Biological Physics
Measurement-limited learning of conformational heterogeneity in cryo-electron microscopy
Henry H. Mattingly, Luke Evans and Pilar Cossio
Cryogenic electron microscopy images sample individual biomolecules from their conformational landscapes, offering a route to infer the distributions underlying molecular mechanisms. However, because images are indirect measurements, they limit which features of an underlying landscape are statistically identifiable. In ensemble reweighting, this problem appears as a choice of resolution: conformational space is discretized into representative structures whose population weights are inferred from images. Adding structures increases nominal resolution, but nearby conformations may generate overlapping image distributions and indistinguishable weights. Here, we develop an information-theoretic framework that selects representative conformations by maximizing mutual information between ensemble weights and images under a probabilistic forward model. Analytically, we show in a one-dimensional Gaussian model that measurement noise sets the optimal spacing. Applied to molecular conformations sampled from simulation, the framework constructs near-optimal ensembles that span heterogeneity while avoiding redundancy. Thus, the measurement process induces a maximally learnable coarse graining of conformation space.
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Space Physics · Cross-listed from astro-ph.SR
Realtime forecasting of solar energetic particle event and proton flux using multi-source solar observations and multi-task deep learning
Yian Yu, Yang Chen, Lulu Zhao et al. (+3 more)
Solar energetic particle (SEP) events, defined by
proton flux exceeding 10 pfu in the > 10 MeV channel, pose major risks to spacecraft operations, astronaut safety, and high-latitude aviation. Due to the complexity and rarity of SEP events, reliable operational SEP
forecasting remains an important challenge in space weather. Here we present a novel 24-hour-ahead realtime
forecasting framework, SEPNET-PRISM, based on a multi-task learning structure and a thoroughly constructed list of features from multiple sources spanning multiple
solar cycles, that jointly predicts SEP event occurrence and future
proton and soft X-ray fluxes. SEPNET-PRISM extends the earlier-introduced SEPNET-based models by integrating a broader range of
solar observations, including active-region
magnetic parameters from SHARP and SMARP,
solar-
flare information, coronal mass ejections, soft X-ray flux, and historical > 10 MeV
proton flux. As compared with SEPNET, the inclusion of SMARP data expands the temporal coverage of
magnetic-field predictors to earlier
solar cycles, while flux-based inputs provide additional precursor information. Evaluation on the CLEAR SEP
benchmark dataset shows improved classification performance over the earlier SEPNET-O (operational version of SEPNET) on the newly aligned
dataset. The best operational model is obtained when
magnetic, radiative, and
proton-flux predictors are combined, highlighting the value of expanded historical coverage and complementary precursor information for improving realtime SEP
forecasting.
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All authors Yian Yu, Yang Chen, Lulu Zhao, Kathryn Whitman, Ward Manchester, Tamas Gombosi
Plasma Physics
Plasma Instabilities in Arbitrary Distributions: Comparison between ALPS and BO
Xudong Guo, Huasheng Xie, Kristopher G. Klein et al. (+3 more)
Determining accurate wave dispersion relations is a central problem in
plasma physics. Recent advances have enabled the numerical computation of linear dispersion relation in
plasmas with arbitrary particle velocity distribution functions (VDFs), using two distinct solvers, BO and ALPS. Their reliability and mutual consistency, however, have not been systematically tested for a broad range of VDFs. Here we compare the dispersion relations obtained from BO and ALPS for several representative distributions. We find that the two solvers give consistent unstable modes for kappa distributions with large values of $\kappa$, as well as for ring-beam, shell, and
proton core-beam distributions. BO, however, becomes unreliable for kappa distributions with $\kappa < 4$. For an observationally derived VDF, the two solvers give similar real frequencies for the unstable waves but substantially different growth rates. This difference is mainly caused by the imperfect fitting of the input distribution required by BO. Despite this limitation, BO has a clear computational advantage because it can obtain all roots in a single run. Considering the complementary strengths of the two solvers, their combined use can provide a more reliable and effective framework for investigating instabilities in non-Maxwellian
plasma environments.
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All authors Xudong Guo, Huasheng Xie, Kristopher G. Klein, D. Verscharen, Chen Shi, Jinsong Zhao
Computational Physics
Measuring qualitative change: A variational score for tracking dynamical shifts in partial differential equations
Joseph J. Pollacco, Jonathan Wong, Navonil Neogi et al. (+2 more)
Partial differential equations (PDEs) regulate the behaviour of countless spatiotemporal systems in the physical and life sciences. In many cases, they encode the coupling between the system's degrees of freedom, leading to nonlinear equations whose solution space is challenging to explore exhaustively. Systematic approaches to PDE model exploration are a holy grail of computational science. In this article, we formulate a criterion for increasing the diversity of a search campaign, based on the PDE residual behaviour under solution deformation. We develop a practical formalism to compute this property and illustrate its role in a few cases of interest.
