Biological Physics · Cross-listed from math.OC
Robust Sparse Identification of Nonlinear Dynamics via Least Trimmed Squares
Fabio Amaral, Geovani N. Grapiglia and Cassio M. Oishi
In this work, we propose a robust Sparse Identification of Nonlinear Dynamics (SINDy) pipeline for handling
datasets corrupted by noise and outliers. The method decouples outlier filtering from sparse regression by combining Iterative Least Trimmed Squares (ILTS) with Sequentially Thresholded Least Squares (STLS). Unlike standard approaches that treat all observations uniformly within a single regression stage, the proposed ILTS-SINDy framework first applies an ILTS procedure that iteratively minimizes the sum of the smallest squared residuals to identify the most reliable observations without prior knowledge of outliers, after which STLS is used to recover a parsimonious governing model. Extensive numerical experiments show that ILTS-SINDy can significantly outperform existing robust SINDy variants across a range of outlier contamination levels, with performance maintained even under settings with up to $20\%$ corrupted observations.
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Fluid Dynamics
Nonlinear evolution of instability in inertialess elasto-viscoplastic Poiseuille flow
James D. Shemilt, Neil J. Balmforth and Duncan R. Hewitt
A widely used constitutive law for elasto-viscoplastic fluids (Saramito's model) predicts linear instability in inertialess pressure-driven channel flow. The instability arises due to the yield stress of the fluid and is strongest at the shortest streamwise wavenumbers, calling into question the physical validity of the constitutive model. Here, we show that the short wavelengths can be controlled by the addition of
polymer stress
diffusion and conduct two-dimensional numerical simulations to explore the nonlinear dynamics. On reaching finite amplitude, the instability is shown to generate spatio-temporally complicated states. Fluctuations about the final mean state are pronounced near and between the yield surfaces that border an unyielded plug spanning the centre of the channel. The instability and transition arise for Weissenberg numbers of order unity and higher.
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Fluid Dynamics
Data-driven linear analysis of turbulent flows
Benjamin Herrmann, Katherine Cao, Carlos A. Gonzalez et al. (+2 more)
Mean-flow-based linear analyses of turbulent flows, such as resolvent analysis, provide valuable insight about flow structures and their dynamics that has been widely leveraged to model, control and understand the underlying flow physics. However, these analyses are computationally expensive for flows over complex geometries and require the use of specialized codes that are typically only available in research environments. On the other hand, data-driven modal decompositions, such as the dynamic mode decomposition (DMD), identify turbulent flow structures that, although statistically relevant, do not provide insight into the physical mechanisms driving their dynamics. Here we introduce a novel data-driven method – nonlinearity-subtracted DMD (NSDMD) – that leverages knowledge of the structure of the Navier–Stokes equations to ensure that the learned operator is a low-rank approximation of the underlying mean-flow-linearized dynamics. Specifically, the method uses snapshots of the nonlinear terms in the perturbation equations to explicitly account for the contribution of the nonlinear forcing to the dynamics. We demonstrate the use of NSDMD to perform data-driven resolvent analysis on direct numerical simulation (DNS) and large-eddy simulation (LES)
datasets, starting with a minimal channel flow and scaling up to the flow over a full aircraft model. As a result, NSDMD allows performing linear analyses of turbulent flows as a post-processing step on simulation data obtained with any available high-fidelity computational fluid dynamics (CFD) code.
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All authors Benjamin Herrmann, Katherine Cao, Carlos A. Gonzalez, S. L. Brunton, Beverley J. McKeon
Atmospheric and Oceanic Physics
Analytical Model for the Higher Order Moments of Midlatitude Atmospheric Temperature Distributions
Keiko Kircher, Cristi Proistosescu and Ryan L. Sriver
Observed distributions of atmospheric temperature are non-Gaussian. Therefore, moments beyond variance are necessary in determining the frequency of extreme temperature events. Here we propose a simple kinematic model for atmospheric mid-latitude temperature variability based on symmetric advection from a non-symmetric background temperature profile. We then use this model to derive analytical expressions for the higher order moments of temperature distributions. Our results show that nonzero skewness and kurtosis arise due to the nonlinearity of the time-mean meridional temperature profile. The analytical model matches an idealized Held-suarez atmospheric model, indicating nonlinearity of time-mean temperature in latitude is the dominant contribution to nonzero skewness and kurtosis in synoptic temperature variations. Model analysis further shows decrease in higher order moments due to
climate change come roughly equally from changes in mixing length and changes in the background temperature profiles.
