Applied Physics · Cross-listed from nlin.CD
Pole-organized cusp catastrophes in locally nonlinear resonator networks
Kanchan Sarkar
A resonator with one localized cubic nonlinearity can acquire additional cusp pairs when it is coupled to an otherwise linear network. At first-harmonic order, the cusp locus is a phase contour of the driving-point receptance $G(\Omega)$ at the nonlinear coordinate. We resolve that contour into the contribution of an active pole and its background. For a lightly damped, isolated modal pole on a slowly varying, nearly real background, cusp-pair birth occurs once the participation of that pole at the nonlinear coordinate exceeds a closed-form threshold set by its damping half-width and that background. On the weak-mixing hardening branch generated by a pole above the primary resonance, the birth coupling then grows as the square root of the damping at fixed mode separation, and of the mode separation at fixed damping. The
curvature of the full cusp contour determines whether the local fold set opens a gap (beaks type) or creates an isolated loop (lips type). The isolated-pole approximation reduces this classifier to a background-receptance rule whose range of validity is quantified on 100 random networks. The same ensemble tests the pole ladder numerically, while multi-harmonic continuation follows the whole reconnection sequence—connected band, gap, isolated lens, none—in the original equations at the fold of the birth locus, where the classifier is least protected. The construction also extends to coupled-mode Kerr networks, where the driving-point response sets the intrinsic fold geometry and the drive-port transfer response sets its accessibility.
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Computational Physics · Cross-listed from cs.LG
Enhancing generalization in endwall film cooling prediction: Incorporating the superposition principle into transformer-based neural operators
Qineng Wang, Liming Song, Tianyuan Liu et al. (+1 more)
In this study, a physics-enhanced neural operator framework is proposed to enhance the generalization prediction ability of the cooling layout of a turbine endwall with variable number of film holes. Specifically, inspired by the film cooling superposition principle, we propose a film cooling prediction model, namely superposition-based deep neural operator (SDNO), that divides the endwall temperature field prediction into two stages. In the first stage, the cooling layout of a turbine endwall is divided into several sub-parts with randomly assigned film holes, and a
Transformer-based neural operator network, namely Calculate Net, is designed to predict the temperature field of each sub-part. Then, in the second stage, another neural operator network, i.e., Super Net, is trained to combine the temperature fields predicted by Calculate Net for each sub-part and obtain the superposed temperature field of the full cooling layout. Additionally, instead of directly taking the film cooling contours as pixel plots, a signed distance function (SDF) which is sensitive to the variable locations of cooling holes, is designed to encode the location information of cooling holes. Furthermore, the proposed endwall film cooling prediction model is trained with the samples that changing the number of film holes from 1-5 with variable locations. Then, the trained prediction shows excellent generalization prediction ability, which can accurately predict the film effectiveness of the cooling layout with 10-20 film cooling holes that are unseen in the training samples. The proposed SDNO also improves prediction accuracy relative to the fully supervised baseline. With the above, the effectiveness of our proposed prediction model has been well demonstrated.
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All authors Qineng Wang, Liming Song, Tianyuan Liu, Zhendong Guo
Chemical Physics
Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation
Tianren Zhang
Designing
polymers with targeted properties requires navigating vast chemical spaces from limited labeled data. Here we introduce PolyLatentFlow, a framework based on continuous-time flow matching in latent space for unconditional and conditional
polymer generation, together with LlamaUni, a
multimodal representation combining
polymer sequence and 3D structural information. In unconditional generation, PolyLatentFlow with LlamaUni produced the largest yield of valid candidates novel relative to PolyInfo among the evaluated unconditional generators while maintaining high diversity. For $T_g$ conditioning, generated property distributions shifted systematically across a 200 {\deg}C target range. In multi-property tasks, molecular representations showed similar surrogate target fidelity but differed markedly in validity, training-set replay, and structural proximity to labeled
polymers. PolyLatentFlow with LlamaUni consistently combined high validity with low replay and achieved the largest per-attempt yield of nonreplayed target hits for CO$_2$/N$_2$ conditioning. These results demonstrate latent space flow matching for
polymer inverse design and identify molecular representation as a key determinant of target control and exploration beyond labeled chemistry.