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All authors Joseph J. Pollacco, Jonathan Wong, Navonil Neogi, Callum Simpson, Eloisa Bentivegna
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Modifying Electrochemical Doping in Light-Emitting Electrochemical Cells with Gold Nanoparticles
Ajay K. Poonia, Anton Kirch, Joan Ràfols-Ribé et al. (+8 more)
Electrochemical doping offers dynamic control of the electronic properties of organic
semiconductors, and it is the enabling feature of a range of technologies, including electrochemical transistors, energy-storage devices, light-emitting electrochemical
cells (LECs), and bioelectronics. Electrochemical doping is commonly controlled by the selection of the constituents in the active material of the device or the applied voltage bias, but herein we report that the incorporation of Au nanoparticles (Au-NPs) at an electrode interface can constitute an alternative control parameter. The LEC features balanced p- and n-type electrochemical doping that forms a p-n junction doping structure in its active material, and we find that it is possible to reshape this doping profile by incorporating Au-NPs at an electrode interface. Specifically, we establish that the inclusion of neat non-capped Au-NPs at the anodic interface shifts the p-n junction (i.e., the emission zone) away from the anode. In contrast, the inclusion of Au-NPs capped with sodium citrate is found to reverse this behavior, so that the emission zone is instead moved towards the anode. We utilize this control parameter to shift the emission zone towards a position of constructive (destructive) interference, as manifested in a strong increase (decrease) of the LEC emission efficiency. Our findings establish an interfacial strategy for modulating the spatial profile of electrochemical doping and tuning device performance without altering the chemistry of the active material, relying instead on the surface modification of one electrode. This approach is important because it provides a versatile and minimally invasive route to optimize electrochemical devices while preserving the intrinsic properties and formulation of the active material.
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All authors Ajay K. Poonia, Anton Kirch, Joan Ràfols-Ribé, Lucrezia Catanzaro, Anish Rao, Karol Kołątaj, Vittorio Scardaci, Giuseppe Compagnini, Guillermo P. Acuna, Ludvig Edman, Nicolò Maccaferri
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
The Future of Computing for Materials Science Challenges
Phalgun Lolur, Richard P. Padbury, George H. Booth et al. (+10 more)
Materials discovery increasingly relies on the coordinated use of theory, computation, experiment, data-driven methods, and emerging
quantum technologies, yet the full potential of these tools is realised only when they operate within workflows that reflect the complexity of real systems. This perspective summarises current capabilities, limitations, and opportunities across these domains, drawing on contributions from academia, industry, and national laboratories to identify the scientific and structural requirements for more reliable and efficient discovery. Classical simulations provide broad coverage across design spaces, while experimental measurements reveal degradation, heterogeneity, and kinetic processes that determine performance under realistic conditions. Machine learning accelerates exploration when supported by well-curated
datasets with clear provenance and uncertainty quantification, and
quantum computing offers promising routes into correlated electronic behaviour when aligned with properties that influence engineering decisions. Collectively, these insights highlight the need for reproducible workflows, shared data standards, realistic
benchmarks, and a research culture that prepares scientists to work across paradigms. By integrating these methodological and organisational elements, the community can move toward discovery processes that deliver robust predictions, support confident decision making, and shorten the path from conceptual design to deployable materials.
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All authors Phalgun Lolur, Richard P. Padbury, George H. Booth, Katherine Inzani, Nicole Holzmann, Thomas W. Keal, Joseph Montaya, Daniel F. Urban, Thomas Eckl, Emanuele Marsili, Wibe A. de Jong, Jonathan R. Owens, Julian van Velzen
Data Analysis and Statistics · Cross-listed from hep-ex
Machine-learned particle flow as a foundation model for collider physics
Farouk Mokhtar, Joosep Pata, Michael Kagan et al. (+1 more)
The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks. We show that casting event reconstruction as a machine learning problem naturally produces such a shared representation. We repurpose a machine learning model trained for particle-flow reconstruction (MLPF) to perform three distinct analysis tasks: jet
flavor identification, jet energy regression, and missing momentum regression. By appending the per-particle latent representations learned during reconstruction as additional input features, we substantially improve over baselines that use kinematic features alone. We further demonstrate that a single linear layer trained using only the latent representations achieves competitive performance against state-of-the-art baseline architectures, and outperforms the baseline for missing momentum regression with approximately 35 times fewer parameters. These results demonstrate that the latent representations learned during reconstruction encode essential physics information needed for downstream analysis, establishing MLPF as a foundation model and offering a concrete step toward an end-to-end pipeline from detector data to physics analysis.
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All authors Farouk Mokhtar, Joosep Pata, Michael Kagan, Javier Duarte
Instrumentation and Detectors · Cross-listed from quant-ph
Certification of the genuine resolution of photon number resolving detectors
Jef Pauwels, Towsif Taher, Roope Uola et al. (+3 more)
Photon-number-resolving (PNR) detectors are essential components of photonic
quantum technologies, yet thus far, no practical metric exists to certify how many
photons they can genuinely resolve in a single measurement. Here we introduce an operational framework for quantifying the capability of a PNR detector to distinguish between different numbers of
photons, i.e. its genuine resolution. In turn, we develop a practical and scalable protocol for certifying the genuine resolution of a detector, which is based on coherent state probes. We apply the method to a 28-pixel
photon-number-resolving superconducting nanowire single-
photon detector (PNR-SNSPD) and certify genuine four-outcome resolution. Our work highlights the critical requirements in terms of detector efficiency towards achieving high genuine resolution. This approach provides an operational
benchmark for PNR detectors and fills a crucial gap in the characterization of photonic
quantum devices.