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Plasma Physics
High-Performance Resilient Multi-GPU Hybrid Particle-in-Cell Monte Carlo Simulations at Scale
Jeremy J. Williams, Stefan Costea, David Tskhakaya et al. (+13 more)
The increasing demand for high-performance computing in
plasma physics has driven scalable and resilient simulation methods capable of efficiently exploiting modern multi-GPU architectures. This work extends a portable hybrid MPI+OpenMP implementation of BIT1, focusing on high-performance resilience for accelerated Particle-in-
Cell (PIC) Monte Carlo (MC) simulations under both uniform and non-uniform load conditions. Scalable particle load balancing and robust checkpoint/restart mechanisms across Nvidia and AMD accelerators are integrated with standardized I/O using openPMD and ADIOS2. This leverages BP4 for high-performance file-based checkpointing and SST for in-
memory data streaming, enabling efficient data movement, resilient large-scale execution, seamless continuation from existing checkpoints, and effective handling of computational and I/O workloads. Advanced HPC profiling and tracing tools, including Nvidia Nsight Systems and AMD ROC-Profiler with Perfetto, provide detailed insights into computation, communication, and system-level behavior for optimization. Performance results on Frontier (OLCF-5), MN5, and LUMI-G demonstrate strong and weak scaling up to 800 GPUs, validating the framework for large-scale PIC MC simulations, while in-situ analysis and visualization using scalable I/O further enhance scientific insight without interrupting multi-GPU execution on current and future exascale systems.
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All authors Jeremy J. Williams, Stefan Costea, David Tskhakaya, Leon Kos, Ales Podolnik, Jakub Hromadka, Jordy Trilaksono, Yi Ju, Kallia Chronaki, Evangelos Gkolantas, Vassilis Papaefstathiou, Allen D. Malony, Sameer Shende, Frank Jenko, Erwin Laure, Stefano Markidis
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Microscopic and macroscopic characterization: MBE-grown versus sputter-deposited Au/Co/Au thin films for CISS and MIPAC effect studies
Lokesh Rasabathina, Thi Ngoc Ha Nguyen, Aleksandr Kazimir et al. (+12 more)
Chirality-induced
spin selectivity (CISS) enables
spin-dependent transport at
chiral molecule/Au(111) interfaces and is used in spintronics when combined with
ferromagnetic thin films in
spin-valve-type hybrids. However, the influence of substrate microstructure on CISS and the related magnetization induced by the proximity of adsorbed
chiral molecules (MIPAC) effect is still not well understood. In this study, we compare the effects of the adsorption of L-
chiral alpha-helical alanine-rich peptides on Au/Co/Au
ferromagnetic thin films fabricated by molecular beam epitaxy (MBE) and magnetron sputtering. X-ray reflectivity and X-ray diffraction show sharper interfaces and a narrower Au(111) rocking-curve width for the MBE-grown sample. However, atomic force microscopy and scanning tunneling microscopy images reveal that both sample types have locally smooth Au(111) surface regions suitable for peptide adsorption, despite clear differences in larger-scale morphology. Microscopic scanning tunneling spectroscopy after peptide exposure yields similar magnetization-direction-dependent tunneling currents in both sample types, confirming a similar magnitude CISS effect on the molecular scale. In contrast, macroscopic magneto-optical Kerr effect hysteresis loops and effect microscopy reveals that only sputter-deposited samples show slight coercivity enhancements and a consistent reduction in domain wall velocity after peptide exposure. These results suggest that microscopic CISS signatures are robust for both sample types, whereas macroscopic MIPAC-type
magnetic responses are more sensitive to the substrate microstructure.
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All authors Lokesh Rasabathina, Thi Ngoc Ha Nguyen, Aleksandr Kazimir, Rico Ehrler, Julia Krone, Franziska Schölzel, Zihao Liu, Peter Heinig, Markus Gößler, Irene Coin, Christina Lamers, Georgeta Salvan, Lech Tomasz Baczewski, Christoph Tegenkamp, Olav Hellwig
Biological Physics
Effect of environmental variation on the benefits of learning
Xiao Zhou and BingKan Xue
Signal recognition plays a critical role in species interactions and can be enhanced by learning signal characteristics through experience. In brood parasitism, host species may use visual cues to recognize and reject parasite eggs from their nests; because egg appearances vary within and between host individuals, a host can improve recognition by learning a tailored template of its own eggs. Nevertheless, constitutive and induced costs of learning may inhibit an extended learning period. We use a simple model of signal detection and learning to study how the benefits of learning are affected by different sources of variation in the learning signal. We find that phenotypic variation in egg appearances within a host hinders learning by adding noise to the signals, whereas genotypic variation between individuals promotes learning by carrying more information in the signals. Moreover, we consider environmental variation that can cause egg appearances to fluctuate across clutches over time. We find that such environmental variation reduces the fitness of learning hosts by distorting the signals, creating an effective cost that can offset the benefits of learning. Our results imply that learning or even a brief period of imprinting may be evolutionarily disfavored in homogeneous populations and variable environments.
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Chemical Physics
Limitations of Classical Force Fields for Metal Coordination Modes in Proteins: A Multilevel Study of Ca$^{2+}$ in Integrin $\alpha_V\beta_3$
Levy Andrea, Frigerio Giulia, Grollier Jules et al. (+3 more)
Standard biomolecular force fields often present limitations in modeling metal coordination modes. Here, we combined classical and QM/MM molecular dynamics simulations to investigate the Ca$^{2+}$ mediated binding of cRGD to integrin $\alpha_V\beta_3$. The results demonstrate the inherent limitations of fixed-point-charge force fields in reproducing asymmetric binding modes and highlight the value of QM-based multilevel approaches to assess the correctness and accuracy of FF models and capture metal coordination modes in complex biomolecular systems.