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Fluid Dynamics · Cross-listed from nlin.SI
Riemann spaces associated with the Navier-Stokes equations: a survey of the geometric approach
Valerii S. Dryuma
We survey a
geometric approach to the Navier-Stokes equations developed by the author over the past fifteen years. The central object is a Riemannian space of fourteen dimensions whose metric is
Ricci-flat precisely on the solutions of the Navier-Stokes system, so that the behaviour of a viscous incompressible fluid becomes a property of
curvature. The space decomposes into a flat six-dimensional part and two dual quadruples of coordinates, Eulerian and Lagrangian, which exhibits the two classical descriptions of a fluid as two halves of a single geometry. We also recall the associated six-dimensional metric, whose integrability condition is the incompressibility of the fluid; the link with the projective geometry of second-order ordinary differential equations in the sense of E. Cartan; and the use of Cartan's invariants to construct a three-dimensional analogue of the Taylor-Green
vortex. An application of the same construction to the equations of rotation of a rigid body is indicated.
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Geophysics
Preemptive Ensemble Forecast Sensitivity to Observations
Fumitoshi Kawasaki and Shunji Kotsuki
Estimating the impact of observations newly added to an existing numerical weather prediction (NWP) system requires reintegrating the ensemble forecast from the analysis that assimilates the additional observations, which is laborious and computationally expensive. We therefore propose preemptive EFSO (PEFSO; preemptive ensemble forecast sensitivity to observations), which estimates observation impact without model reintegration by invoking a stronger tangent-linear approximation than that used in EFSO. The estimated impact, however, is merely a scalar quantity at a specified verification time, so it is also valuable to obtain the ensemble forecast in which the remaining additional observations are assimilated after denying the detrimental ones. We refer to this procedure as ADD-SEL, but carrying it out requires reintegration. We therefore propose two methods for updating the ensemble forecast without reintegration as approximations to ADD-SEL, collectively termed ADD-SEL-PRE: one recomputes the ensemble transform
matrix (i.e., the Kalman gain), and the other modifies only the innovations at lower cost. Experiments with the Lorenz-96 model examine whether both can be approximated without reintegration. PEFSO estimates observation impact comparable to that obtained by EFSO within the range where the tangent-linear approximation remains valid, even under more practical conditions: an ensemble size of 10, the analysis as the reference state, and only the additional observations available. When additional observations are assimilated after denying detrimental ones, both ADD-SEL-PRE methods update the ensemble forecast, giving forecast errors comparable to those from ADD-SEL, particularly for lead times up to two days, where the tangent-linear approximation is expected to hold.
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Space Physics
Low-frequency Intermittency and Structures in the Solar Wind at 1 au
Jiaming Wang, Francesco Pecora, Yan Yang et al. (+1 more)
Intermittency in the
solar wind is commonly studied within the inertial and dissipative ranges, where scale-dependent
magnetic field distributions become increasingly non-Gaussian toward smaller scales until dissipation becomes important. Conversely, whether Gaussianity is recovered at large scales remains unclear. To address this, we systematically examine
magnetic field increment kurtosis over scales from 1 minute to 1 year using more than two decades of in situ observations from NASA's Wind and ACE spacecraft. We find that although kurtosis tends toward Gaussian value of 3 near the correlation scales, it generally remains elevated (super-Gaussian) at larger scales. Kurtosis also varies substantially over time, with pronounced super-Gaussian intervals during high
solar activity phases and sporadic sub-Gaussian intervals primarily in the radial component. These results provide evidence for large-scale intermittency in the
solar wind, potentially arising from mixing of different
solar wind streams and nonstationary driving of
solar sources.