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All authors Jef Pauwels, Towsif Taher, Roope Uola, Boris Korzh, Nicolas Brunner, Pavel Sekatski
Chemical Physics · Cross-listed from quant-ph
Quantum-Classical Hierarchical Equations of Motion
Amartya Bose
We develop a
quantum-classical hierarchical equations of motion (QC-HEOM) approach for simulating non-Markovian open
quantum systems. The method combines the ensemble-averaged classical path reference of the
quantum-classical path integral formalism with a hierarchy of auxiliary
quantum influence functionals. By incorporating thermal fluctuations through an ensemble average over reference trajectories, the hierarchy is required to represent only the residual
quantum memory associated with the imaginary part of the bath response function. Consequently, unlike conventional hierarchical equations of motion, QC-HEOM does not require Matsubara or Padé expansions of the thermal kernel and exhibits only weak temperature dependence of the hierarchy size. Furthermore, because thermal fluctuations are supplied through reference classical trajectories, the framework naturally extends beyond harmonic baths and enables the incorporation of anharmonic and molecular environments through externally generated trajectories. We derive the formalism and demonstrate its exactness for a harmonic bath. Applications to an asymmetric
spin-
boson model and the seven-site Fenna–Matthews–Olson complex illustrate the accuracy of QC-HEOM. It reproduces
benchmark quasi-adiabatic path integral and hierarchical equations of motion results while requiring substantially fewer auxiliary objects, particularly at low temperatures. These results establish QC-HEOM as an efficient framework for treating residual
quantum memory in
quantum-classical descriptions of open-system dynamics. The separation of thermal fluctuations from residual
quantum memory through the use of Wigner trajectories provides an approximate route toward hierarchical treatments of complex anharmonic environments that are inaccessible to conventional HEOM approaches.
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Physics and Society
Detecting Historical Turning Points in Italian Media: A Complex Systems Approach to a Diachronic News Corpus
Dario Zarcone, Salvatore Miccichè and David Sanchez
The increasing availability of large-scale textual corpora has opened new possibilities for data-driven, quantitative approaches to historical analysis using Natural
Language Processing (NLP). However, diachronic corpora with historical relevance from the pre-digital era remain scarce and often incomplete. We present a quantitative approach to historical analysis based on the reconstruction and exploration of a diachronic corpus of around 600,000 articles from the Italian newspaper "La Repubblica", covering all the articles published from the 1st of January 1985 to the 31st of December 2000 - a period of major political, social, and geopolitical change in Italy and globally. Using NLP techniques, we analyze the text at both lexical and semantic levels; we then apply tools from complex systems and statistical physics to trace shifts in media discourse over time. This allows us to detect key transition periods, such as the transition from the First Republic to the Second Republic in Italy, or major international conflicts like the Gulf War or the Kosovo War, without relying on prior labeling. The results show how combining computational linguistics with ideas from complex systems can offer new quantitative insight into historical changes, opening up new paths for studying the dynamics of media and society through large-scale textual data.
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Optics
Chirally-sensitive optical rectification by isotropic chiral media
Raju Adhikary, Luca Assogna, Ambaresh Sahoo et al. (+8 more)
Chiroptical sensing is central to gain fundamental insight into electronic, vibrational and rotational degrees of freedom of
chiral molecules, and is a cornerstone for nanomedicine and drug discovery platforms. Current
chiral sensing technologies to assess the enantiomeric imbalance of
chiral pharmaceutical compounds are sensitive to ml volumes but are time-consuming and cannot be integrated on a chip, thus creating a major bottleneck for drug discovery and nanomedicine. Here, we propose a novel chiroptical sensing approach based on optical rectification in a photonic micro-cavity filled by a drug solution with nl volume. We theoretically demonstrate that, upon optical excitation by intense pulsed
laser light, such a nonlinear effect produces a chirally-sensitive nV voltage burst at the electrically-gated micro-cavity boundaries, with sign depending solely on the drug enantiomeric imbalance. Our results shed light on the potential of optical rectification as a robust platform for innovative lab-on-a-chip devices enabling
chiral sensing with nl sensitivity.
