate the recording of extracellular action potentials from cardiomyocytes with single-
cell resolution and a high sensitivity of 6.3 {\mu}V. Combining ECORE with microscopy simplifies the optical setup, allows for the simultaneous imaging of specimens, and makes ECORE accessible to a broader community of researchers, allowing for a better understanding of the biological processes that are integral to life.
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All authors Burhan Ahmed, Erica Liu, Lothar Maisenbacher, Pengwei Sun, Dana Griffith, Kenneth Nakasone, Ashwin Singh, Yuecheng Zhou, Bianxiao Cui, Holger Müller
Optics
Topology-optimized distributed 3d anisotropic Raman emission
Ian M. Hammond, Pengning Chao, Henry O. Everitt et al. (+4 more)
Topology optimization (TO) of 3D surface-enhanced Raman scattering (SERS) substrates faces challenges in managing field singularities and modeling orientation-averaged anisotropic molecules. We present 3D TO for manufacturable SERS substrates that maximize spatially averaged signals from randomly oriented, anisotropic molecules in both elastic and inelastic scattering. A new trace formulation provides a closed-form rotational average of anisotropic Raman tensors, which are not equivalent to isotropic molecules because of tensor nonlinearity. Optimized silver and Si3N4 devices show that lengthscale constraints are sufficient to suppress designs that rely on unphysical mathematical field divergences at sharp corners. Metallic designs deliver broadband enhancement and remain robust to typical Raman shifts, whereas dielectric designs yield narrower, quality-factor-limited gains that are inferior to metallic designs for quality factors below about 500. Our approach readily incorporates additional physics, such as a nonlinear damage model. Together, these results provide a practical route to improved manufacturable SERS substrates and extend naturally to other distributed-emitter design problems.
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All authors Ian M. Hammond, Pengning Chao, Henry O. Everitt, Rasmus E. Christiansen, Alan Edelman, Francesc Verdugo, Steven G. Johnson
Instrumentation and Detectors
Inter-detector differential fuzz testing for tamper detection in gamma spectrometers
Pei Yao Li, Jayson R. Vavrek and Sean Peisert
We extend physical differential fuzz testing as an anti-tamper method for radiation detectors [Vavrek et al., Science and Global Security 2025] to comparisons across multiple detector units. The method was previously introduced as a tamper detection method for authenticating a single radiation detector in
nuclear safeguards and treaty verification scenarios, and works by randomly sampling detector configuration parameters to produce a sequence of spectra that form a baseline signature of an untampered system. At a later date, after potential tampering, the same random sequence of parameters is used to generate another series of spectra that can be compared against the baseline. Anomalies in the series of comparisons indicate changes in detector behavior, which may be due to tampering. One limitation of this original method is that once the detector has `gone downrange' and may have been tampered with, the original baseline is fixed, and a new trusted baseline can never be established if tests at new parameters are required. In this work, we extend our anti-tamper fuzz testing concept to multiple detector units, such that the downrange detector can be compared against a trusted or `golden copy' detector, even despite normal inter-detector manufacturing variations. We show using three NaI detectors that this inter-detector differential fuzz testing can detect a representative attack, even when the tested and golden copy detectors are from different manufacturers and have different performances. Here, detecting tampering requires visualizing the comparison metric vs. the parameter values and not just the sample number; moreover this baseline is non-linear and may require anomaly detection methods more complex than a simple threshold. Overall, this extension to multiple detectors improves prospects for operationalizing the technique in real-world treaty verification and safeguards contexts.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Prototype-based Explainable Neural Networks with Channel-specific Reasoning for Geospatial Learning Tasks
Anushka Narayanan and Karianne J. Bergen
Explainable AI (XAI) is essential for understanding machine learning (ML) decision-making and ensuring model trustworthiness in scientific applications. Prototype-based XAI methods offer an intrinsically interpretable alternative to post-hoc approaches which often yield inconsistent explanations. Prototype-based XAI methods make predictions based on the similarity between inputs and learned prototypes that represent typical characteristics of target classes. However, existing prototype-based models are primarily designed for standard RGB image data and are not optimized for the distinct, variable-specific channels commonly found in geoscientific image and raster
datasets. In this study, we develop a prototype-based XAI approach tailored for multi-channel geospatial data, where each channel represents a distinct physical environmental variable or spectral channel. Our approach enables the model to identify separate, channel-specific prototypical characteristics sourced from multiple distinct training examples that inform how these features individually and in combination influence model prediction while achieving comparable performance to standard neural networks. We demonstrate this method through two geoscientific case studies: (1) classification of Madden Julian Oscillation phases using multi-variable
climate data and (2) land-use classification from multispectral satellite imagery. This approach produces both local (instance-level) and global (model-level) explanations for providing insights into feature-relevance across channels. By explicitly incorporating channel-prototypes into the prediction process, we discuss how this approach enhances the transparency and trustworthiness of ML models for geoscientific learning tasks.
