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Physics Physics

physics Watch Physics

Wednesday 16 September

Applied Physics

Giant Resonant Reflection Gain from Injection-Induced Quenching in a Tunnel Diode

Darmindra Arumugam, Jack Bush and Brook Feyissa

Negative-resistance microwave oscillators can simultaneously sustain autonomous oscillations and coherently scatter electromagnetic waves, enabling active reflection beyond conventional linear amplification. Here we demonstrate giant resonant reflection gain from synchronization-induced phase localization in a self-sustained tunnel diode oscillator operating near 2.96 GHz. Weak resonant RF injection drives a transition from a broadband free running state to a phase localized narrowband reflected carrier. The resulting reflected enhancement reaches 83.1dB relative to the injected signal and is accompanied by greater than order of magnitude linewidth collapse, nonlinear injection pulling, and a synchronization bandwidth of 15.84 kHz. Time resolved spectrograms directly resolve injection locking and frequency entrainment, while noisy Stuart Landau simulations reproduce the observed spectral concentration and detuning dependent gain roll off near the Hopf instability. Artificially increasing phase diffusion with broadband bias noise suppresses the reflected enhancement, confirming synchronization-induced quenching of phase fluctuations as the origin of the giant coherent reflection gain.
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Chemical Physics

DFT GGA based datasets for H$_2$O potential energy surfaces, permanent moment and polarizability tensors

Anoop Ajaya Kumar Nair and Elvar Örn Jónsson

We present in this article data on the computed potential energy surface, permanent moment surface (from dipole to hexadecapole) and polarizability surface (from dipole-dipole to quadrupole-quadrupole) for the H$_2$O molecule, calculated at the level of the generalized gradient approximation (GGA) in Kohn-Sham density functional theory (KS-DFT) in the grid-based projector augmented wave code GPAW, using the charge perturbation code C-pol. The GGA-based functionals are PBE, RPBE and BLYP. The monomer surfaces are composed of systematic, symmetry-reduced deformations of the H$_2$O monomer and comprise 10,000 configurations per functional, with traceless Cartesian tensor moment values reported as data strings in the xyz file format. In addition, we provide cluster datasets for n-H$_2$O aggregates (n = 2-6), sampled from liquid-like configurations and computed with the same KS-DFT settings, where each extended xyz file encodes the total interaction energy of the cluster in the comment line. Finally, ab initio molecular dynamics (AIMD) trajectories of periodic liquid water boxes, containing between 25 to 150 H$_2$O molecules at ambient conditions, are included, furnishing time series of atomic configurations and energies that are consistent with the monomer and cluster data and suitable for validating classical and machine-learned water models.
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Medical Physics · Cross-listed from eess.SP

Closed-form weak-wave propagation pressure amplitudes for arbitrary power-law attenuation

Esteban Avilés, Michael Oelze, Roberto Lavarello et al. (+1 more)

Biological tissues exhibit power-law frequency dependence of the attenuation coefficient. We derive closed-form weak-wave expressions for fundamental, second-, and third-harmonic pressure amplitudes of monochromatic acoustic plane waves in homogeneous media with arbitrary power-law attenuation to extend classical expressions for quadratic attenuation. For linear frequency dependence of attenuation, one-dimensional simulations using the k-Wave k-space pseudospectral solver yielded 35-42% (classical) and 0-2% (proposed) normalized root-mean-square errors for fundamental-band depletion. This extension supports improved fundamental-band B/A estimation for quantitative ultrasound tissue characterization. A proof-of-concept through-transmission application in phantoms with nearly linear attenuation yielded experimental B/A estimation errors of 23.7-52.7% (classical) and 4.0-5.9% (proposed).
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All authors Esteban Avilés, Michael Oelze, Roberto Lavarello, Andres Coila

Applied Physics · Cross-listed from nlin.PS

The Origin of Imperfection Sensitivity in the Buckling of Cylindrical Shells

Tian Yang and Tobias M. Schneider

Buckling of thin cylindrical shells under axial compression, a classical example of a subcritical instability, is highly sensitive to small imperfections, with minute geometric variations causing large changes in buckling threshold. To uncover the origin of this sensitivity, we use numerical continuation and bifurcation analysis while systematically varying the depth and size of a single localized Gaussian defect. We show that the instabilities of the imperfect shell originate from localized equilibria already present in the perfect shell. By breaking translation symmetry, the defect pins these equilibria and changes how they connect to the imperfect base state. Small changes in defect geometry can thereby switch the bifurcation that triggers buckling, producing non-monotonic and discontinuous changes in buckling threshold and abrupt changes in buckling mode. Imperfection sensitivity is therefore not simply sensitivity to imperfection magnitude, but sensitivity of the underlying bifurcation structure to imperfection geometry.
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Atmospheric and Oceanic Physics

A more predictable Madden-Julian Oscillation index derived from Koopman spectral analysis

Claire Valva and Edwin P. Gerber

The Madden-Julian oscillation (MJO) is a major source of subseasonal-to-seasonal (S2S) predictability. The MJO is commonly defined and tracked with indices such as the Real-time Multivariate MJO (RMM) index. Although the RMM provides a useful description of the MJO, its evolution can be noisy and difficult to predict. We define an MJO index using a data-driven approximation of the Koopman operator. The Koopman index captures similar tropical circulation and convection patterns to the RMM but evolves more smoothly and predictably. Skillful prediction extends to 46 days for the Koopman index compared to 11 days for the RMM under the same prediction framework. While this new approach does not recover the RMM as well as operational S2S models, which provide skillful forecasts up to 35 days, the Koopman index could complement existing MJO diagnostics in evaluating and developing extended-range forecast systems.
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Fluid Dynamics

How universal are warm turbulent cascades?