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All authors Levy Andrea, Frigerio Giulia, Grollier Jules, Siani Paulo, Di Valentin Cristiana, Rothlisberger Ursula
Atmospheric and Oceanic Physics
Investigation of regional variations in CO$_2$ growth rates : Integrating Emission Inventories and Atmospheric Observations
Yogesh Bali, Darja Cvetković, Juan Gancio et al. (+7 more)
Atmospheric carbon dioxide (CO2) growth rates reflects the combined influence of anthropogenic emissions, biospheric carbon exchange, and
climate variability. While
climate mitigation is primarily evaluated using bottom-up emission inventories within political boundaries, there is a need to validate these emission reductions using atmospheric measurements. Here, we present a global top-down analysis of atmospheric CO2 growth rates using CAMS atmospheric CO2 reanalysis, EDGAR anthropogenic emissions, GOSIF
dataset and the Southern Oscillation Index (SOI) as a measures of biospheric activity, to quantify the relative influence of human and natural drivers. We find that atmospheric CO2 growth rate varies substantially across space and time but is dominated by natural carbon-cycle processes and global background trends. Anthropogenic emission signals are frequently masked by natural variability, making regional top-down detection of human emission changes difficult. The COVID-19 emission reductions in 2020, despite occurring during a neutral ENSO year, were not consistently reflected in regional atmospheric CO2 growth rates, highlighting the dominant roles of biospheric dynamics and atmospheric transport. Using unsupervised clustering and persistence analysis, we identify five characteristic carbon-cycle regimes. Spatial averaging removes much of the regional variability, leaving large-scale
climate as the dominant control in most regimes. The active biosphere is the main exception, where strong biogenic signals persist, underscoring the critical role of tropical forests in shaping atmospheric CO2 variability.
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All authors Yogesh Bali, Darja Cvetković, Juan Gancio, Adrián Gutiérrez-Arroyo, Sofia Vazquez Alferez, Xuan Tung Vu, Jin Yan, Pietro Zgaga, Fakhteh Ghanbarnejad, Nasrin Mostafavi Pak
Medical Physics · Cross-listed from q-bio.QM
Establishing the Minimal Clinically Important Difference (MCID) for Smartphone-Derived Gait Measures in Multiple Sclerosis
Mike D Rinderknecht, Bernhard Fehlmann, Dimitar Stanev et al. (+7 more)
Background: Digital health technologies allow for frequent, remote gait monitoring in people with multiple sclerosis (MS). However, to differentiate daily variability from actual disease progression in longitudinal data, established minimal clinically important differences (MCID) are required. Currently, there is limited literature defining these thresholds for digital gait metrics. Objective: To establish MCIDs for digital gait measures reflecting progression in MS. Methods: Digital gait measures were captured via daily, remote, smartphone-based Two-Minute Walk Tests in CONSONANCE (NCT03523858), a phase 3b study of ocrelizumab in progressive MS. Using an anchor-based approach, median changes from baseline at Week 96 on digital gait measures were computed for patients showing clinically meaningful worsening on either Timed 25-Foot Walk, Ambulation Score, Expanded Disability Status Scale, or 12-item Multiple Sclerosis Walking Scale. These changes were subsequently triangulated to derive the MCID estimates. Results: 243 patients with progressive MS (female: n=125 (51%); mean [SD] age: 49.3 [9.3]; mean [SD] EDSS: 4.8 [1.4]) had digital gait data available at baseline and Week 96. Median changes were generally consistent across anchors. Triangulated MCIDs are: Step Velocity = -0.16 m/s, Step Velocity Scaled to Walking Time = -0.18 m/s, Step Duration = 0.06 s, Step Length = -0.07 m, Total Number of Steps = -28, and Total Distance Walked = -24 m. Conclusion: These MCIDs provide a framework for interpreting meaningful gait changes and integrating digital measures into MS outcome evaluation. Beyond facilitating novel clinical trial endpoints to evaluate treatment efficacy, they enable objective, real-world monitoring to advance personalized patient care.
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All authors Mike D Rinderknecht, Bernhard Fehlmann, Dimitar Stanev, Cedric Simillion, Ernst Bos, Letizia Leocani, Agne Kazlauskaite, Gary Cutter, Helmut Butzkueven, Licinio Craveiro
Popular Physics
Signs and Signatures of Intelligence
Julia DeMarines
Research into the emergence and
evolution of intelligence is underrepresented in astrobiology. I propose noosemiotics, the study of noosignatures, to address this gap. Noosignatures are the structured traces that minds leave on a medium, whether physical (tools, etchings, architecture) or signal-based (encoded transmissions, complex animal communication), that remain detectable as the products of intelligence regardless of whether their meaning can be recovered. Where biosignatures mark life and technosignatures mark technology, noosignatures mark the space between. This paper defines noosemiotics, situates it within astrobiology, and proposes a research program for empirical metrics, with material culture as the primary test bed.