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All authors Jiaming Wang, Francesco Pecora, Yan Yang, William H. Matthaeus
Geophysics
Ensemble Forecast Updates without Model Re-integration Based on Ultra-rapid Data Assimilation: Idealized Experiments with a Heavy Rainfall Case
Fumitoshi Kawasaki, Kenta Kurosawa, Atsushi Okazaki et al. (+1 more)
Observations at high frequency have become dramatically more abundant with recent technological advances. Ultra-rapid data assimilation (URDA) has been proposed to exploit them, frequently updating ensemble forecasts at lower cost without re-integrating the forecast model. In this study, we examine the applicability of URDA to a realistic numerical weather prediction (NWP). Specifically, we conducted idealized experiments for the heavy rainfall event of August 2021, using the regional atmospheric model Scalable Computing for Advanced Library and Environment (SCALE-RM). In the experiments, pseudo-observations emulating the Automated Meteorological Data Acquisition System (AMeDAS) were assumed to become available every 10 min. The results show that the threat score for hourly precipitation is generally improved in the forecasts updated by URDA relative to the pre-existing baseline forecasts, through the ensemble-based error covariance. Furthermore, for sea level pressure, temperature, relative humidity, and winds, the root mean square error of the URDA-updated forecasts is reduced relative to that of the baseline forecasts as the forecasts are successively updated by assimilating additional observations. These results indicate the potential of URDA to operate effectively with a realistic NWP model.
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All authors Fumitoshi Kawasaki, Kenta Kurosawa, Atsushi Okazaki, Shunji Kotsuki
Optics
Inverse reconstruction of dissipative Kerr soliton interactions
Carlo Silvestri, Panayotis G. Kevrekidis, Stephane Coen et al. (+2 more)
We reconstruct the pairwise interaction potential between dissipative Kerr
solitons from the linear stability spectrum of a perfect
soliton crystal solution of the Lugiato–Lefever equation. The
solitons form an overdamped
lattice whose positional
eigenvalues encode the pairwise-force derivative, mirroring the extraction of microscopic interactions from
phonon or relaxation spectra in condensed-matter systems. The method extends beyond quadratic dispersion and assumes no functional form for the interaction. Our theory predicts a novel state -the soft
soliton crystal- which has vanishing stiffness and a diverging positional relaxation time, and also explains experimentally observed
soliton steps as arising from a sign reversal of the pairwise-force derivative.
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All authors Carlo Silvestri, Panayotis G. Kevrekidis, Stephane Coen, C. Martijn de Sterke, Antoine F. J. Runge
Accelerator Physics
Continuous-Wave Operation at 30 MV/m of a Superconducting Radio-Frequency Gun Cryomodule with an Exchangeable Cathode Plug
T. Konomi, T. Xu, Y. Choi et al. (+30 more)
A prototype 185.7 MHz superconducting
radio-frequency (SRF) electron gun cryomodule has been developed for the proposed LCLS-II-HE Low Emittance Injector at SLAC. It has an in-situ ultra-high-
vacuum exchangeable cathode plug system compatible with
semiconductor photocathodes. With a copper plug, the gun achieved continuous-wave RF operation at a cathode field of 30 MV/m. No measurable degradation was observed after plug removal and reinsertion with the cavity at 4.4 K. This result is a significant improvement over past continuous-wave SRF gun systems with exchangeable plugs.
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All authors T. Konomi, T. Xu, Y. Choi, K. Elliott, B. Gower, B. Tousignant, J. Wenstrom, A. Taylor, C. Compton, W. Chang, X. Du, W. Hartung, S. H. Kim, S. Miller, D. Morris, M. Patil, L. Popielarski, J. Wei, Z. Yin, J. Smedley, J. Maniscalco, C. Adolphsen, M. Murphy, D. A. Gonnella, M. P. Kelly, T. Petersen, A. Arnold, S. Gatzmaga, R. Steinbrück, P. Murcek, R. Xiang, J. Teichert, J. Lewellen
Fluid Dynamics
Spreading dynamics of a drop of yield-stress fluid subject to vertical oscillations
Alice Woodbridge, Claudio P. Fonte and Anne Juel
The measurement of rheological properties from non-idealised flows is routinely used to characterise yield-stress materials. We extend the canonical slump test, which evaluates yield stress based on the spread of a large drop under
gravity, by subjecting such a drop to vertical oscillation of its substrate. We test this flow configuration by direct comparison between experiments and numerical simulations of the time-dependent Cauchy equations coupled to the Saramito-Herschel-Bulkley (SHB) constitutive model. This provides a sensitive test of the SHB model near yield because the vibrated drop can releases stress exceeding the yield stress by adjusting its shape as the forcing acceleration is increased, and thus, remains close to the yield threshold for all forcing. We find that the SHB model systematically underpredicts the amplitude of the viscoelastic oscillatory response of the drop to forcing by a factor of five. However, spreading can be captured numerically by selecting a solvent viscosity that represents the appropriate post-yield rheology of the material at the expense of matching sub-yield properties. This highlights a fundamental challenge in simulating flows in which yielded and unyielded material coexist, such as those encountered in proto-rheological tests. We establish the vibrated drop as a powerful proto-rheological configuration for estimating the yield stress across drops of different rheology, size and shape, by establishing a proportional relationship between the threshold acceleration required to induce spreading relative to
gravity and the yield number, the ratio of yield stress to gravitational stress acting at the centre of mass of the drop.