Ben Y. Israeli and Gregory Falkovich

It is often assumed that general stationary solutions to the wave kinetic equation are described solely by conserved fluxes and temperature. The families of solutions parameterized by these quantities are referred to as warm cascades, and are derivable from differential models of wave kinetics. These models rely on the assumption of local interactions, and we demonstrate by a time scale argument that the nonlocal effect of thermal damping on the cascade may result in dependence on an additional parameter, the UV cutoff, calling into question the validity of warm cascade solutions. This is shown for systems with both three-wave and four-wave interactions. We then consider the depletion of the cascade by this damping, and present a numerical demonstration of this nonlocal and nonuniversal behavior.
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Computational Physics · Cross-listed from nlin.CD

Ladder of information limits on prediction for reduced-order models

Adrian Lozano-Duran

What can and cannot be predicted by a model when only limited information is available? We answer this question by constructing a ladder of information limits that organizes prediction tasks according to their information requirements: from memoryless and memory-augmented trajectory forecasts to event prediction, stationary statistics, and generative laws. The analysis, which is independent of the model structure or architecture, identifies the minimum attainable error along with the information limitations that give rise to it. The approach distinguishes information hidden in unresolved variables from information recovered through time-delayed observations and connects these contributions to Mori–Zwanzig memory. The ladder also reveals why Lyapunov growth alone cannot characterize reduced-order prediction error, why stationary statistics may remain predictable even when individual trajectories become unpredictable, and why stochasticity can represent (but cannot recover) missing information. Numerical studies of the Kuramoto–Sivashinsky equation and the Lorenz system illustrate these results across the ladder. This unified perspective provides a principled language for understanding the fundamental limitations of ROMs for a given prediction task and for clarifying whether improved predictions require richer observations, greater precision in the input, or additional memory.
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Chemical Physics

Agentic AI for Density-Functional Development: Revisiting r2SCAN

Santosh Adhikari, Kelsey A. Parker, Etinosa Osaro et al. (+2 more)

We demonstrate physics-constrained agentic development of a meta-generalized gradient approximation (meta-GGA) functional using a large language model (LLM) to assist the search and optimization of a band-gap-oriented revision of r2SCAN. No real bonded systems were fitted, preserving r2SCAN's nonempirical philosophy. Across the finalist set, band gaps and several molecular subsets improve relative to r2SCAN; the top finalist, r2SCAN+, reduces the band-gap MAE on a benchmark comprising 24 solids from 1.26 to 0.96 eV and the aggregate MAE on 329 molecular properties from 4.79 to 4.42 kcal/mol. We first curated 78 exchange and 90 correlation candidate correction terms from r2SCAN's dimensionless ingredients, spanning polynomial terms through third degree, exponentials, exponentially damped products, and ratios. Allowing each candidate to combine one to three correction terms from the exchange catalog, the correlation catalog, or both yields about 8 x 10^5 distinct forms, making exhaustive high-throughput screening impractical. We defined the search criteria for the LLM agent using r2SCAN's exact constraints, physical norms, and the targeted iso-orbital derivative response. The agent then combined these criteria with its pretrained knowledge and accumulated search feedback to propose and refine sparse forms, prioritizing terms tied to the iso-orbital response; a second LLM critic screened proposals before deterministic verification. Compared with uniform random search, the workflow learned from prior evaluations, incurred far fewer downstream rejections (0.6% versus 24.6%), and located stronger high-response candidates: 51 agentic candidates exceeded the best random-search response of 1.263, with the overall best reaching 1.331. These results show that agentic search can support density-functional development when flexible hypothesis generation is coupled to automated physical verification.
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All authors Santosh Adhikari, Kelsey A. Parker, Etinosa Osaro, Swagata Roy, Dario Rocca

Physics Education

Calculating a journey to Mars with introductory physics

Jorge Pinochet

The problem of a crewed mission to Mars provides a compelling and realistic context for teaching introductory physics. In this work, we present a coherent set of calculations describing a round-trip journey between Earth and Mars, using only mathematical tools accessible at the high-school level. The treatment integrates key topics that are typically taught separately -such as Newtonian gravitation, energy, circular motion, and basic astrodynamics- into a single coherent framework. The analysis includes Hohmann transfer orbits, travel times, launch conditions, and mission constraints, illustrating how a complex real-world problem can be transformed into an effective classroom resource. This work aims to support teachers and students in exploring interplanetary travel as an accessible and motivating application of fundamental physics.
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Physics Education

From Prompts to Physical Laws: A Generative AI Workflow for Engineering Physics Education

Laura B. Alvarado-Cruz, Josep Ll. Suñer, Pedro Yuste et al. (+3 more)

This work explores the use of generative artificial intelligence (AI) as a source of synthetic experimental content for introductory physics courses in engineering education. Using PixVerse.ai, Grok Imagine, and Pippit, three video scenarios were generated to represent distinct resistive force regimes: constant friction, linear drag, and quadratic drag. Kinematic data were extracted from the generated videos using Tracker, an open-source video analysis tool, and subsequently fitted to the corresponding analytical models through non-linear least-squares regression in Microsoft Excel. The results show good agreement between the synthetic data and the classical kinematic equations derived from Newton's Second Law. From the fitted parameters, physically meaningful quantities were recovered in each case, with values broadly consistent with those reported in the literature under the assumed conditions. A recurring observation is that the physical plausibility of the generated motion depends on the level of detail included in the text description used to generate the videos. More specific descriptions tend to produce more coherent dynamical behaviour, suggesting that the formulation of input prompts plays a relevant role in shaping the resulting physical consistency and can be regarded as an integral component of the experimental design process. The integration of generative AI, video-based motion tracking, and curve fitting provides a complete workflow that mirrors key stages of experimental practice, from model construction to quantitative validation. The proposed methodology engages students in experimental design, data acquisition, parameter estimation, and model evaluation, promoting key engineering competencies in modelling of physical models using widely available tools. Overall, the proposed methodology demonstrates the potential of generative AI to support physics education
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All authors Laura B. Alvarado-Cruz, Josep Ll. Suñer, Pedro Yuste, Juan C. Castro-Palacio, Juan A. Monsoriu, Francisco M. Muñoz-Pérez