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All authors Raju Adhikary, Luca Assogna, Ambaresh Sahoo, Matteo Venturi, Andrea De Marcellis, Massimiliano Aschi, Antonio Mecozzi, Jens Biegert, Davide Tedeschi, Carino Ferrante, Andrea Marini
Fluid Dynamics
Flow behind the Imperial Front Wing: comparison of results from volumetric PTV experiment and Nektar++ simulations
Isabella Fumarola, Alexandra I. Liosi, Parv Khurana et al. (+4 more)
High-fidelity simulations are increasingly adopted, due to advances in computational power and methods such as Direct Numerical Simulation (DNS) and hybrid Large-Eddy Simulation (LES). These approaches are particularly valuable for unsteady flows around complex geometries at high Reynolds numbers; however they still require careful experimental validation. Planar and stereo Particle Image Velocimetry (PIV) are widely used for measurements but limited by measurement-plane selection and their ability to capture
vortices shapes and trajectories. This motivates the growing interest in volumetric techniques, historically difficult to implement in industrial settings. Recent advances in Particle Tracking Velocimetry (PTV) for measuring flows over large volumes make this approach suitable for validating numerical simulations of complex flows.This study compares volumetric PTV measurements against high-fidelity LES to assess the capabilities and limitations for industrial flows. The aim is to establish a
benchmark PTV
dataset for motorsport aerodynamics using the Shake-The-Box
algorithm. The experiment was carried out in the 10x5 wind tunnel at Imperial College London equipped with a rolling road for ground effect simulation and capable of testing up to 50% scale F1 model. Volumetric PTV measurements were performed downstream of the open-source Imperial Front Wing (IFW) at Re=74896. Results are compared with planar PIV studies and implicit LES simulation using spectral h/p elements in Nektar++. This work addresses open questions in the literature concerning the wake of the IFW. Good quantitative agreement is observed in the wake
topology. A previously unreported
vortex is identified which has the key role of preventing the merging of other dominant structures. These results demonstrate the suitability of PTV and STB for industrial applications while providing a
benchmark dataset for the IFW.
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All authors Isabella Fumarola, Alexandra I. Liosi, Parv Khurana, Adam Meziane, Isaac Balbolia, Sherwin J. Spencer, Jonathan F. Morrison
Physics and Society
Wealth Inequality and Planetary Boundaries in a Stylized Agent-Based Model
Thomas Valade, Michael Benzaquen, Matthieu Cristelli et al. (+2 more)
At the intersection of rising wealth inequality and intensifying environmental pressures, we investigate a reverse causal relationship that has received comparatively little
attention: wealth inequality may not only be a consequence of environmental crises, but also act as a structural obstacle to the ecological transition itself. We develop a stylized
agent-based model in which heterogeneous
agents, whose initial wealth follows a Pareto distribution, allocate their income between either a Brown or a Green sector through a utility function. The function is designed to capture the trade-off between short-term returns and exposure to long-term systemic risks. A central ingredient is that wealthier
agents perceive themselves as less vulnerable to environmental shocks, thereby reducing the amount of resources available for the transition. We show that, beyond inequality thresholds compatible with those observed in most developed countries, the economy remains locked in a Brown regime, even when a substantial share of
agents is sensitive to externalities. We then assess a set of stylized fiscal policies (basic income, carbon taxation, Green incentives, and a combined scheme) and find that their effectiveness depends strongly on the inequality regime and on the regressivity embedded in the fiscal mechanism, revealing multidimensional trade-offs between transition speed, cumulative environmental destruction, growth, and fiscal pressure.
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All authors Thomas Valade, Michael Benzaquen, Matthieu Cristelli, Stanislao Gualdi, Pierre Lenders
Chemical Physics
Thermal feedback as a kinetic control mechanism in reaction-diffusion pattern formation
Sudip Dutta and Pushpita Ghosh
Pattern formation in reaction-
diffusion systems is traditionally analyzed under isothermal assumptions, overlooking the dynamical role of temperature in systems where reactions generate and dissipate heat. Here, we investigate non-isothermal reaction-
diffusion dynamics by coupling activator-inhibitor kinetics to a dynamically evolving temperature field that modulates reaction rates through Arrhenius-type dependencies. This coupling introduces an additional feedback mechanism that influences stability and pattern selection. Through analytical and numerical analysis of the Cholrine dioxide-Iodine-Malonic acid (CDIMA) and Schnakenberg models, we demonstrate that thermal feedback modifies dispersion relations by enhancing instability growth rates and shifting pattern selection toward shorter wavelengths. Beyond these intrinsic effects, we identify a boundary-mediated mechanism in which thermal constraints qualitatively alter global dynamics. In particular, fixed-temperature boundaries induce nonstationary behavior in the CDIMA system, whereas the Schnakenberg model exhibits robust stationary patterns. These results establish thermal-kinetic coupling as a general mechanism for controlling pattern formation and highlight the role of boundary-mediated heat exchange as a tunable parameter for spatiotemporal organization.
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Computational Physics · Cross-listed from math.NA
Mathematical Modeling of Salt Precipitation and Multi-Phase Flow in High Enthalpy Fractured Geothermal Systems
Micheal B. Oguntola, Omar Duran, Eirik Keilegavlen et al. (+1 more)
Simulating high-enthalpy fractured geothermal reservoirs is challenging due to the complex coupled processes of non-isothermal, multiphase, multicomponent flow, strongly nonlinear thermodynamics, and the dominant role of fractures. These complexities are amplified by mineral scaling, such as halite precipitation, which can impair reservoir permeability and well productivity. To address this, we present a new compositional flow model based on a persistent set of primary variables (pressure, enthalpy, and overall salt mass fraction). The formulation naturally handles phase transitions without manual switching, enhancing numerical stability. The model integrates a discrete fracture-
matrix approach and employs an efficient, robust correlation-based phase-behaviour linearisation of saltwater thermodynamics, replacing expensive on-the-fly phase separation calculations. It incorporates the Kozeny-Carman relation to dynamically model porosity and permeability reduction from halite precipitation. Implemented in the open-source PorePy framework, the model is verified through a 1D salt dissolution
benchmark against the established closed-source simulator CSMP++, showing strong agreement across geothermal conditions involving transitions between single- and multi-phase regions. Application to a 2D halite-saturated fractured reservoir with injection and production demonstrates the model's capability to predict halite precipitation patterns and their impact on permeability damage and energy recovery. Numerical results further show the model's value in predicting operational challenges such as wellbore blockage and the role of fracture connectivity. The model thus provides an open-source numerical tool for analysing complex heat and mass transport with mineral scaling in high-enthalpy fractured geothermal systems.