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Atmospheric and Oceanic Physics
WIND-Bench: A Benchmark Dataset for In-Situ Near-Surface Wind Speed Observations Across the Conterminous United States
Kyla Bazlen, Grant Buster, Brandon Benton et al. (+5 more)
Accurate wind forecasts are essential for operational decision-making and public safety, yet forecasts tend to miss near-surface high wind speeds in complex terrain. In response, advances in machine learning (ML) weather prediction methods have demonstrated the ability to improve forecast skill beyond traditional numerical weather prediction (NWP) models. However, the absence of a
benchmark dataset to evaluate NWP and ML models with sufficient, quality-controlled wind speed observations in complex terrain poses challenges to the development and intercomparison of high-quality surface wind forecasts across the Conterminous United States (CONUS). We develop the Wind IN-situ Data
Benchmark (WIND-Bench), a
benchmark dataset from in-situ observations in the Meteorological Assimilation Data Ingest System (MADIS) observational network. WIND-Bench integrates multiple sensor networks with quality control that distinguishes sensor failures from high-wind conditions, using a framework that validates observations against forecasts from the National Oceanic and Atmospheric Administration (NOAA) High-Resolution Rapid Refresh (HRRR) model. WIND-Bench provides a standardized
benchmark for evaluating ML and NWP models and for quantifying forecast skill, accelerating the development, evaluation, and operational deployment of skilled near-surface wind forecasts.
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All authors Kyla Bazlen, Grant Buster, Brandon Benton, Lauren North, Ansley Baring, David D. Turner, Emily Wells, Laura Vimmerstedt
Optics
Frequency-Multiplexed Photonic Reservoir Computing with a Synchronously Pulse-Driven Optical Cavity
Amir Arsalan Arabieh, Simon-Pierre Gorza and Serge Massar
In this work, we introduce a new platform for frequency-multiplexed reservoir computing based on an optical cavity driven by pulses whose repetition period is matched to the cavity roundtrip time. The input symbols are encoded by modulating either the amplitude or the phase of the driving pulses. Our numerical results show that the proposed synchronously pulse-driven cavity operates in a weakly nonlinear regime under anomalous dispersion, while under normal dispersion it exhibits optical bistability with a high-peak-power upper-branch state. By isolating the key physical parameters, we quantify their individual contributions to information-processing performance. We further show that spectral
symmetry breaking induced by third-order dispersion and Raman scattering almost doubles the information-processing capacity. Finally, we demonstrate that modulation-induced branch switching within the bistable regime limits stable reservoir operation.