Chemical Physics

Machine-Learned Dynamical Representations for Accelerated RiteWeight Convergence

Sagar Kania

The increasing use of generative models has made ensembles of short molecular dynamics trajectories increasingly common, creating a growing need for methods that can recover physically meaningful steady-state populations and kinetics from improperly weighted conformational ensembles. Randomized Iterative Trajectory Reweighting (RiteWeight) addresses this problem through repeated random clustering and iterative reweighting, without requiring the fixed Markovian discretization used in conventional Markov state models (MSM). However, the choice of reduced feature space in which RiteWeight performs random clustering has not been systematically investigated. Here, we compare two machine-learned representations, DeepTICA and SPIB-VAE, with linear TICA for recovering steady-state observables from flawed distributions. DeepTICA learns nonlinear coordinates by targeting slow transfer-operator eigenmodes, whereas SPIB-VAE compresses configurations into a low-dimensional latent space while retaining information predictive of future metastable states. DeepTICA provided a comparatively robust RiteWeight representation under limited hyperparameter exploration, whereas SPIB-VAE benefited more strongly from broader optimization. Moreover, a kinetic score computed from a coarse MSM at a resolution comparable to that used for RiteWeight random clustering provided a useful criterion for efficiently selecting reduced representations and their hyperparameters for RiteWeight.
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Applied Physics

Static-to-dynamic field conversion in a temporally switched Lorentz medium

Alazar G. Salfore and Mario J. Mencagli

We investigate static-to-dynamic electromagnetic field conversion in a temporally switched Lorentz medium initially subjected to an electrostatic field. An abrupt change of the oscillator strength drives the material away from its initial equilibrium, while the polarization retains memory of the pre-switch state and acts as the source of the resulting transient. We formulate the resulting initial-value problem in the Laplace domain and derive an analytical representation of the generated field in terms of the poles of the finite Lorentz slab. Material dispersion gives rise to a richer modal structure than in the nondispersive case, including families of slab-mode poles that accumulate toward the singularities associated with the Lorentz polarization dynamics. We further show that material memory governs the earliest stage of the transient, producing a smooth field onset and setting the velocity of the earliest propagating disturbance through the high-frequency permittivity. Because the temporal switch generates broadband spectral content, the transient samples positive-, near-zero-, and negative-permittivity regions of the post-switch material response, leading to qualitatively different spatial field distributions. These results reveal how material dispersion and memory fundamentally shape the generation and evolution of radiation produced from an initially static electromagnetic state.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci

Pressure-dependent melting and crystallization of B2-NiAl from neural-network molecular dynamics

A. S. Onegin, P. R. Levashov and N. M. Chtchelkatchev

We investigate pressure-dependent melting of ordered B2-NiAl using neural-network molecular dynamics with a Deep Potential interatomic model. Melting temperatures are determined from two-phase solid-liquid coexistence simulations over a broad pressure range, yielding the melting curve $T_m(P)$. Relative to available experimental and previous molecular-dynamics results, the present calculations predict a stronger increase of the melting temperature with pressure at elevated compression. To assess the thermodynamic consistency of the calculated melting line, we evaluate the enthalpy and volume changes upon melting and compare the Clapeyron slope with the derivative of the fitted $T_m(P)$ curve. The two estimates are in good agreement over most of the investigated pressure range, supporting the internal consistency of the coexistence results. To probe the character of melting, we perform a layer-resolved composition analysis of the coexistence configurations and find that the coexisting liquid remains essentially equiatomic at all studied pressures, with deviations of the aluminum fraction from the stoichiometric value not exceeding $5\times10^{-3}$. This provides direct atomistic evidence that melting of B2-NiAl remains congruent within the present model. Together, these results establish a thermodynamically consistent pressure-dependent melting description of B2-NiAl and clarify the character of its melting under compression.
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Geophysics

Vertical Attenuation of Thermoremanent Magnetic Anomalies: Implications for Drone-Borne Archaeological and Shallow Geophysical Surveys

Alexandru Hegyi, Kristoffer Aalstad, Arne Anderson Stamnes et al. (+6 more)

Magnetic surveys are widely used in archaeology to reveal buried features by detecting variations in the Earth's magnetic field caused by past human activities. However, as sensor height increases, magnetic anomalies produced by subsurface sources weaken and broaden, posing challenges for both ground-based and aerial (UAV) surveys. This study quantifies that altitude-dependent loss of signal and spatial definition using a fully synthetic model of a uniformly magnetized circular kiln together with upward continuation of measured fluxgate data from the Storbekken 1 iron-production site in central Norway. The synthetic experiment shows that a 2 m diameter, 1 m thick magnetized cylinder buried 0.3 m below ground retains only about 11% of its near-surface (0.2 m) peak amplitude at 2 m sensor height and about 3% at 4 m. In the hybrid experiment, composite furnace anomalies of several hundred nanoteslas collapse to tens of nanoteslas by 2 to 3 m, while distinct lobes merge into broad, low-contrast features. The results show that UAV-borne magnetometry can remain effective for site detection and delineation at elevations where detailed intra-site morphology is already strongly degraded. For small or subtle archaeological targets, sub-metre to low-metre sensor heights provide substantially greater interpretative detail. Recent field evidence on UAV-system noise further indicates that practical detectability will depend not only on source-distance attenuation but also on platform configuration and acquisition strategy. The resulting attenuation curves and morphological observations provide transferable guidance for planning archaeological and shallow-geophysical UAV magnetic surveys.
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All authors Alexandru Hegyi, Kristoffer Aalstad, Arne Anderson Stamnes, Luc Girod, Sebastian Westermann, Diaa Sheishah, Enas Abdelsamei, Michał Pisz, Norbert Pirk