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Plasma Physics
Self ordering to imposed ordering of dust – a continuous spatial phase transition experiment in MDPX
Siddharth Bachoti, Saikat Chakraborty Thakur, Rahul Banka et al. (+2 more)
Previous experiments conducted in the Magnetized Dusty
Plasma eXperiment (MDPX) revealed an intriguing phenomenon first referred to as imposed ordering. This occurs when micron-sized
dust particles become aligned with the geometry of a conducting mesh placed above the
dust (at a distance much larger than the
plasma Debye length or the ion-neutral or electron-neutral mean free paths) in the presence of a strong
magnetic field perpendicular to the mesh. In this work, results of a transition experiment are presented wherein starting from a classical two-dimensional Coulomb
crystal with hexagonal
symmetry in an unmagnetized
plasma $(B = 0\,T)$,
dust transitions to a state in which it flows along the geometry of a conducting mesh placed above it, mapping out the 4-fold
symmetry of the boundary condition. It is hypothesized that beyond a certain magnetization, elongated electric potential structures emanating from the mesh drive the
dust motion to reflect the mesh morphology, transitioning from a 6-fold self ordering to 4-fold imposed ordering. The various
dust phases are quantified and a critical value of
magnetic field is identified in the transition experiment indicating the onset of imposed ordering.
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All authors Siddharth Bachoti, Saikat Chakraborty Thakur, Rahul Banka, Cameron Royer, Edward Thomas
Chemical Physics
Predicting the hydrogen bond strength from water reorientation dynamics at short timescales
Frederik Zysk, Ana Vila Verde, Naveen K. Kaliannan et al. (+2 more)
Path-integral molecular dynamics simulations and electronic structure-based energy decomposition analysis (EDA) are employed to connect hydrogen bond (H-bond) strength, its asymmetry, and the total delocalization energy at the water/air interface to experimentally measurable observables, such as the reorientation dynamics and the sum-frequency generation (SFG) spectrum. Using SFG spectra for distinct layers at the water/air interface, we validate the accuracy of our simulations and report a red-shift from the interface to bulk and a strongly bonded water peak at around 3250 cm$^{-1}$ in the layer closest to bulk. The reorientation dynamics of water molecules slow down from the interface to bulk, which correlates with the SFG results. From our EDA based on absolutely localized molecular orbitals, we observe a strong decline in total delocalization energy from bulk to the interface, as well as a decline in the strength of the strongest donor and acceptor interactions. The asymmetry between the two strongest interactions similarly rises towards the interface, while the importance of interactions from the outer solvation shells is greatly diminished and is lower than previously reported. Finally, we find that the strength of the strongest H-bond donor/acceptor is best correlated with the local minimum of the autocorrelation function resembling the L2 band librational motions. Following that, we propose a simple yet quantitative relationship between H-bond strength and the short-time reorientation dynamics at the water/air interface that could potentially be extended to predict H-bond strength in other hydrophobic systems from experimentally obtainable observables.