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All authors Micheal B. Oguntola, Omar Duran, Eirik Keilegavlen, Inga Berre
Medical Physics
iGLU 5.0: A Novel, Non-invasive and Intelligent HbA1c Measurement Device using Glucose values and Physiological Parameter for Smart Healthcare
Prateek Jain, Amit M. Joshi and Saraju P. Mohanty
The laboratory test process of HbA1c measurement is a time-consuming and invasive method. The HbA1c parameter is the most important feature to predict the level of diabetes. Although invasive methods are irritating in the case of frequent measurements. Moreover, HbA1c measurement is only possible at the diagnostic centre, followed by medical protocol. Hence, it is still challenging to measure the HbA1c frequently at remote locations, where diagnostic centres are not easily available. Therefore, an intelligent and new non-invasive HbA1c measurement system, iGLU 5.0, is proposed for instant diagnosis of the HbA1c value without prior measurement setup. The proposed measurement device is based on optical spectroscopy for the collection of glucose values in different formats. The glucose values have been collected in fasting, postprandial, and random formats. The glucose value has also been collected using the oral glucose tolerance test (OGTT), along with the average blood pressure value, correspondingly. These four formats of glucose values, along with blood pressure, were used to predict the estimated average glucose (eAG) using an optimized prediction model. Further, the predicted average glucose is converted into an HbA1c value using a standard formula. The eAG prediction models have been trained and validated using 2000 samples of healthy, prediabetic and diabetic people to analyze the optimized prediction model. 94% and 96% accuracy have been examined during training and cross-validation of optimized DNN model, respectively. A 0.3 mean absolute difference has been identified from predicted HbA1c values using the proposed DNN model. The novel non-invasive HbA1c prediction system is useful for instant diagnosis without irritation for smart healthcare.
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Plasma Physics
PIConGPU modeling of nanoplasma formation in helium nanodroplets irradiated by intense femtosecond laser pulses
Cristian Medina, Klaus Steiniger, Brian Edward Marre et al. (+14 more)
Helium nanodroplets provide a unique and versatile platform for investigating strong-field-driven nanoplasma dynamics. In this work, we present large-scale, GPU-accelerated particle-in-
cell simulations using PIConGPU to study the interaction of pure helium nanodroplets containing up to $10^{6}$ atoms with intense near-infrared femtosecond
laser pulses, and compare the results with single-shot velocity-map electron imaging and ion measurements. The simulations describe the
plasma evolution from the first ionization events to collective electron motion, nanoplasma formation, and early expansion. We show that the calculated electron and ion observables reproduce the main features of the measured spectra in systems with similar cluster sizes and
laser intensities. Our results demonstrate that PIConGPU captures the essential physics of nanoplasma formation previously addressed mainly with molecular-dynamics or TDDFT approaches, while remaining computationally efficient and applicable to much larger systems. This establishes PIConGPU as a powerful and scalable tool for connecting nanoplasma theory with experimentally accessible observables.
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All authors Cristian Medina, Klaus Steiniger, Brian Edward Marre, Marcel Mudrich, Asbjørn Ø. Lægdsmand, SivaRama Krishnan, Keshav Sishodia, Martin Albrecht, Eva Klimešová, Maria Krikunova, Jakob Andreasson, Weiyu Zhang, Deepthy Mootheril, Nikolas Rapp, Serge A. Krasnokutski, Robert Moshammer, Thomas Pfeifer.
Optics
Compact Photonic Fibre-based Deformation Sensor Fabricated by Two-Photon Polymerization
Vladimir Osipov, Ayman El-Tamer, Ulf Hinze et al. (+2 more)
We have demonstrated that compact deformation sensors with heights and widths of about 100 micrometers can be fabricated by two-
photon polymerization, using commercially available optical ferrules with embedded 125 micrometers-diameter optical fibers as the basic platform and the commercial photopolymers OrmoComp and FemtoBond as the fabrication materials.
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All authors Vladimir Osipov, Ayman El-Tamer, Ulf Hinze, Alexander Dulebo, David J. Webb
Optics
Characterization of Rocking Block Behaviors with Classical and Alternative Restitution Models
Fernando Gaibor E., Alexander López and Esther D. Gutiérrez
This work investigates how restitution modeling affects the dynamics of rocking blocks subjected to harmonic excitation. While several studies have reported discrepancies between experimentally observed impact behavior and the predictions obtained using the classical Housner restitution coefficient, the implications of adopting alternative restitution formulations on the global dynamics of rocking systems remain largely unexplored. The system is formulated as a hybrid non-smooth dynamical model and analyzed through bifurcation diagrams, Lyapunov exponents, and basins of attraction for different slenderness ratios. By comparing the classical restitution model proposed by Housner with the alternative formulation of Mao et al., we show that the choice of restitution model strongly influences the predicted system response. The alternative formulation leads to an earlier onset and greater prevalence of complex oscillations, as well as changes in the type, stability, and accessibility of attractors compared to the classical model. However, as the slenderness ratio increases, the dynamical features produced by both formulations progressively converge, indicating a reduced sensitivity to the restitution model for taller blocks. These results provide a dynamical perspective on why alternative restitution formulations, which predict impact responses closer to experimental observations, can produce markedly different behaviors from those obtained using the classical Housner model.