Optics

Time-resolved topology of aperiodically driven systems

Justin T. Cole, Christina Jörg, Terry A. Loring et al. (+1 more)

Topological phases of periodically driven systems are commonly classified through Floquet Hamiltonians, whose definition relies on a repeated driving cycle. However, this framework breaks down for systems whose realized evolution is aperiodic or nonlinear, even when robust topological phenomena appear to persist. Here, we introduce an evolution-history spectral localizer framework that classifies Chern topology directly from a system's finite-time unitary evolution operator. The method produces a time-resolved local Chern marker and an associated local gap, allowing topology and its protection to be tracked during the actual dynamics of a driven system. We demonstrate this approach in quasiperiodically driven and nonlinear photonic models, where it identifies transient topological protection, its subsequent loss, and the recovery of topology after nonlinear excitations disperse. The developed framework provides a route for analyzing driven photonic systems whose topological behavior depends on finite-time dynamics rather than an idealized Floquet cycle.
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All authors Justin T. Cole, Christina Jörg, Terry A. Loring, Alexander Cerjan

Applied Physics

Si- and C-Induced Modifications in Vapor-Solid-Grown InGaAs Nanowires: From Crystal Structure to Carrier Dynamics

Hamidreza Esmaielpour, Leopold Rothmayer, Thomas Trinkl et al. (+5 more)

Determining the impact of dopants in ternary III-V nanowires (NWs) is crucial for their applications in next-generation optoelectronic and photonic devices; however, the interplay between dopant species, crystal structure, and carrier dynamics in vapor-solid-grown InGaAs NWs remains underexplored. In this study, the influence of silicon (Si) and carbon (C) doping on the structural, morphological, and optical properties of catalyst-free, selective-area grown InGaAs NWs fabricated by molecular beam epitaxy is investigated. Scanning electron microscopy demonstrates that C-doping increases the NW aspect ratio by over 100% compared to undoped NWs, while a concomitant decrease in their planar defect density is observed, indicating enhanced crystal quality with C-doping. Despite their superior crystal quality, the C-doped NWs present a diminished photoluminescence intensity, due to point defects and increased surface recombination velocity. This is further supported by time-resolved photoluminescence spectroscopy, revealing accelerated non-radiative recombination in the C-doped NWs. Lastly, hot-carrier effects in these nanostructures are studied, and their signatures remain observable, albeit with reduced intensity in both Si- and C-doped NWs. This comprehensive study establishes clear correlations between doping parameters and the resulting material characteristics and carrier dynamics, offering valuable insights into the fundamental mechanisms governing the VS-grown InGaAs NWs and their implications in optoelectronic devices.
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All authors Hamidreza Esmaielpour, Leopold Rothmayer, Thomas Trinkl, Laura Niermann, Tore Niermann, Michael Lehmann, Jonathan J. Finley, Gregor Koblmüller

Fluid Dynamics

Fredholm Backstepping with Heymann Pre-Feedback for Linearized Navier–Stokes 2D Channel

Miroslav Krstic

We stabilize the linearized 2-D Poiseuille channel flow at arbitrary Reynolds numbers, and assign the decay of every wavenumber to any user-prescribed rate. The actuation is as in Vazquez–Krstic (IEEE TAC, 2007), VK'07 below: the two velocity components at only one wall. This is the first Fredholm backstepping design for a Navier–Stokes system. In VK'07 the wall-normal input renders the plant spatially causal, after which the tangential input closes a Volterra backstepping loop. Here the normal input carries a finite-rank feedback of arbitrarily small gain, whose sole role, in the sense of Heymann's lemma (TAC, 1968), is to give the plant simple spectrum and make every mode controllable from the tangential velocity. The tangential input then applies a stabilizing feedback, obtained through an invertible Fredholm transform to a Stokes target PDE, shifted left by an arbitrary decay. No simplicity of eigenvalues of the Orr–Sommerfeld operator is assumed and the exclusions of plant coefficient values that have accompanied Fredholm backstepping are removed. We state the comparison with the Volterra design without embellishment: the Fredholm architecture is powerful, but its gain is spectral where VK'07 is closed-form, the simpler of the two, and one from which the Fredholm approach takes its model, its actuation, and its target. What is demonstrated here is the reach of the Fredholm method on its most demanding plant to date. The feedback is tested at Reynolds number $30000$, with the dominant part of the closed-loop spectrum assigned so that it amounts to slowing the flow about twentyfold.
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Physics Education · Cross-listed from astro-ph.IM

Gravitas: A Browser-Based Astrophysics Laboratory for Prediction, Measurement, and Discovery