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All authors Frederik Zysk, Ana Vila Verde, Naveen K. Kaliannan, Kristof Karhan, Thomas D. Kühne
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Defects, Corrugation and Temperature Govern Rarefied-Air Drag on Graphene Coatings
Samuel Cajahuaringa, Davide Bidoggia, Maria Peressi et al. (+1 more)
In rarefied atmospheric environments, where continuum fluid dynamics breaks down, aerodynamic drag is governed by gas-surface momentum exchange, making surface structure and chemistry key design knobs. Using molecular dynamics simulations, we show that coating the $\alpha$-Al2O3(0001) surface with
graphene markedly reduces the tangential momentum accommodation coefficient (TMAC) of N2, shifting scattering toward more specular reflection and thereby lowering drag; we further
benchmark this response against graphite. The reduction strengthens up to 900 K. While structural defects can increase TMAC via defect-induced corrugation and local atomic and electronic rearrangements,
graphene retains its performance at experimentally relevant defect densities.
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All authors Samuel Cajahuaringa, Davide Bidoggia, Maria Peressi, Antimo Marrazzo
Optics
A Novel Differential Pathlength Factor Model for Near-Infrared Diffuse Optical Imaging
Kaiser Niknam, Mannu Bardhan Paul and Mini Das
Near infrared diffuse optical imaging can be performed in reflectance and transmission mode and relies on physical models along with measurements to extract information on changes in chromophore concentration. Continuous-wave near-infrared diffuse optical imaging relies on accurate differential pathlength factors (DPFs) for quantitative chromophore estimation. Existing DPF definitions inherit formulation-dependent limitations that can introduce large errors in modified Beer–Lambert law analyses. These errors are significantly higher at smaller source-detector separations in a reflectance mode of measurement. This minimizes their applicability in situations where large area detection is used and also when signal depth is varying. Using Monte Carlo simulations, we derive two distance- and property-dependent DPF models one ideal and one experimentally practical and
benchmark them against standard formulations. The proposed models achieve errors below 10 percent across broad optical conditions, whereas conventional DPFs can exceed 100 percent error. The theoretical predictions are further validated using controlled phantom experiments, demonstrating improved quantitative accuracy in CW-NIR imaging.
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Fluid Dynamics
A Geometric Reinitialization for Conservative Level-Set Methods in the Context of Capillary Flows
Helene Papillon-Laroche, Amishga Alphonius, Magdalena Schreter-Fleischhacker et al. (+2 more)
Simulations of immiscible two-phase flows involving surface tension (ST) require a robust high-fidelity framework. State-of-the-art multi-phase models, such as the Conservative Level-Set (CLS) approach, use Eulerian representations of the fluids and their interface, and require reinitialization methods to ensure volume conservation and accurate ST force modeling. This work proposes a new
geometric reinitialization strategy for CLS formulations as an alternative to the typical PDE-based reinitialization methods. Building on the
geometric reinitialization developed in context of level-set frameworks, the proposed method is an extension to the CLS approach within an open-source finite element framework that allows for fully distributed and adaptively refined, two- and three-dimensional meshes. This extension enables application of the
geometric reinitialization method to large-scale three-dimensional problems with strongly deforming interfaces, including breakup. The method is quantitatively compared to two established reinitialization approaches: the PDE-based reinitialization proposed in the original CLS method and a simple projection-based approach. The assessment comprises three 3D application cases of increasing complexity: the capillary migration of a droplet, the rise of a bubble, and the Rayleigh-Plateau instability development in a capillary jet. The
geometric method leads to high-quality, spatially-converged results in good agreement with
benchmark and reference solutions. It robustly and accurately recovers expected interface
topology-based metrics (e.g., the volume, surface area and sphericity), and relevant flow quantities. In addition, the proposed method has only two parameters and is robust across parameter choices and cases.