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Optics · Cross-listed from eess.IV
A Zero-Shot Deep Image Prior Framework for Denoising and Deconvolution in Fluorescence Microscopy
Xiangyu Qian, Jing Liu, Yunqing Tang et al. (+2 more)
Fluorescence microscopy images are degraded by noise and diffraction-induced blur, which compromise structural fidelity and limit quantitative analysis. Supervised deep learning methods achieve impressive restoration performance but require large-scale paired
datasets that are difficult to obtain in practice. To address this issue, we propose SDIP, a zero-shot deep image prior (DIP) framework that sequentially performs denoising and deconvolution without external training data. An aSeqDIP-based module first suppresses noise while preserving fine structures through sequential autoencoding regularization. In the deconvolution stage, a wavelet-based background correction step is incorporated before the proposed RLG-DIP module performs artifact-reduced deconvolution. RLG-DIP uses the Richardson-Lucy deconvolution result as a physically consistent guidance prior, integrating the imaging model with the implicit prior of DIP to stabilize the ill-posed deconvolution process. Experiments on the BioSR
dataset across multiple cellular structures demonstrate that SDIP improves both signal-to-noise ratio and resolution, achieving superior visual quality and improved quantitative performance on most evaluated structures. The proposed framework may also provide useful insights for designing physically guided DIP methods for other inverse problems.
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All authors Xiangyu Qian, Jing Liu, Yunqing Tang, Luru Dai, Qiushi Li
Physics and Society
Drift Behavior in a Bounded-Confidence Opinion Model with Media Influence
Oliver Zheng and Mason A. Porter
People's opinions can change both from their interactions with each other and from their interactions with media sources. Bounded-confidence models (BCMs) of opinion dynamics provide one framework to study such dynamics. In a BCM, the nodes of a network are
agents with continuous-valued opinions, and these
agents interact with each other via the edges of the network. In this paper, we extend the original Deffuant–Weisbuch (DW) BCM by incorporating influence from two media sources – one with a positive value and one with a negative value – to capture the effects of a polarized media landscape. We show both numerically and analytically that our extended DW model exhibits drifting behavior in which a large cluster of opinions shifts toward one of the media
agents. We analyze how the drift trajectory and speed depend on the model parameters, and we identify conditions in which drift is promoted or suppressed. Our results provide insight into how competing media sources can influence collective opinion formation in social systems.
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Biological Physics · Cross-listed from cond-mat.stat-mech
Optimal parameterization of nonequilibrium generalized master equations from discrete-time experimental data
Chih-Wei Joshua Liu, Jérémie Klinger and Grant M. Rotskoff
Kinetic analyses of experiments often require coarse-grained descriptions, but complex systems rarely conform to the widely used modeling assumptions of Markovianity and thermodynamic
equilibrium.
Memory is indeed a general and often inevitable consequence of coarse-graining. Markov state models (MSMs) are a popular choice of coarse-grained description, but require microstate assignments – which are rarely experimentally tunable – to macrostates that minimize
memory. Generalized master equations (GMEs) circumvent this limitation of MSMs by explicitly capturing
memory. However, GMEs are difficult to parameterize and usually formally approximate in the experimentally relevant discrete-time setting. Here we introduce a maximum-likelihood-based procedure to parameterize formally exact, physically feasible, discrete-time generalized master equations from experiments and simulations in and out of
equilibrium. By adapting
algorithms typically used in optimal transport, we construct physical-constraint-satisfying conditional-maximum-likelihood
estimators of both exact Nakajima-Zwanzig
memory kernels and time-convolutionless GME propagators in discrete time. Applying these
estimators to three examples – experimental recordings of Förster-resonance energy-transfer in an ion channel, experimental nanoparticle tracking of a processive molecular motor, and simulated folding of a
benchmark protein domain – we recover kinetic parameters including relaxation rates, irreversibilities, dwell times, and first-passage times. These results establish discrete-time GMEs as a physically and statistically principled alternative to MSMs for kinetic analyses of experimental and simulated biomolecular systems.
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Fluid Dynamics · Cross-listed from hep-th
Surface Water Wave Scattering and the Hydrotope
Nima Arkani-Hamed, Francesco Calisto, Nail Ussembayev et al. (+2 more)
We study the classical tree-level scattering amplitudes of deep-water surface
gravity waves using the methods of high-energy physics. For scattering in one horizontal dimension and in the two-negative-wavenumber sector we obtain a closed formula for $n$-wave scattering. Up to a kinematic prefactor, the amplitude is the volume of a classic polytope – a box sliced by a hyperplane, which we dub the hydrotope, whose purpose in life is simply to organize the sign patterns of the "chambers" characterizing all the different regions of the two-minus kinematic space. The general formula was discovered by Claude Opus 4.6 working under our guidance, beginning with our earlier discovery of a one-term expression valid in the "simplest" kinematic chamber. Our results resolve the puzzle raised by Y.V. Lvov's 1997 computation of the five-wave amplitudes, unifying and extending it to all multiplicities.