Carl Ziegler

Astronomy students are often asked to reason about systems that cannot be handled in a laboratory, evolve over inaccessible timescales, or reveal themselves only through indirect measurements. Gravitas is a free, browser-based astrophysics environment designed to turn those limitations into opportunities for experimentation. Its version 1.0.0 release combines a configurable gravitational sandbox with 59 ready-to-run scenarios and 22 guided investigations comprising 636 steps and roughly 15-19 hours of structured work. Students can build and alter systems, change reference frames, measure distances, angles, periods, energies, forces, and orbital elements, and observe simulated systems through transit, radial-velocity, astrometric, rotation-curve, stellar-evolution, and gravitational-wave displays. Guided activities use a predict-test-measure-revise-explain cycle, retain the student's evidence, and export a report for submission. Instructor guides, answer keys, short classroom routes, lecture and embed modes, reproducible share links, Spanish localization, keyboard-accessible navigation, and offline operation lower the practical barrier to adopting the simulation in introductory astronomy and physics courses. A public dashboard reports 243 scientific checks across 25 areas and names the sources and tolerances behind them. This article describes the learning experiences available in Gravitas, illustrates several ways it can be used from a brief demonstration to a full laboratory exercise, and states the scientific scope and limitations of the models. The aim is not to replace quantitative calculation or observation, but to give students a place where they can make a prediction, change one thing, measure what follows, and build an explanation from their own evidence.
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Applied Physics · Cross-listed from cond-mat.supr-con

Encapsulation epitaxy of air-stable monolayer superconducting films for quantum circuits and qubits

Xudong Zheng, Sameia Zaman, Kenan Zhang et al. (+22 more)

Two-dimensional (2D) superconductors are an emerging platform for strongly correlated physics and quantum information science. Their reduced dimensionality, atomically flat interfaces, and high crystallinity are attractive for realizing compact lumped-element devices in superconducting circuits. However, synthesizing large-area, monolayer 2D superconductors remains challenging because of their susceptibility to oxidation. Here, we report an "encapsulation epitaxy" mechanism that enables the growth of large-area, air-stable, monolayer superconducting NbSe2 films and explore their use in superconducting quantum circuits. A 2D encapsulation layer, such as graphene or hexagonal boron nitride (hBN), pre-deposited on a 3D substrate (e.g., SiO2 or Si3N4), serves both as a template for epitaxial growth of monolayer NbSe2 (1L-NbSe2) underneath it and as a protective cover. This approach produces uniform, large-area (>1-inch) 1L-NbSe2 with greatly enhanced ambient stability, enabling device fabrication in air. The resulting 1L-graphene/NbSe2 heterostructures exhibit robust superconductivity (Tc ~ 1 K) and enhanced charge density wave order (TCDW ~ 177 K), indicative of high material quality. We further integrate 1L-NbSe2 into superconducting circuits using oxidation-free transfer and superconducting edge-contact techniques. The 1L-NbSe2 exhibits a measured kinetic inductance LK ~ 0.7 nH/square, making it suitable for quantum circuits requiring high-kinetic-inductance elements. Encapsulation epitaxy thus provides a route to air-stable 2D superconductors and van der Waals heterostructures, with potential for wafer-scale, monolithic fabrication of superconducting quantum circuitry.
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All authors Xudong Zheng, Sameia Zaman, Kenan Zhang, Connor A Occhialini, Haowei Xu, Zhien Wang, Fangyuan Liu, Luiz Gustavo Pimenta Martins, Sejoon Lim, Tianyi Zhang, Tilo H. Yang, Jiangtao Wang, Yunyue Zhu, Zachariah Hennighausen, Sein Park, Steven Vitale, Kevin Tibbetts, Stephen Margiotta, Phillip Kim, Cong Su, Ju Li, Riccardo Comin, William D. Oliver, Joel Î-j. Wang, Jing Kong

Fluid Dynamics · Cross-listed from eess.SY

Optimal Control and Closed-Loop Stability of Droplet Transport in a Microchannel

Rajneesh Anand and Mayuresh V. Kothare

Understanding the efficient transport of fluid droplets in confined geometries has been a domain of interest for industrial applications in recent times. In the present work, we focus on designing control strategies that optimally steer droplet motion. Here, we apply optimal control framework to the droplet transport problem in a microchannel based on lubrication theory that minimizes viscous dissipation. Two complementary modeling routes are adopted: a reduced-order ordinary differential equation (ODE) model optimized via Pontryagin's Maximum Principle, and a full nonlinear partial differential equation (PDE) model optimized using a Covariance Matrix Adaptation-Evolutionary Strategy. By parameterizing target displacement, droplet size, and capillary number, we uncover two distinct optimal transport regimes: a "translate-relax" strategy for short distances and a "compact-translate-relax" strategy for longer targets. In continuum mechanics, the competition between surface forces and cumulative viscous dissipation decides the optimal transport strategies. We further show that the displacement range over which the reduced-order controller transfers to the continuum model is governed by capillary stiffness. We finally address closed-loop stability by adapting a control Lyapunov function (CLF) framework to the reduced-order dynamics. We demonstrate that the terminal cost which penalizes deviation from the target, serves as a cost-compatible CLF on the physical domain, and prove that the CLF-compatible feedback exponentially stabilizes the target state.
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Instrumentation and Detectors · Cross-listed from quant-ph

Observing and evading quantum back-action on a kilogram-scale oscillator

Begüm Kabagöz, Eric Oelker, Dhruva Ganapathy et al. (+203 more)