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All authors Helene Papillon-Laroche, Amishga Alphonius, Magdalena Schreter-Fleischhacker, Jean-Philippe Harvey, Bruno Blais
Biological Physics
You ain't seen nothing, and yet: Future biochemical concentrations can be predicted with surprisingly high accuracy
Ketevan Danelia, Sean A. Ridout and Ilya Nemenman
Accurate sensing of chemical concentrations is essential for numerous biological processes. The accuracy of this sensing, for small numbers of molecules, is limited by shot noise. Corresponding theoretical limits on sensing precision, as a function of sensing duration, have been well-studied in the context of quasi-static and randomly fluctuating concentrations. However, during development and in many other cases, concentration profiles are not random but exhibit predictable spatiotemporal patterns. We propose that leveraging prior knowledge of these structured profiles can improve and accelerate concentration sensing by utilizing information from current molecular binding events to predict future concentrations. By framing the constrained sensing problem as
Bayesian inference over an allowed class of spatiotemporal profiles, we derive new theoretical limits on sensing accuracy. Our analysis reveals that maximum a posteriori (MAP) estimation can outperform the classical Berg-Purcell and maximum-likelihood (Poisson counting) limits, achieving a sensing precision of $\delta c/c = 1/\sqrt{a^2N}$, where $N$ is the number of binding events, and $a > 1$ in certain cases. Thus knowledge of the statistical structure of concentration profiles enhances sensing precision, providing a potential explanation for the rapid yet highly accurate
cell fate decisions observed during development.
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Applied Physics
Principles of Use of Tensile J-Curve Materials in Antagonistic Arrangements
Liuyang Cheng, Wonsik Eom, Qiong Wang et al. (+8 more)
Natural ligaments are soft connective tissues that must simultaneously provide high stretchability to enable dexterous flexibility and high stiffness to protect the musculoskeletal system. These two functions cannot be independently tuned in conventional engineering materials with linear or hyperelasticity. Ligaments achieve this balance through a highly nonlinear tensile response characterized by a J-shaped curve, featuring an extended "toe region" of low force up to intermediate strains followed by an inflection, called the "heel region" which marks the onset of nonlinear stiffening. Here, we present a framework for characterizing the defining features of J-curve behavior. Based on these features, we define measures for protectiveness and mobility to quantitatively describe the effective stiffness and the level of nonlinearity, thereby elucidating how the J-curve enables decoupled fine-tuning of flexibility and damage protection. A simplified mathematical model, supported by experimental validation, reveals the performance advantages of J-curve materials in antagonistic arrangements and highlights their unique design space compared with linear elastic systems. Furthermore, we develop synthetic J-curve materials capable of self-strain sensing via piezoresistive transduction, enabling their integration into practical devices. Collectively, these materials, models, and insights advance the understanding of nonlinear mechanical mechanisms in natural systems and provide a foundation for harnessing J-curve behavior in engineering applications such as bio-inspired
robots.
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All authors Liuyang Cheng, Wonsik Eom, Qiong Wang, Hyeongkeun Kim, Roberto Pineda Guzman, Jeongmin Kim, Montse Solis, Shreyas Malladi, Samuel Tsai, Mariana E. Kersh, Sameh H. Tawfick
Optics
On-chip electrically reconfigurable octave-bandwidth optical amplification from visible to near-infrared
Guanyu Han, Wenjun Deng, Yu Wang et al. (+8 more)
Achieving broadband on-chip optical amplification spanning the visible and near-infrared (NIR) can enable diverse
quantum sensing, metrology, and classical communication applications within a single unified device. However, conventional
semiconductor and ion-doped amplifiers suffer from limited gain bandwidths set by fixed energy levels, while optical parametric amplifiers (OPAs) operating continuously from the visible to the NIR have remained elusive due to dispersion-limited bandwidth and the high pump powers required in the visible or ultraviolet (UV). Here, we overcome these limitations by introducing an electrically reconfigurable OPA architecture on lithium niobate integrated photonics. By synergistically combining ultra-high effective $\chi^{(2)}$ nonlinearity ($\sim$7,000%/W-cm$^2$), high-order dispersion engineering, and local electro-thermal tuning of quasi-phase matching, our device achieves record gain spectral spanning more than an optical octave, from 770 to 1650 nm. This range covers key transitions of many photonic
quantum systems and all telecommunication bands. Moreover, our approach eliminates the need for high-power, wavelength-tunable visible or UV pumps, delivering a peak on-chip gain of 23.67 dB with a single 1060 nm pump at 90 mW average on-chip power. This work opens new avenues for multi-functional, reconfigurable photonics unifying the visible and infrared regimes, with broad implications for
quantum sensing and communications.