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Optics · Cross-listed from quant-ph
Path-Degenerate Quantum Interferometry for Loss Resilience in Gravitational-Wave Detectors
Jonas Rittmeyer, Niels Boettner, Farid Khalili et al. (+2 more)
Optical decoherence degrades
quantum correlations in squeezed states of light, severely limiting the
quantum-nondemolition (QND) sensitivity of gravitational-wave detectors. Here, we propose the path-degenerate
quantum interferometry scheme that obviates the need for entire optical subsystems—including additional filter cavities, auxiliary parametric amplifiers, and Faraday isolators—thereby drastically reducing spatial mode mismatches while inherently integrating variational output, a long-standing theoretical proposal to further deepen the QND regime. We experimentally demonstrate a core aspect of this scheme, achieving a shot-noise-preserving signal enhancement that directly counteracts the detrimental effects of readout loss. By delivering an improved signal-to-
quantum-noise ratio solely through the consolidation and reduction of currently considered optical subsystems, our approach offers a highly optimized route toward enhanced
quantum-noise reduction in upcoming LIGO upgrades and next-generation gravitational-wave observatories.
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All authors Jonas Rittmeyer, Niels Boettner, Farid Khalili, Mikhail Korobko, Roman Schnabel
Atomic Physics · Cross-listed from quant-ph
Ab initio multichannel calculation of the Bethe surface and Compton defects for molecular hydrogen
Hakon Volkmann and Alejandro Saenz
A correlated multichannel computation of the generalized oscillator-strength density for electrons scattering off randomly oriented molecular hydrogen at the internuclear separation of 1.4 $a_0$ is being presented. The numerical procedure features a novel free-boundary method based on configuration interaction of ionic orbitals expanded in B splines using prolate-spheroidal coordinates. Satisfactory agreement between the present and both experimental as well as other theoretical results has been found, thus providing a view on the Bethe surface for energy transfers up to of 558 eV. The systematic and robust convergence behavior of the method further allowed for assessing the validity of the binary-encounter approximation, providing a confirmation of experimentally determined Compton defects for H$_2$ using a single consistent theoretical description. Overall, the present findings are expected to be helpful for calculating highly accurate energy-loss spectra for fast electrons passing through and ionizing molecular hydrogen gas or its isotopologues.
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Fluid Dynamics
Transferable Low-Dimensional Representations of Aircraft Surface Fields
Francis G. VanGessel, Cashen Diniz and Mark Fuge
Analyzing flow fields on aerodynamic surfaces is critical to designing next-generation aircraft. Raw computational representations are high-dimensional and costly for complex three-dimensional bodies, while conventional low-dimensional representations apply only to the systems from which they derive. We introduce an approach that learns fluid flow representations from simple two-dimensional geometries and transfers them to complex real-world three-dimensional aircraft. Comparing the proposed Least Volume autoencoder (LVAE) to proper orthogonal decomposition (POD), we show both yield compact 28-41 dimensional representations of two-dimensional flow fields, with the nonlinear LVAE producing more accurate in-domain reconstructions at matched dimensionality. These models transfer zero-shot to three-dimensional systems, and counterintuitively the linear POD transfers more accurately than LVAE. Applied to extruded wings, POD-reconstructed lift and drag remain within 0.1% and 0.7% of true values, while transfer to complex blended wing body aircraft preserves lift and drag to within 0.3% and 0.7% without any three-dimensional training data. We further show that three-dimensional flow patterns missed by the pretrained model can be isolated to train a disentangled 3D latent space via a dual encoder-decoder transfer learning strategy, performing accurately in previously inaccessible data-scarce regimes. Using only five blended wing training cases, mean squared reconstruction error drops by 5x relative to the 2D pretrained models and 70x relative to models trained from scratch. These results establish that transfer learning from readily available two-dimensional
datasets unlocks compact, interpretable representations of complex three-dimensional aircraft surface flows, enabling design insights in data-starved early phases where conventional approaches require hundreds to thousands of simulations.