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All authors Nima Arkani-Hamed, Francesco Calisto, Nail Ussembayev, W. Wayne Zhao, Zihan Zhou
Atmospheric and Oceanic Physics
A Two-Step Ensemble Score Filter for Data Assimilation in Partially Observed Systems
Zixiang Xiong, Feng Bao, Hristo G. Chipilski et al. (+3 more)
Data assimilation blends model forecasts with observations to estimate the evolving state of complex dynamical systems, but sparse observing networks remain challenging because unobserved state variables are not directly constrained by observations. In this work, we introduce the Ensemble Score Filter with Linear Regression (EnSF-LR), a two-step filtering method for partially observed nonlinear systems. At each analysis time, EnSF-LR first applies the Ensemble Score Filter (EnSF) to update the observed state components using a nonlinear score-based analysis update. It then computes the resulting observed-state analysis increments and maps these corrections to the unobserved components through the ensemble-based prior covariance
matrix. The latter amounts to the same linear regression mechanism used by Ensemble Kalman Filters (EnKFs). We evaluate EnSF-LR using the Lorenz-63 and 40-dimensional Lorenz-96 systems with sparse linear and nonlinear observations. The method is compared with the original EnSF and with the classical
stochastic EnKF. In the linear-observation experiments, EnSF-LR produces accuracy comparable to the EnKF baseline while substantially reducing error relative to the original EnSF. In the nonlinear-observation experiments, EnSF-LR achieves lower full-state root-mean-square error than both the original EnSF and the EnKF reference. These results suggest that hybridizing score-based and EnKF analysis schemes provides an effective strategy for assimilating sparse and nonlinear observations.
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All authors Zixiang Xiong, Feng Bao, Hristo G. Chipilski, Siming Liang, Jingqiao Tang, Guannan Zhang
Biological Physics
The Allee Effect in Compressible Flows
Jonathan Bauermann, Roberto Benzi, David R. Nelson et al. (+1 more)
Microbes in marine environments are often confined to thin near-surface layers while being advected by turbulent flows. Because such constrained advection generates an effectively compressible flow, reproduction and transport interact in a nontrivial way. Here, we focus on populations whose growth is governed by an Allee effect and show that sinks and sources, generated by the compressible flow, have dramatic consequences for the survival of such species. We derive analytical expressions for the carrying capacity as a function of the Allee strength in the limit of small and large Damköhler number, which measures the product of the large eddy turnover time and the organism growth rate. Numerical simulations reveal how these two limits connect. In the limit of small Damköhler number, we find a maximal Allee strength, set by the statistics of the compressible flow, that leads to species extinction in fully developed
turbulence.
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All authors Jonathan Bauermann, Roberto Benzi, David R. Nelson, Federico Toschi
Chemical Physics · Cross-listed from quant-ph
Efficient targeting of arbitrary excited states with quantum inverse power iteration through filtering polynomials
Srushti Patil and Nina Glaser
In this work, we introduce a
quantum inverse power iteration (QIPI)
algorithm based on the
quantum singular value transformation (QSVT) to target arbitrary excited states. Given an energy shift $\omega$, QIPI prepares the target excited state by iteratively applying an approximation of the shifted inverse
Hamiltonian $(H-\omega I)^{-1}$ to a trial state. Prior
quantum inverse power approaches typically relied on
Fourier decompositions of the inverse
Hamiltonian, with numerical quadrature used to reconstruct the transformation, but such methods are highly sensitive to hyperparameter choices and have been observed to be numerically unstable, effectively restricting their use to ground-state preparation. To enable robust excited-state targeting, we investigate two alternative transformation techniques: a Chebyshev decomposition of the inverse (Cheb-inv) and an eigenstate filtering (EF) approach based on QSVT. We find that EF-based QIPI is substantially more robust than Cheb-inv and other decomposition-based approaches due to the
symmetry of the applied filtering
polynomial, avoiding divergence with respect to the choice of $\omega$ and efficiently suppressing off-target eigenstates even in closely spaced spectra. Numerical simulations for molecular
Hamiltonians of H$_2$, LiH, and BeH$_2$ show improved convergence and enhanced access to higher excited states relative to other
quantum power methods. Assuming standard oracle access to the
Hamiltonian, we further provide logical resource estimates in fault-tolerant settings in terms of T gate counts, and conclude that QIPI can achieve high target state amplification with modest
polynomial degrees, thereby making it a promising candidate for scalable excited-state preparation in fault-tolerant
quantum chemistry applications.