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All authors Guanyu Han, Wenjun Deng, Yu Wang, Ziyao Feng, Wei Wang, Meng Tian, Yu Liu, Souvik Biswas, Carlos A. Meriles, Andrea Alù, Qiushi Guo
Physics and Society
Fragmentation of a longitudinal population-scale social network: Decreasing structural social cohesion in the Netherlands
Eszter Bokányi, Yuliia Kazmina, Eelke M. Heemskerk et al. (+1 more)
Population-level dynamics of social cohesion and its underlying mechanisms remain difficult to study. In this paper, we propose a network approach to measure the
evolution of social cohesion at the population scale and identify mechanisms driving the change. We use twelve annual snapshots (2010-2021) of a population-scale social network from the Netherlands linking all residents through family, household, work, school, and neighbor relations. Results show that over this period, social cohesion, quantified as average closure in the network, declines by more than 15%. We demonstrate that the decline is not due to changes in demographic composition, but to rewiring in individual ego networks. Statistical models confirm a decreasing overlap of social contexts and greater geographical mobility as drivers. Residential relocation, however, temporarily increases closure, suggesting that local cohesion-seeking behavior can yield global network fragmentation, with implications for policies related to housing, urban planning, and social integration.
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All authors Eszter Bokányi, Yuliia Kazmina, Eelke M. Heemskerk, Frank W. Takes
Biological Physics
Intelligent Control of Transportation Flow in Physarum Networks
Bingyang Han, Luolan Chen and Tieyan Si
The Physarum network expands or retracts in response to environmental stimuli, demonstrating an intelligent adaptive capability to locate optimal paths for nutrient transport. The underlying physical mechanism governing this intelligence behavior remains an unresolved problem in biological physics.unlike the unidirectional flow typical of urban traffic networks, cytoplasmic flow within the Physarum network exhibits periodic oscillations modulated by biological repellents and attractants. In this study, we investigate how local flows within the networks branch channels interact to collectively govern the global oscillatory dynamics.We find that the measured flow fluxes at intersection nodes obey Kirchhoff's current law. Phase differences exist among the flows in different branches.At the microscopic scale, flow distribution exhibits only brief periods of traffic congestion, which are resolved by the oscillatory flows. By mapping the flow flux vectors onto the
magnetic moment vector of
spin ice model, we demonstrate that the flow vectors strictly obey the ice-rule of vertex models in statistical physics.Notably, the three branches converging at a Y-shaped node never become blocked simultaneously, thereby preventing traffic congestion and ensuring efficient transmission of nutrients and signals.This intelligent flow control phenomenon offers novel insights for addressing traffic congestion and advances our understanding of frustrated
quantum magnetism.
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Chemical Physics
The two-positron gluic bond as a manifestation of "super" van der Waals interactions
Mohammad Goli, Dario Bressanini and Shant Shahbazian
Recently, it has been demonstrated theoretically that the interaction of two PsH atoms, each being a stable bound state of a hydrogen atom and a positronium atom, is attractive, leading to the formation of a molecular complex denoted as (PsH)2. However, the physical nature of this interaction has remained elusive. In the present study, we show that the stabilizing mechanism is entirely encoded in the
quantum correlations between the two positrons and, to a lesser extent, in the electron-positron correlations. Notably, the interaction cannot be recovered at the mean-field (Hartree-Fock) level, nor by computational models that include only electron-electron correlation effects. Accordingly, the bond formed between PsH units, termed here a two-positron gluic bond to emphasize its fundamentally distinct character from the two-positron covalent bonds present in pure antimatter molecules, emerges only when matter and antimatter particles form a common bound state. When classified within the framework of known bonding mechanisms, this gluic bond falls into the category of stabilizing dispersion interactions, giving rise to a van der Waals complex. However, its remarkably large bond dissociation energy, compared with those of strongly bonded van der Waals complexes of similar size, reveals an anomalously strong interaction. For this reason, we propose that (PsH)2 is most appropriately described as a "super" van der Waals complex stabilized by a "super" van der Waals bond.