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Instrumentation and Detectors
Agentic TCAD Calibration Workflow for Oxide Semiconductor Transistors
Gyujun Jeong, Junmo Lee, Sungwon Cho et al. (+11 more)
Experimental TCAD calibration is essential for predictive technology modeling of emerging
oxide semiconductor transistors. However, it remains time-consuming and expert dependent because of model ambiguity. Multiple physical models and parameter sets can reproduce the same measured transfer characteristics, while local fitting alone cannot uniquely identify the underlying device physics. We present the first demonstration of an agentic TCAD calibration workflow for a fabricated bottom-gate In–W–O (BG-IWO) transistor. Starting from the measured transfer curve and device information, the workflow uses measurement–TCAD residuals and local sensitivity tests to select bounded parameter corrections or evaluate additional physical models, and accept only updates that improve device metrics. The LLM
agent orchestrates the workflow, while Sentaurus governs the device physics. For the 2%-W reference device, five
agent-suggested updates yield a fixed calibrated model, reducing the multi-metric device objective $J$ by 14.3$\times$. Maximum $V_{\mathrm{th}}$/$I_{\mathrm{on}}$ errors are 36.1~mV/0.022 decade for varying-drain-bias tests and 46.2~mV/0.062 decade for varying-channel-length tests, demonstrating model transferability across bias and geometry rather than a local parameter fit. W-composition tests provide process-sensitive insight. This agentic workflow provides a faster route to model development for emerging device technologies.
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All authors Gyujun Jeong, Junmo Lee, Sungwon Cho, Woohyun Hwang, Kwangyou Seo, Suhwan Lim, Wanki Kim, Daewon Ha, Rishi Ranade, Kihang Youn, Ram Cherukuri, Yiyi Wang, Asif Khan, Shimeng Yu
Physics and Society · Cross-listed from cs.SI
Motifs in temporal hypergraphs
Quintino Francesco Lotito, Lorenzo Betti, Federico Battiston et al. (+1 more)
Network motifs, recurrent local patterns of interactions in graphs, provide fundamental insights on the interplay between structure and functionality in complex systems. Many real-world systems are not well represented by traditional static pairwise networks, as interactions may involve groups of nodes, occur over time, or encode directionality. In this paper, we introduce temporal motifs for hypergraphs and directed hypergraphs, extending motif analysis to timestamped many-body interactions. We formalize the corresponding mining problem, study the combinatorial structure of these motifs, and develop exact
algorithms for their enumeration. In particular, we propose a dynamic programming
algorithm that substantially reduces the computational cost of motif mining, achieving orders of magnitude speedups on empirical
datasets. We also introduce a null model for temporal hypergraphs to assess the statistical over- and under-expression of motifs. Applying the proposed framework to real-world
datasets from different domains, including face-to-face contacts, scientific collaborations, e-mail exchanges, and Bitcoin transactions, we show that temporal hypergraph motifs reveal distinct forms of local organization across systems. Finally, we demonstrate their use as an exploratory tool through focused case studies on persistent patterns in scientific collaborations and e-mail communications.
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All authors Quintino Francesco Lotito, Lorenzo Betti, Federico Battiston, Giuseppe Francesco Italiano
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Functionalization-Driven Charge Redistribution Enabling Ultra-High-Capacity V2B MBene Anode for Li/Na ion batteries: A First-Principles study
Shaiokh Bin Abi and Ahmed Zubair
Amid increasing global demand for clean, sustainable energy, the search for novel electrode materials has emerged as a crucial link to advancing future energy storage technologies. Here, we explored the potential of N-functionalized 2D MBene V2BN2 as anode materials for Li- and Na-ion batteries using first-principles calculations.
Phonon dispersion and ab initio molecular dynamics calculations were employed to assess the dynamic and thermal stability of the material. The intrinsic metallic properties of V2BN2 were revealed through electronic band structures and density of states analyses. Importantly, Bader charge analysis demonstrates substantial charge redistribution upon Li/Na adsorption, leading to stronger ion-substrate interactions compared to the pristine counterpart. This redistribution plays a decisive role in enhancing Li/Na ion adsorption and stabilizing ion accommodation. Furthermore, owing to favorable multilayer adsorption of Li and Na ions, V2BN2 exhibited high theoretical specific capacities of 1524 and 762 mAh/g, as well as low open
circuit voltages of 0.73 and 0.23 V for Li and Na, respectively. In addition, the energy barriers were calculated to be 0.49 and 0.29 eV for Li- and Na-ion transport, respectively, indicating rapid ion transport and excellent rate capability. These results indicate that V2BN2 holds significant potential as an anode material for next-generation rechargeable ion batteries.
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