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Atomic Physics · Cross-listed from cond-mat.quant-gas
Coexisting Regular and Chaotic Dynamics in the Dysprosium Feshbach Spectrum
Julie Veschambre, Alexandre Journeaux, Maxime Lecomte et al. (+7 more)
Strongly dipolar gases, such as dysprosium, erbium and thulium, exhibit dense Feshbach spectra whose level statistics have been associated with
quantum chaos arising from couplings among many molecular channels. Here, we combine a precise calibration of the Feshbach spectrum of $^{162}$Dy with spectroscopic measurements of the differential
magnetic moments of bound states associated with more than 80 resonances between 0 and 30 G. These
magnetic moments provide an eigenstate-sensitive probe of the molecular states underlying the resonance spectrum. We find that the level statistics are not uniform: resonances associated with states near the center of the
magnetic-moment distribution display enhanced level repulsion, whereas those near the lower edge remain close to Poisson statistics. Our results reveal hidden structure within the chaotic dysprosium Feshbach spectrum and identify molecular-state composition as a key ingredient in the emergence of
quantum chaos in strongly dipolar scattering.
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All authors Julie Veschambre, Alexandre Journeaux, Maxime Lecomte, Alice Belmon, Ethan Uzan, Inès de Verdelhan, Patricia Christina Marques Castilho, Jakub Zakrzewski, Jean Dalibard, Raphael Lopes
Computational Physics · Cross-listed from math.NA
A fast sum-of-Gaussians algorithm for the high-dimensional fractional Fokker-Planck equation
Shidong Jiang, Dong Wang and Qi Zhou
We present a fast, high-order
algorithm for the free-space fractional Fokker-Planck equation (FFPE) in arbitrary spatial dimension. Its fundamental solution, corresponding to a Dirac-delta initial condition, is obtained from the explicit
Fourier representation by applying a sum-of-Gaussians (SOG) approximation to the nonseparable stretched exponential, using its complete monotonicity as the Laplace transform of a one-sided $\alpha$-stable density. Each Gaussian term is an ordinary heat kernel and therefore factorizes across spatial coordinates. On a tensor-product grid, the separated form can be assembled in $O(MdN)$ work and storage, rather than forming all $O(N^d)$ grid values, where $M$ is the number of Gaussian terms and $N$ is the number of points per dimension. We prove an a~priori error estimate for the pure-fractional fundamental solution and give a parameter-selection procedure for prescribed accuracy over specified ranges of space and time. In numerical experiments the method achieves more than ten digits of relative accuracy, with $M$ growing only logarithmically in the inverse tolerance, and maintains this accuracy in dimensions up to $d=10^{5}$. This exceeds the dimensions reached in comparable radial-quadrature tests, where the integrand becomes increasingly oscillatory as the dimension grows. Because the method represents the fundamental solution as a separated sum of heat kernels, any initial datum given as a finite sum of tensor products can be evolved in closed form using only one-dimensional convolutions. This yields a computable class of high-dimensional solutions that is amenable to error analysis, and tensor neural networks provide one possible way to construct such separated representations for more general data.
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Fluid Dynamics · Cross-listed from cond-mat.soft
Universality of Bubble Coalescence in Electrolytic Media
Afsal Chakkam Palliyalil, Gaurav Tomar and Susmita Dash
Bubble coalescence phenomenon in electrolytic media finds applications in technologies from mineral flotation to electrochemical energy conversion. However, the underlying governing physics still remains unresolved, with longstanding disagreement over the extent to which Marangoni stresses affect the coalescence time by modulating the interfacial mobility. Here, we show that the thin film morphology governs drainage more strongly than the interfacial boundary conditions. We demonstrate experimentally that thin film drainage during bubble coalescence proceeds through three distinct regimes. An initial visco-capillary stage that exhibits a power-law thinning, followed by an exponential decrease in film thickness with time induced by rim stabilisation. The final regime is governed by disjoining pressure and is marked by an exponential relaxation of the film to the
equilibrium thickness. We show that, irrespective of the electrolyte type and concentration, film
evolution exhibits universal behavior by collapsing onto a single curve when rescaled with the characteristic film thickness and time scale, demonstrating that electrolyte effects act only to renormalize timescales rather than alter the underlying dynamics.
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Instrumentation and Detectors · Cross-listed from astro-ph.IM
Radiation tolerance of a diamond radiation detector for space use
Yoshiyuki Ando, Shutaro Ueda, Ryota Heibatake et al. (+13 more)
We present a study of the radiation tolerance of two types of diamond radiation detectors for space use. We plan to launch a 3U-size CubeSat, KSAT3-X, developed by Kanazawa University in 2027. The KSAT3-X mission is aimed to observe inflows and
outflows of charged particles such as electrons and
protons, particularly in the 10 - 40 keV energy range, in the Earth's magnetosphere. As the mission instrument, we have developed two diamond radiation detectors. The first is composed of a microwave
plasma chemical vapor deposition (MPCVD) diamond fabricated by Element Six, and the second is based on a MPCVD diamond produced in-house at Kanazawa University. We irradiate both diamonds with 100 MeV
protons and evaluate their spectroscopic performance as an indicator of radiation tolerance using characteristic X-rays from radioisotope sources. We find no significant degradation in their spectroscopic performance up to at least the 10-year equivalent irradiation under the orbital environments of KSAT3-X. We additionally irradiate the Element Six diamond with 100 MeV
protons up to the 100-year equivalent. As a result, no significant degradation in the spectroscopic performance is observed. These results indicate that the two diamond radiation detectors have sufficiently high radiation tolerance. We also discuss possible physical origins of the observed difference in the spectroscopic performance between the two detectors.