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Fluid Dynamics
Surface-tension calibration for N-phase mixtures
M.F.P. ten Eikelder and A. Brunk
Diffuse-interface (phase-field) models are a widely used framework for interfacial dynamics in complex fluids, in which sharp interfaces are replaced by smooth transition layers and interfacial forces follow from a free-energy functional. In these models, surface tensions and diffuse thicknesses are not prescribed directly but are encoded implicitly by the bulk multiwell potential and the gradient-energy term through one-dimensional
equilibrium profiles. While this link is classical in the binary Cahn–Hilliard setting, calibrating multiphase models is substantially more delicate because multiple pairwise surface tensions must be matched simultaneously and the relevant
equilibrium paths are constrained by the Gibbs simplex. The practical problem is therefore: given a chosen bulk potential and a set of target pairwise surface tensions, determine gradient-energy coefficients that reproduce these targets in the full multiphase model. Here we present a thermodynamically consistent calibration procedure for N-phase diffuse-interface free energies of Cahn–Hilliard type. The method determines a symmetric capillary
matrix that matches prescribed pairwise surface tensions through the model's
equilibrium profiles. We further introduce a rescaling strategy that adjusts diffuse interface widths to mesh-resolvable values while preserving the calibrated surface tensions. The resulting calibrated free-energy closure can be incorporated directly into N-phase mixture simulations, and we demonstrate this by applying it to N-phase Navier–Stokes–Cahn–Hilliard flows.
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History and Philosophy of Physics
The Good, the Bad, and the Ugly – Living with Priors in Bayesian confirmation
Niels Linnemann and Christian Wüthrich
Bayesian confirmation faces a classic problem: where do initial priors come from? In cases with abundant data and repeated updating, different priors tend to converge to the same posterior. However, in frontier research this convergence often fails, and confirmation remains sensitive to priors. We examine how physics practice in the case of gravitational wave research deals with such cases and, normatively, when prior sensitivity should be regarded as epistemically problematic. We offer a practice-based account of the prior problem, so far absent from the philosophical literature on Bayesianism. One upshot is a clearer diagnosis of so-called 'analogue confirmation'.
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Applied Physics
Microscaled Tunable Magnonic RF Phase Shifters
Johannes Greil, Antonio Angotti, Felix Kohl et al. (+11 more)
Tunable, microscopic, and energy-efficient solutions for
radio-frequency (RF) signal manipulation in the GHz regime are a key technology for efficient communication and sensing applications.
Spin waves offer micrometer wavelengths at GHz frequencies, combined with strong
magnetic-field tunability, making them inherently well-suited for tunable analog signal processing. Here, we demonstrate a novel concept: a micron-scale tunable RF phase shifter based on the wavelength shift of propagating
spin waves. High energy efficiency is achieved by using the stray field of a micromagnet on a piezoelectrically actuated MEMS cantilever to locally induce this shift. The device shows a phase shift of more than 360{\deg} at a center frequency of 6.1 GHz using a phase-shifting area of less than 0.02mm$^2$. By changing the
magnetic bias field, its functionality is experimentally confirmed over a range of center frequencies from 3 GHz to 8.2 GHz, and simulations show its applicability up to 14 GHz. A system-level characterization of an embedded device version demonstrates the qualification of magnonic phase shifters for highly integrated RF systems.
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All authors Johannes Greil, Antonio Angotti, Felix Kohl, Ádám Papp, Matthias Wagner, Maria Cocconcelli, Andrea Del Giacco, Dieter Ferling, Björn Heinz, Federico Maspero, György Csaba, Riccardo Bertacco, Markus Becherer, Philipp Pirro
Computational Physics
Large Language Model Based Agent for Automated Discovery in Computational Physics
Hang Lin, Chongwen Liu and Gang Yan
Scientific discovery in computational physics can often be framed as the optimization of quantitatively evaluable objectives subject to physical constraints. While researchers excel at formulating such problems, they frequently devote substantial effort to iterative refinement of methods and solution strategies. To accelerate this process, we introduce PhyNex, an autonomous
agent that systematically explores the solution space of scorable scientific tasks by coupling large
language model (LLM)-guided search with domain-specific computational tools that enforce physical consistency. PhyNex operates via progressive local search, accumulates reusable knowledge from both successful and failed attempts, and produces interpretable exploration trajectories that reveal which algorithmic components drive performance improvements. We validate PhyNex on three representative and scientifically important problems: predicting frequency-dependent dielectric spectra of
semiconductors from
crystal structure, designing probabilistic-
circuit heuristics for Max-Cut on graphs, and optimizing charging protocols for Dicke
quantum batteries in the chaotic coupling regime. Across the three tasks, PhyNex autonomously identifies solutions that match or exceed state-of-the-art approaches designed by human scientists, yielding search-averaged improvements of up to 3.8% in spectral similarity, up to 15.0% in normalized mean cut for Max-Cut, and 5.9% in ergotropy at the $80\mathrm{k}$ training checkpoint in open exploration. These findings demonstrate that LLM-based
agents with structured, feedback-driven exploration can substantially accelerate the path from problem specification to effective implementation, suggesting a practical division of labor in which scientists define objectives and constraints while automated systems navigate the methodological search space.