Continuous quantum displacement measurements are fundamentally limited by a trade-off between readout imprecision and measurement back-action, constrained by the Heisenberg uncertainty principle. In the Laser Interferometric Gravitational-Wave Observatory (LIGO), these two quantum noise components dominate much of the observation band, making it an excellent testbed. We induce a sub-Hz-linewidth optomechanical mode by trapping the differential motion of the 40-kg mirrors in a band where radiation-pressure back-action dominates the motion. Engineering the quantum state entering the dark port creates correlations between imprecision and back-action that partially cancel their contributions, reducing observed motion near resonance by ~47%. A framework resolving the imprecision, back-action, and correlation terms identifies the origin of this suppression. These results demonstrate quantum back-action evasion and quantum reservoir engineering in a macroscopic optomechanical system.
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All authors Begüm Kabagöz, Eric Oelker, Dhruva Ganapathy, Nergis Mavalvala, Vivishek Sudhir, Vladimir Bossilkov, Joseph Betzweiser, Valery V. Frolov, Anamaria Effler, Adam Mullavey, Lisa Barsotti, Evan D. Hall, Peter Fritschel, R. Abbott, I. Abouelfettouh, R. X. Adhikari, A. Ananyeva, S. Appert, S. K. Apple, K. Arai, N. Aritomi, S. M. Aston, M. Ball, S. W. Ballmer, D. Barker, B. K. Berger, D. Bhattacharjee, G. Billingsley, S. Biscans, C. D. Blair, N. Bode, E. Bonilla, A. Branch, A. F. Brooks, D. D. Brown, J. Bryant, C. Cahillane, A. Calafat, S. R. Callos, H. Cao, E. Capote, F. Clara, J. Collins, C. M. Compton, G. Connolly, R. Cottingham, D. C. Coyne, R. Crouch, J. Csizmazia, A. Cumming, L. P. Dartez, D. Davis, N. Demos, E. Dohmen, K. L. Dooley, J. C. Driggers, S. E. Dwyer, A. Ejlli, T. Etzel, M. Evans, J. Feicht, R. Frey, W. Frischhertz, M. Fuentes-Garcia, P. Fulda, M. Fyffe, B. Gateley, T. Gayer, J. A. Giaime, K. D. Giardina, J. Glanzer, E. Goetz, R. Goetz, A. W. Goodwin-Jones, S. Gras, C. Gray, D. Griffith, H. Grote, T. Guidry, J. Gurs, J. Hanks, J. Hanson, M. C. Heintze, A. F. Helmling-Cornell, N. A. Holland, D. Hoyland, H. Y. Huang, Y. Inoue, A. L. James, A. Jamies, R. Jaume, A. Jennings, W. Jia, D. H. Jones, S. Karat, S. Karki, M. Kasprzack, K. Kawabe, N. Kijbunchoo, P. J. King, J. S. Kissel, K. Komori, A. Kontos, R. Kumar, K. Kuns, M. Landry, B. Lantz, M. Laxen, K. Lee, M. Lesovsky, F. Llamas Villarreal, M. Lormand, H. A. Loughlin, R. Macas, M. MacInnis, C. N. Makarem, B. Mannix, G. L. Mansell, R. M. Martin, K. Mason, F. Matichard, N. Maxwell, G. McCarrol, R. McCarthy, D. E. McClelland, S. McCormick, T. McRae, F. Mera, E. L. Merilh, J. R. Mérou, F. Meylahn, R. Mittleman, D. Moraru, G. Moreno, M. Nakano, T. J. N. Nelson, A. Neunzert, J. Notte, J. Oberling, T. O'Hanlon, R. Oram, C. Osthelder, D. J. Ottaway, H. Overmier, W. Parker, O. Patane, A. Pele, H. Pham, M. Pirello, J. Pullin, V. Quetschke, K. E. Ramirez, K. Ransom, J. Reyes, J. W. Richardson, M. Robinson, J. G. Rollins, C. L. Romel, J. H. Romie, M. P. Ross, B. I. Rotimi, K. Ryan, T. Sadecki, A. Sanchez, E. J. Sanchez, L. E. Sanchez, R. L. Savage, D. Schaetzl, M. G. Schiworski, R. Schnabel, R. M. S. Schofield, E. Schwartz, D. Sellers, T. Shaffer, R. W. Short, D. Sigg, B. J. J. Slagmolen, C. Soike, S. Soni, V. Srivastava, L. Sun, D. B. Tanner, M. Thomas, P. Thomas, K. A. Thorne, M. R. Todd, C. I. Torrie, G. Traylor, A. S. Ubhi, G. Vajente, J. Vanosky, A. Vecchio, P. J. Veitch, A. M. Vibhute, E. R. G. von Reis, J. Warner, B. Weaver, R. Weiss, C. Whittle, B. Willke, C. C. Wipf, J. L. Wright, V. A. Xu, H. Yamamoto, L. Zhang, M. E. Zucker

Fluid Dynamics

The Level Set Ensemble Kalman Filter: Sequential Data Assimilation for Flows With Shocks

Michael K. Sleeman, Lorenzo Beronilla, Hangchuan Hu et al. (+4 more)

The ensemble Kalman filter is commonly used to perform sequential data assimilation in science and engineering because of its robust performance and scalability when the state space dimension is high. However, the standard EnKF generates spurious oscillations when applied to compressible flows with shocks; the cause is the uncertainty, across the ensemble, in the shock location. This paper develops the level set EnKF, which overcomes this challenge by employing a nonlinear mapping from the original state space into a latent space in which the analysis step is undertaken. The latent representation comprises a level set function that encodes the discontinuity location and smooth state extensions, defined over the full physical domain, that represent the solution on either side of the discontinuity. We first formulate the level set EnKF as a specific instance of a more general approach of performing data assimilation in a latent space. We then demonstrate the feasibility of the level set EnKF for several one-dimensional compressible flows and a two-dimensional blast wave.
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All authors Michael K. Sleeman, Lorenzo Beronilla, Hangchuan Hu, Xu-Hui Zhou, Matthias Morzfeld, Andrew M. Stuart, Tamer A. Zaki

Physics and Society · Cross-listed from cs.SI

Experienced partisan segregation across patterns of mobility behavior in US cities

Marco Tonin, Michele Tizzoni, Bruno Lepri et al. (+1 more)