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All authors Yoshiyuki Ando, Shutaro Ueda, Ryota Heibatake, Kaito Ozawa, Makoto Arimoto, Tatsuya Sawano, Daisuke Yonetoku, Kimiyoshi Ichikawa, Norio Tokuda, Taichi Miyazaki, Shoya Matsuda, Yasuhiro Shoji, Satoshi Hatori, Kyo Kume, Shinko Sando, Satoshi Mizushima
Optics · Cross-listed from quant-ph
Vacuum Fluctuation-Induced State Switching in Degenerate Optical Parametric Oscillators
Yihao Huang, Seou Choi, Rom Simovitch et al. (+5 more)
Bistable driven-dissipative systems near bifurcations can exhibit noise-activated switching between steady states. Here, we investigate how
quantum vacuum fluctuations induce such switching in a biased optical parametric oscillator (OPO), a nonlinear system with intrinsic bistability. We show how microscopic
quantum fluctuations driving macroscopic transitions can be controlled with an external bias field that reshapes the OPO steady-state metapotential. We derive analytical expressions for the average switching time and validate them through simulations of the OPO field distribution and inter-state probability flow under bias injection. We further examine how switching depends on bias strength, pump gain, and optical nonlinearity. Our findings clarify how
quantum noise can shape macroscopic dynamics and provide a foundation for noise-assisted photonic machine learning and probabilistic
quantum gates.
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All authors Yihao Huang, Seou Choi, Rom Simovitch, Jamison Sloan, Charles Roques-Carmes, Michael Horodynski, Marin Soljačić, Yannick Salamin
Optics
Direct Observation of X-ray Double-Slit Interference in Momentum Space
Fugui Yang, Xiaoxiao Liang, Tianchong Zhang et al. (+1 more)
Young's double-slit experiment is conventionally deemed a spatial phenomenon emerging from free-space transport. In this Letter, we invert this perspective to demonstrate that Young's interference can be accessed directly as a pure momentum-space observable. Using a perfect-
crystal diffraction to project the field's reciprocal-space profile immediately downstream of the aperture, we resolve the complete hard X-ray double-slit fringe structure without any propagation arm, focusing optics, or imaging detector. This direct capture of the field's invariant momentum marginal establishes a compact, lensless, and propagation-free approach to coherence diagnostics, proving that the fundamental physics of wave interference can be detached from real-space propagation.
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All authors Fugui Yang, Xiaoxiao Liang, Tianchong Zhang, Xiaowei Zhang
Atmospheric and Oceanic Physics
Observations and empirical functions for the ocean surface wave spectrum
Hannah Hata Williams, Michael E. Mueller and Luc Deike
Accurate parameterizations of ocean wave spectra are necessary in a wide array of disciplines including coastal, ocean, and naval engineering as well as in the study of wave interactions and ocean-atmosphere momentum flux. Many such applications use spectrum parameterizations based on temporal data collected well over a half century ago. The development of spatial wave measurement techniques that can accurately capture a larger range of scales allows us to revisit the question of how best to represent an ocean wave spectrum in a variety of ocean wave conditions. We discuss two commonly used wave spectrum parameterizations through a comparison to data collected in field campaigns studying fetch-limited, fully-developed, and mixed sea conditions. We discuss a spectrum parameterization for fully-developed seas that has a $k^{-2.5}$ (or $\omega^{-4}$) dependence on the wavenumber (or angular frequency) in the tail as opposed to the $k^{-3}$ (or $\omega^{-5}$) dependence seen in other frequently-used parameterizations. With knowledge of the peak wavenumber $k_p$ and significant wave height $H_s$, alongside the wind speed, fully-developed conditions can be well-represented. We then compare the impact of using different wave spectrum parameterizations through a Large Eddy Simulation (LES) study of Marine Atmospheric Boundary Layers (MABLs) over the sea surface and find that changing the parameterization used results in variations in the equivalent roughness akin to significant changes in wave conditions.
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Plasma Physics
A Weakly Nonlinear Theory of Zonal-Flow Forcing in Gyrokinetic Turbulence
Georgia Acton, Eduardo Rodrìguez, Gareth Roberg-Clark et al. (+1 more)
The forced generation of zonal flows by microinstability-driven
turbulence is investigated within the framework of local gyrokinetic theory far from marginality. We use a numerically and physically informed three-wave truncation scheme, which allows the prediction of the zonal-flow k_{\psi}-spectrum during the early phase of nonlinear gyrokinetic simulations. The model reproduces the known 2-\gamma growth rate resulting from nonlinear beating of linearly unstable primary modes, in line with previous results, without any marginal stability point. The phase-space structure of such zonal flow is strongly constrained by that of the driving fluctuations, which is essential to understand its behaviour in the region of validity. It is shown that this leads to an enhanced residual spectrum compared to the classic Rosenbluth-Hinton calculation.