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Plasma Physics
Free-Molecular Face-Flux Preprocessing for Reduced Neutral-Continuity Modeling in Hall Thrusters: Particle-Based Reference and Deterministic SN-DFEM Realization
Yingjie Chen, Xi Chen and Yinjian Zhao
Neutral gas transport directly affects the ionization source, propellant utilization, and low-frequency discharge oscillations in
Hall thrusters. High-fidelity particle-based neutral models or DSMC methods can describe rarefied gas transport, but they are computationally expensive; in contrast, reduced neutral-continuity models are cheaper but require a closure for the neutral velocity or face-normal flux. Under a low-pressure collisionless approximation, this work adopts a free-molecular preprocessing strategy to provide a reference density field and the mean-velocity or face-normal-flux closure used by the reduced neutral-continuity equation in a manner consistent with the underlying transport model.On this basis, a particle-based free-molecular faceflux preprocessor is used as a
stochastic reference, and an SN-DFEM deterministic free-molecular preprocessor is proposed to generate the corresponding reference density, velocity moments, and face-normal fluxes within a unified free-molecular transport framework. Results show that the SN-DFEM preprocessor preserves the main neutral-density and velocity structures and reduces the statistical error in face-flux closure by about three orders of magnitude in the baseline continuity-recovery test. A prescribed moderate ionization-loss case further demonstrates the extension of the framework to free-molecular preprocessing with volumetric neutral removal.
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Fluid Dynamics
Impact of alignments between fluctuating and mean density gradients on the scale-dependent energetics of stably stratified turbulence
Soumak Bhattacharjee, Stephen M. de Bruyn Kops and Andrew D. Bragg
Non-trivial
alignments between vorticity and the strain-rate tensor play an important role in the
evolution of velocity gradients and the energy cascade in isotropic
turbulence. Here we explore how
alignments between the fluctuating and mean density gradients impact the mechanisms governing the turbulent kinetic energy (TKE) and available potential energy (APE) across scales in stably stratified
turbulence. This is motivated by analytical results that demonstrate a connection between them, and is conducted using direct numerical simulations (DNS) of statistically stationary, stably stratified
turbulence for $Pr = 1, 7, 50$ in the strongly stratified regime. After demonstrating how the gradient field
alignments depend on scale and $Pr$, we show that the
alignments are intimately connected to the reversal of the buoyancy flux at small-scales, and that regions of strong
alignment and misalignment correspond to regions where the horizontal TKE inter-scale flux becomes weak. The same is also true of the APE flux, except that at larger scales, regions of strong
alignment are associated with an upscale APE flux. The TKE and APE dissipation rates, and the mixing coefficient also show a strong dependence on the
alignment, especially for $Pr=1$. Finally, we explore the connection between the local
alignment and stability of the flow, and we find a non-trivial relationship, with regions of strong
alignment surprisingly occurring most often in stable regions. This demonstrates that the dynamical significance of the
alignments on the flow energetics cannot be understood through a simple connection between the local
alignments and local stability of the flow.
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Optics
Analysis of a compact interferometric imager
Laurent M. Mugnier, Vincent Michau, Hiyam Debary et al. (+1 more)
The advent of photonic integrated
circuits (PICs) will allow the replacement of the large aperture of an optical
telescope by a dense array of small apertures combined interferometically. The light coming from aperture pairs can be combined by a PIC in order to extract interferogram characteristics known as complex visibilities, from which the observed object can then be reconstructed. In such a compact interferometric imager, the optical components dedicated to image formation in a regular
telescope are no longer necessary. In particular, such a concept is relevant for space missions where weight and size are critical. In this communication, we study such an instrument concept, focusing on signal-to-noise considerations. We recall the design basis for the field and the spatial resolution, and we show that the spectral resolution must be no less than the field to resolution ratio. Then, we analyze the signal-to-noise ratio of this concept, assuming that each spatial frequency is recorded only once, and compare the signal-to-noise ratio with that of a monolithic
telescope. We perform the comparison in
Fourier space for an identical number of recorded
photons. We show that the noise propagation of the interferometric imager is identical to that of a monolithic
telescope that would have a flat Modulation Transfer Function with a level roughly given by the ratio of the small apertures' diameter to the maximum baseline. We conclude that the noise propagation in low and medium spatial frequencies is unfavorable for the interferometric imager.