Residential and social sorting along political lines structure Americans' daily interactions, often limiting encounters with opposing viewpoints. Here, we combine high-resolution anonymized mobility data from 11 major U.S. metropolitan areas with precinct-level voting results to quantify and examine individuals' experienced partisan segregation in urban areas. We find that residential context provides a strong baseline for political exposure, but experienced partisan segregation is also shaped by everyday mobility behavior, since individuals living in similar residential environments can experience different levels of partisan segregation depending on their daily routines. In particular, individuals who travel farther encounter more politically diverse environments, especially in highly segregated areas, indicating that mobility range moderates the relationship between residential and experienced segregation, while exploring a wider range of places does not reduce partisan segregation and may even reinforce it. Moreover, place segregation largely reflects political geography, yet categories of points of interest have distinct segregation patterns, with community places and social businesses tending to attract more left-leaning visitors, reflecting visitation patterns rather than local accessibility alone. Our findings show that experienced partisan segregation extends beyond residential geography and is also associated with the mobility behavior and visitation patterns that structure people's daily lives.
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All authors Marco Tonin, Michele Tizzoni, Bruno Lepri, Esteban Moro

Computational Physics · Cross-listed from quant-ph

A Square-Root Barrier to Quantum Gate Speed under Linear Coupling

Pablo Restrepo Gaviria and Mischa P. Woods

Faster quantum gates can suppress the decoherence accumulated during a computation, but in superconducting processors stronger microwave pulses can also increase leakage, off-resonant excitation, stray-field errors, and crosstalk. This creates a central energy–speed–error tradeoff: can quantum state engineering make a gate parametrically faster without paying proportionally more drive energy? We address this question by treating the driving pulse as a quantum bosonic field rather than a classical waveform. For a finite-dimensional system coupled linearly to that field, we prove that fixed-fidelity gate transition rates grow at most as the square root of the pulse energy, under stated uniformity conditions on the coupling and accepted dynamics. The bound permits arbitrary pulse states, including squeezed and non-Gaussian states, as well as drive–system entanglement and back action; coherent Gaussian pulses attain its energy exponent. Thus squeezing or other state engineering alone cannot replace the square-root energy law of a conventional linear drive by the linear scaling allowed by general quantum speed limits. Achieving that improvement requires changing the interaction class, in addition to using a suitable nonclassical pulse, thereby identifying interaction nonlinearity as an essential resource for relaxing the practical gate-speed error tradeoff.
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Applied Physics · Cross-listed from cs.LG

Radio-Frequency Convolutional Neural Networks

Zhihui Gao, Shi-Yuan Ma, Yiran Chen et al. (+2 more)

Running artificial intelligence (AI) models directly on edge devices such as smartphones, wearables, and drones offers low latency, pervasive scalability, and data privacy, but these devices rarely carry the computing capability that modern neural networks demand. Edge accelerators have been developed in response, yet each adds computing hardware to devices already constrained in size, weight, power, and cost (SWaP-C). An alternative lies in what these devices already carry: the frequency mixer in every wireless radio multiplies signals in time, natively performing convolution in the frequency domain. Here we introduce radio-frequency convolutional neural networks (RF-CNNs), which repurpose existing communication hardware for CNN inference. Multi-channel convolutions are mapped onto frequency tones for a passive mixer to execute in a single pass. We experimentally demonstrate that RF-CNN runs deep CNNs up to 26.4 million parameters and nine layers from classification of wireless signals and images to controllable image generation, close to full-precision performance. Because the weights arrive over the air and the analog hardware is shared with communication, the edge device spends energy only on data preparation and readout-down to 0.72 femtojoules per multiply-accumulate, two orders of magnitude less than it would cost on an added digital processor. These results suggest that deployed wireless infrastructure can bring efficient, state-of-the-art AI inference to the billions of devices it already connects.
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All authors Zhihui Gao, Shi-Yuan Ma, Yiran Chen, Dirk Englund, Tingjun Chen

Data Analysis and Statistics · Cross-listed from stat.ME

Binning-Independent Bayesian Analysis of Time-Dependent Perturbed Angular Distribution data

Franziskus von Spee

We propose a new approach for analyzing Time-Dependent Perturbed Angular Distribution data. In this, a likelihood is constructed with the help of conditional probabilities of single events and binning of time data is avoided. This likelihood is used in a Bayesian framework to calculate the posterior probability density function for the $g$ factor of interest. This approach is compared to more traditional approaches that use binned data by analyzing simulated datasets. In many cases the resulting posterior probability densities are observed to be multimodal and the results can thus often not be summarized with a single Gaussian approximation. We find that results from the new approach are more reliable for low-statistics datasets.
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Geophysics

Period-Inverted Blast-Radius Normalization for a Non-Ablating Hypersonic Reentry Source Using OSIRIS-REx Capsule Infrasound