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All authors Georgia Acton, Eduardo Rodrìguez, Gareth Roberg-Clark, Alessandro Zocco
Instrumentation and Detectors
A single platform with van der Pauw geometry for measurement of Seebeck coefficient, resistivity, and Hall effect of thin films
Niraj Kumar Singh, Martin Falk, Per-Anton Schön Wallander et al. (+3 more)
A modular thermoelectric properties measurement setup in van der Pauw configuration was developed for a straightforward and simultaneous measurement of electrical resistivity and Seebeck coefficient in an extensive temperature range of 25{\deg}C - 600{\deg}C and can also perform
Hall measurements at room temperature. The setup is optimized for accurate measurement of voltages and temperatures gradients by minimizing possible errors from offset voltages, wire contributions and thermal contact resistances which helps getting reliable data. The setup is user friendly, and the measurements are fully automated and controlled using a LabVIEW program. The detachable modules make this setup quite versatile and provide an all-in-one (except thermal conductivity) solution for thermoelectric measurements.
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All authors Niraj Kumar Singh, Martin Falk, Per-Anton Schön Wallander, Per Sandström, Per Eklund, Arnaud le Febvrier
Optics · Cross-listed from astro-ph.IM
Differentiable design of the PIAA-ZWFS: a flexible wavefront sensor that approaches the fundamental limit
A. K. Taras, S. Y. Haffert and L. Desdoigts
Extreme adaptive optics (AO) is necessary for high contrast astronomy at scales of the habitable zone of nearby systems. We seek to evaluate wavefront sensors that approach fundamental limits of wavefront sensing, enabling adaptive optics systems to run faster or on fainter targets. We present the phase-induced amplitude apodisation Zernike wavefront sensor (PIAA-ZWFS): an adaptation of the conventional Zernike wavefront sensor (ZWFS) that leverages lossless apodisation of the pupil to concentrate the starlight in the focal plane. We optimise and evaluate the sensor with a differentiable modelling framework, drawing on concepts from
Bayesian experimental design to minimise the variance of a maximum likelihood
estimator that uses the system in the high Strehl regime. Our architecture shows state-of-the-art performance in simulation for different apertures, bandwidths,
photon fluxes and source sizes, closing the gap to the fundamental limit by a factor 10 (2.5) compared to the conventional ZWFS (optimised ZWFS) in a typical
photon-limited case. For extended sources, we show that even an ideal point source sensor rapidly becomes sub-optimal, and our system outperforms it for
stellar diameters larger than 0.8{\lambda}/D. We verify that these gains do not come at the cost of dynamic range with either linear or non-linear reconstructors. Finally, we present a proof that there must be a trade-off between the information gained about amplitude and phase errors for any wavefront sensor. The PIAA-ZWFS is a viable wavefront sensor operating near the fundamental sensitivity limits.
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Chemical Physics · Cross-listed from quant-ph
A Reproducible Pipeline for Symmetry-Respecting Excited States on Near-Term Quantum Computers: The H2O/STO-3G Case
Huajing Song
Variational excited-state
quantum algorithms fail for reasons usually studied in isolation: barren plateaus,
symmetry contamination, finite-sampling instability, and hardware cost. Using one small but complete system – H$_2$O in the STO-3G basis (12
qubits, Jordan–Wigner) – we assemble these into a single reproducible pipeline, checking every claim against exact diagonalization. The bare
qubit Hamiltonian interleaves cation ($N{=}7$) states below the neutral
manifold; hardware-efficient and number-conserving ansätze stall at Hartree–Fock, an exact stationary point by Brillouin's
theorem, while ADAPT-VQE escapes; variational deflation inherits the contamination and inverts the spectrum, whereas the
quantum equation-of-motion (qEOM) subspace method restores the ladder to sub-milli-Hartree accuracy. Particle number is protected structurally under shot noise, and a realistic measurement model collapses the thousands of subspace
matrix elements to $\sim\!10^5$ commuting groups; a
matrix-aware shot allocation then reaches chemical accuracy at $\sim\!3\times10^9$ total shots – a thousandfold below the naive per-element estimate and reachable in days – leaving single-
circuit gate fidelity, not measurement, as the binding constraint. This work is a teaching and benchmarking reference, not a new method; all code, parameters, and figures are released.
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Optics
Physics-constrained neural networks for surrogate modeling of lossless periodic structures
Eric Prehn and Peter Jung
We introduce a physics-constrained neural network for the rapid prediction of rigorous coupled-wave analysis outputs in the form of Jones
matrices. Starting from energy conservation in lossless layered periodic structures, we use the fact that the scattering outputs lie on a Stiefel
manifold. This energy constraint is enforced as a hard condition by projecting onto the
manifold using differentiable symmetric orthogonalization. The resulting surrogate enforces energy conservation by construction while preserving differentiability for gradient-based inverse design. The performance and generality of the proposed approach are demonstrated through the inverse design of a diffractive waveguide combiner for augmented reality glasses.
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