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All authors Laurent M. Mugnier, Vincent Michau, Hiyam Debary, Frédéric Cassaing
Chemical Physics
Fast and accurate simulation of Raman spectra of gold-organic systems
Auguste Tetenoire, Vijaya Raghavan Kannan, Mikaël Kepenekian et al. (+1 more)
Resolving the spectral Raman signature of molecules grafted on a metallic support is often a difficult task, in which
quantum chemistry methods allow for precious additional rationalization and signal attributions, especially to probe the formation of a bond with the support. In the specific case of gold-organic architectures based on a Au-C bond, only a limited amount of experimental and theoretical reference data are available in the literature, and Raman simulations based on
quantum mechanics quickly become unaffordable with the size of the system. In this work, we evaluate the precision of a cost-efficient DFTB method to simulate Raman spectra of gold-organic systems at different scales, from gold complexes to functionalized gold surfaces. After a validation of the method through a careful comparison of DFTB Raman spectra of organometallic gold(I) and (III) complexes to DFT and experimental reference data, we discuss the case of molecules grafted on gold aggregates. For these simulations, the choice of the model (cluster or periodic surface) appears to be critical, and significant differences arise (positions and intensities of the peaks) when considering a full metallic slab, as allowed by the low computational cost of the method.
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All authors Auguste Tetenoire, Vijaya Raghavan Kannan, Mikaël Kepenekian, Arnaud Fihey
Instrumentation and Detectors · Cross-listed from cs.IT
Practical Low-Weight Codes for Energy-Efficient Bus Encoding
Lorenzo Valentini and Marco Chiani
We consider the transmission of data encoded into binary messages, with the goal of minimizing the Hamming distance, i.e., the number of bit-flips, between consecutive messages. This problem is relevant for enhancing the longevity of Non-Volatile
Memories and reducing transition-induced energy consumption in data buses. Known as Write-Efficient
Memory coding in the literature, this challenge has traditionally been addressed using optimal but complex schemes. In low-power computer systems the same topic is known as bus encoding. In this paper, we derive closed-form expressions to evaluate the average number of bit-flips for practical, sub-optimal encoding schemes, and propose two new schemes assisted by predefined random codebooks. We demonstrate that low-complexity solutions achieve performance very close to the optimal schemes, making them attractive for implementation in energy-sensitive and
memory-critical applications. For instance, by adding 8 extra bits to 64-bits data, sub-optimal schemes can achieve a bit-flip reduction (related to energy saving) of approximately 24.7%, compared to the 26.4% reduction offered by the significantly more complex optimal scheme.
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Optics · Cross-listed from cs.GR
HoloPathTracer: Fast and Accurate Wave Path Tracing for Holography
Wenbin Zhou, Xiangyu Meng, Jiankai Xing et al. (+3 more)
Holography offers unique advantages for delivering perceptual realism while preserving compact form factors in VR/AR. Its perceptual quality, however, hinges on encoding rich wavefronts of photorealistic scenes into interference patterns and then incoherently multiplexing the resulting wave fields for perception. Existing CGH paradigms decouple radiance estimation from wave propagation by pre-rendering radiance on discretized scene sectors. This separation between radiometric and wave-optical computation inherently limits the range of focus cues and visual effects that can be faithfully reproduced, including depth- and view-continuity, and physically based material behaviors such as glossy or mirror-like reflection and refraction. We present a physically accurate yet computationally efficient wave optics rendering framework leveraging path tracing to encode full 3D visual cues into phase holograms. Specifically, we employ a Monte Carlo method to solve both the rendering equation and the Rayleigh–Sommerfeld integral simultaneously. Our
algorithm is fully compatible with modern graphics techniques and can generate multiple time-multiplexed random holograms with minimal additional time cost via Path Reuse. By employing a fast approximation with an ambient radiance cache, we realize an order of magnitude convergence speed improvement. The resulting coherent wave fields that inherently encode comprehensive visual effects are converted into phase-only holograms under complex-amplitude supervision. Through extensive simulations and experimental validations on a spatial light modulator-based display prototype, we demonstrate faithful
holographic reconstructions of natural 3D cues and complex materials, including realistic defocus blur, view-dependent effects, as well as appearance highlights and reflections.
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All authors Wenbin Zhou, Xiangyu Meng, Jiankai Xing, Xin Liu, Suyeon Choi, Yifan Peng
Fluid Dynamics
Optimal heat transport at the edge of energy stability
Zijing Ding, Baole Wen and Hui Li
High heat transfer in Rayleigh–Bénard convection is commonly associated with vigorous turbulent motion, but
turbulence intensity alone does not explain how a limiting transport state is selected. We propose that such limiting states are organized by marginal energy stability. Starting from the exact perturbation-energy balance, we determine, for a prescribed mean temperature profile, the smallest neutral Rayleigh number over the balance parameter and all admissible disturbances. The corresponding marginal modes are then coupled to the exact mean-temperature equation, producing a self-consistent profile and heat flux. At large $Ra$, the selected branch gives $Nu\simeq0.0245Ra^{1/2}$ and develops conductive inner layers, logarithmic-like intermediate regions and a weakly stably stratified core. An equivalent background-field formulation yields the same governing equations and establishes uniqueness of the selected mean profile. Three-dimensional simulations at $10^6\le Ra\le10^8$ show that distributed thermal forcing based on this profile suppresses convective motion while retaining a large wall heat flux.
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