Elizabeth A. Silber

Cylindrical blast wave theory relates infrasound from hypersonic sources to energy deposition through a characteristic blast radius R_0, yet the normalization constant k in the scaling R_0 = k (E_0/p_0)^(1/2) differs by up to 40% across the formulations examined here. This uncertainty affects period-based source characterization for elevated atmospheric sources relevant to infrasound monitoring. Using infrasound from the OSIRIS-REx Sample Return Capsule reentry on 24 September 2023, observed periods at 39 stations spanning source altitudes of 44-62 km are inverted through a weak-shock propagation model to obtain period-inverted blast radii R_0,obs without prescribing a blast-wave normalization. Combining R_0,obs with aerodynamic drag energy yields a median k = 0.396, with a modified-bootstrap 95% interval of 0.378-0.474 when the reported period-measurement uncertainties are included. This result is consistent with the Sakurai (1965) and Jones et al. (1968)/Plooster (1970) normalizations, while Few (1969) lies outside the reference confidence interval. However, station-level k values exhibit significant along-trajectory structure (Spearman r_s = -0.84, p < 0.001), indicating that k_med is an ensemble-effective normalization rather than a station-invariant constant. The Mach-diameter approximation R_0 ~ M d_m overestimates R_0 by a factor of ~3.4 for this effectively non-ablating source. A period-based relation, R_0 = 23.99 tau - 0.73 (R^2 = 0.991, MAPE = 2.3%), provides a rapid blast-radius estimator whose residuals show no detectable altitude dependence. These results provide inverse calibration constraints and practical period-based source-characterization relations for OSIRIS-REx-like non-ablating hypersonic entries, while highlighting the need for multi-event validation before broader application.
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Fluid Dynamics

IB-Flows: an open-source multi-GPU immersed boundary code for fluid-structure interaction

Giovanni Vagnoli, Martino Andrea Scarpolini, Fabio Guglietta et al. (+4 more)

We present IB-Flows, an open-source, multi-GPU solver for the direct numerical simulation of incompressible fluid-structure interaction (FSI) problems. The code couples a second-order finite-difference fractional-step Navier-Stokes solver on a staggered Cartesian grid with two immersed boundary methods: a Lagrangian method based on moving least-squares (MLS) interpolation and an Eulerian sharp-interface method. Rigid bodies are advanced with a quaternion-based Newton-Euler solver, while deformable surfaces embedded in three-dimensional flows are described by a structural solver based on the interaction potentials; a predictor-corrector scheme provides either loose or strong fluid-structure coupling. Non-Newtonian fluids with shear-thinning or shear-thickening rheology are handled through a strain-rate-dependent viscosity, and the same framework accommodates subgrid-scale eddy-viscosity models for large-eddy simulations of turbulent flows. The solver is written in CUDA Fortran with MPI domain decomposition, so that the fluid, interpolation and structural kernels are all executed on the GPUs, while the distributed transposes required by the Poisson and implicit solvers are further accelerated by the cuDecomp library through GPU-aware communication. Strong- and weak-scaling tests show near-ideal intra-node scaling and good multi-node efficiency. The solver is validated against a set of benchmark problems spanning wall-bounded turbulence, rigid-body and deformable-body fluid-structure interaction, and a biomedical application. IB-Flows is intended as a reproducible reference implementation for immersed boundary FSI simulations on modern GPU clusters and as a transparent platform to be used for tackling multiphysics problems.
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All authors Giovanni Vagnoli, Martino Andrea Scarpolini, Fabio Guglietta, Joshua Romero, Massimiliano Fatica, Roberto Verzicco, Francesco Viola

Physics and Society · Cross-listed from cs.AI

Flag Game: A Toy Model for Mechanistic Swarm Interpretability

Elizabeth Pavlova and Hidenori Tanaka

Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formation and spread of beliefs about the world, and mechanistic understanding is crucial for collective alignment. To this end, we introduce the Flag Game, a toy model for studying the mechanisms of collective belief formation. Concretely, a hidden country flag defines the ground truth, and each bounded agent directly observes only a private crop but can exchange beliefs and weigh social evidence from peers. Despite its simplicity, the Flag Game reproduces rich collective phenomenology: non-monotonic scaling of performance with population size, accuracy gains from social-awareness prompting and team diversity, and strong effects of organizational structure. In particular, we identify that collective belief collapse at small population sizes turns into collective belief polarization as the population grows. This polarization causes the performance decline at large population sizes, but creates diversity in collective beliefs. Finally, we dissect the mechanisms underlying collective belief collapse and polarization with two complementary approaches. We first introduce social circuit attribution, a technique to predict which agent, and what view, matters most to collective dynamics, and verify its predictions by causal interventions on agents, tracing how agent patching changes collective outcomes. However, the efficacy of causal interventions on agents decreases as the population grows. We therefore develop a statistical mechanical theory for larger populations and verify that it matches the empirical phase diagram. Together, these results take a first step toward mechanistic swarm interpretability, a science of how the properties of individual agents and their communication give rise to emergent collective behavior.
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Chemical Physics

Floquet-Plasmon Enhanced Charge Transfer at Catalytic Interfaces

Annabelle Canestraight, Phillip Christopher and Vojtech Vlcek

Plasmonic excitation of metallic nanostructures can generate energetic carriers capable of transferring charge into nearby adsorbed molecules, providing a possible pathway for driving chemical transformations. In many theoretical descriptions of this process, charge transfer is determined by the equilibrium electronic structure of the molecule and substrate together with the time-dependent carrier distribution produced during plasmon decay. However, the intense transient electric fields associated with localized surface plasmons can also dynamically perturb the molecular electronic structure itself on ultrafast timescales. In this work, we investigate how these time-dependent fields modify the molecular spectral function and influence charge injection at catalytic interfaces. Using a model of CO2 adsorbed on Au(111), we compute the real-time molecular Green's function within a correlated frontier-orbital active space under plasmon-like driving. We find that the driving field rapidly produces transient Floquet-type replica bands in the molecular density of states, opening additional resonant pathways for hot-carrier injection that are absent without external driving or in time-local descriptions. Coupling the evolving molecular spectrum to a time-dependent hot-electron distribution described within a two-temperature Sommerfeld framework, we predict large enhancements in quasiparticle spectral overlap that governs injection during plasmon dephasing. These results suggest that dynamically generated non-equilibrium spectral structure may play an important role in plasmon-assisted catalysis and provide a framework for studying driven catalytic interfaces beyond static electronic structure descriptions.
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