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Plasma Physics
Time-Resolved Interferometric Measurements of Plasma Density Evolution in Laser-Driven Capacitor-Coil Targets
Yang Zhang, Ryo Omura, Rinya Akematsu et al. (+17 more)
Laser-driven capacitor-coil targets provide a compact platform for generating strong
magnetic fields and are widely used in magnetized high-energy-density
plasma experiments. In addition to
magnetic-field generation, these targets also produce
plasma in the coil region, which can influence the subject physical processes, interact with secondary targets or external
plasmas in their applications. However, direct, time-resolved measurements of the
plasma density surrounding the coil remain limited. Here, we report interferometric measurements of the
plasma density
evolution in
laser-driven capacitor-coil targets irradiated by the University of Osaka LFEX
laser. Two-dimensional electron density maps reveal two distinct
plasma sources loading the coil region:
plasma generated in the coil itself and
plasma produced by
laser ablation of the target plates. These results provide quantitative information on
plasma loading and
evolution in capacitor-coil targets and are directly relevant to the design and modeling of magnetized high-energy-density
plasma experiments.
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All authors Yang Zhang, Ryo Omura, Rinya Akematsu, King Fai Farley Law, Brandon K. Russell, Geoffrey Pomraning, Kian Orr, Kai Kimura, Muhammad Fauzan Syahbana, Yuga Karaki, Hiroki Matsubara, Ryuya Yamada, Jinyuan Dun, Ryunosuke Takizawa, Yasunobu Arikawa, Tatiana Pikuz, Yuji Fukuda, Lan Gao, Hantao Ji, Shinsuke Fujioka
Chemical Physics
Leveraging configuration interaction singles for qualitative descriptions of ground and excited states: state-averaging, linear-response, and spin-projection
Takashi Tsuchimochi and Benjamin Mokhtar
While configuration interaction singles (CIS) provides a computationally efficient description of excited states, it systematically overestimates excitation energies and performs poorly for strongly correlated systems, partly due to the lack of orbital relaxation and the strong ground-state bias of Hartree-Fock orbitals. To address these limitations, we present a unified variational framework that extends CIS by incorporating orbital optimization in state-specific and state-averaged forms (SSCIS and SACIS), linear-response orbital relaxation via a double-CIS scheme (DCIS), and
spin-
symmetry breaking and restoration (ECIS). In
spin-projected state-averaged formulations, standard multistate parametrizations are no longer valid because the projection operator breaks the
unitary invariance of orbital rotations and induces nonorthogonal couplings among states. By formulating a rigorous state-averaged objective in the projected subspace, we derive analytic electronic gradients and Hessians and enable robust optimization using a trust-region augmented Hessian
algorithm.
Benchmark calculations show that
spin projection alone significantly exacerbates the CIS overestimation in weakly correlated systems, whereas combining
spin projection with state averaging or double-CIS corrections substantially reduces errors, particularly for Rydberg excitations. We further demonstrate that state averaging and
spin projection provide complementary and essential benefits in strongly correlated regimes, as illustrated by the bond dissociation of hydrogen fluoride and nitrogen.
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Optics · Cross-listed from quant-ph
High-gain effects in broadband continuous-wave parametric down conversion sources and measurements with undetected photons
Martin Houde, Franz Roeder, Christine Silberhorn et al. (+2 more)
We study theoretically how high-gain effects affect the measurement outcome of visible signal spectra in undetected
photon measurement schemes. We consider two interferometric configurations: firstly, the SU(1,1) interferometer where the idler incurs loss and additional dispersion in between two identical, lossless, squeezers; secondly, the induced coherence interferometer where the idler incurs loss and additional dispersion in between two identical, lossless, squeezers and where the second squeezer is seeded by the idler and a
vacuum ancilla mode. Furthermore, we consider a distributed loss configuration where the idler incurs loss as it propagates in the nonlinear medium. Motivated by experimental evidence and due to the fact that broadband sources are ideal for these measurement schemes, we use the dispersive data of a third-order dispersion engineered integrated waveguide parametric down conversion (PDC) source presented in New Journal of Physics 26, 123025 (2024) to model the PDC spectra in the three configurations. For each configuration we consider the case of idler-only (i) absorption, (ii) additional dispersion, and (iii) the combined effects. We obtain results which outline the strength and weaknesses of the different configurations at different operation points.
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All authors Martin Houde, Franz Roeder, Christine Silberhorn, Benjamin Brecht, Nicolás Quesada